WO2024261996A1 - Information generation device, information provision system, information generation method, and recording medium - Google Patents

Abstract

The present invention provides an information generation device in order to generate a risk map in which the location of a person at risk of a target disease is made visible, said information generation device comprising an acquisition unit that acquires sensor data measured by a measurement device mounted on footwear of at least one subject, a risk estimation unit that estimates the disease risk for each disease relating to at least one subject using the acquired sensor data, a map generation unit that generates a risk map in which a display corresponding to the disease risk for a target disease relating to at least one subject is overlaid on a map of a target district, and an output unit that outputs risk information including the generated risk map.

Description

Information generating device, information providing system, information generating method, and recording medium

This disclosure relates to an information generating device, an information providing system, an information generating method, and a recording medium.

In areas far from cities, the increase in the elderly population is increasing the public burden on local governments. In such areas, transportation is often inconvenient. Improving the convenience of activities related to the lives of the elderly, such as shopping, going to the hospital, nursing care, and home visits, is determined by urban development policies. If urban development can be achieved that improves the convenience of these activities, we can expect to see improvements in the health of local residents and the attractiveness of the area. However, in reality, it is difficult to implement such policies because it is not possible to fully grasp the lifestyles and health conditions of local residents.

Patent Document 1 discloses a health information management server that provides analysis results of health information related to health for each area smaller than the entire analysis target area. The server of Patent Document 1 acquires health information related to the health of residents from resident terminals. The server of Patent Document 1 stores the acquired health information in a memory unit in association with the resident to which the health information relates. The server of Patent Document 1 analyzes the health information corresponding to each resident and calculates the resident's personal health risk. The server of Patent Document 1 calculates area health risk for each of a number of pre-set areas based on the calculated personal health risk and the resident's residential location. Patent Document 1 discloses that the level of area health risk calculated for each area is overlaid on the corresponding area on a map and displayed on a management terminal.

JP 2019-185408 A

The method of Patent Document 1 allows the degree of area health risk calculated for each area to be overlaid on a map. Therefore, according to the method of Patent Document 1, the degree of area health risk is overlaid on a map, making it easier to understand the health status of local residents. However, while the method of Patent Document 1 can verify the indicator of individual health risk, it cannot visualize the locations of people at high risk of contracting a specific disease.

The objective of this disclosure is to provide an information generation device, an information provision system, an information generation method, and a recording medium that can generate a risk map that visualizes the locations of people at risk of contracting a target disease.

An information generating device according to one embodiment of the present disclosure includes an acquisition unit that acquires sensor data measured by a measuring device mounted on the footwear of at least one subject, a risk estimation unit that uses the acquired sensor data to estimate a disease risk for each disease for at least one subject, a map generation unit that generates a risk map in which an indication according to the disease risk of a target disease for at least one subject is superimposed on a map of a target area, and an output unit that outputs risk information including the generated risk map.

In one embodiment of the information generating device disclosed herein, sensor data measured by a measuring device mounted on the footwear of at least one subject is acquired, the acquired sensor data is used to estimate a disease risk for each disease for the at least one subject, a risk map is generated in which an indication corresponding to the disease risk of the target disease for the at least one subject is superimposed on a map of the target area, and risk information including the generated risk map is output.

A program according to one embodiment of the present disclosure causes a computer to execute the following processes: acquiring sensor data measured by a measuring device mounted on the footwear of at least one subject; estimating a disease risk for each disease for at least one subject using the acquired sensor data; generating a risk map in which an indication according to the disease risk of a target disease for at least one subject is superimposed on a map of a target area; and outputting risk information including the generated risk map.

The present disclosure makes it possible to provide an information generation device, an information provision system, an information generation method, and a recording medium that can generate a risk map that visualizes the locations of people at risk of contracting a target disease.

1 is a block diagram showing an example of a configuration of an information providing system according to the present disclosure. 1 is a block diagram showing an example of a configuration of a measurement device included in an information providing system according to the present disclosure. 1 is a conceptual diagram showing an example of the arrangement of measuring devices provided in an information providing system according to the present disclosure. 1 is a conceptual diagram for explaining a coordinate system set in a measurement device included in an information provision system in the present disclosure. FIG. FIG. 2 is a conceptual diagram for explaining a human body surface used in the description of the present disclosure. 2 is a block diagram showing an example of a configuration of an information generating device included in the information providing system in the present disclosure. FIG. FIG. 1 is a conceptual diagram for explaining a walking cycle used in the explanation of the present disclosure. 1 is a conceptual diagram for explaining a physical ability estimation model used by an information generating device included in an information providing system in the present disclosure. FIG. 1 is a conceptual diagram for explaining an example of estimating disease risk by an information providing system in the present disclosure. 1 is a conceptual diagram for explaining an example of estimating disease risk by an information providing system in the present disclosure. 1 is a conceptual diagram showing an example of a map of a target area used to generate a risk map by an information generating device provided in an information providing system in the present disclosure. FIG. 1 is a conceptual diagram showing an example of a risk map generated by an information generating device included in an information providing system in the present disclosure. 1 is a conceptual diagram showing an example of a risk map generated by an information generating device included in an information providing system in the present disclosure. 1 is a conceptual diagram showing an example of a risk map generated by an information generating device included in an information providing system in the present disclosure. 1 is a conceptual diagram showing an example of a risk map generated by an information generating device included in an information providing system in the present disclosure. 1 is a conceptual diagram showing an example of a risk map generated by an information generating device included in an information providing system in the present disclosure. 11 is a flowchart for explaining an example of an operation of an information generating device included in the information providing system in the present disclosure. 11 is a flowchart for explaining an example of a walking index calculation process performed by an information generating device included in the information providing system in the present disclosure. 11 is a flowchart for explaining an example of a risk map generation process performed by an information generating device included in the information providing system in the present disclosure. FIG. 1 is a conceptual diagram for explaining a service that uses an information providing system in the present disclosure. 1 is a conceptual diagram showing an example of a display of a risk map provided by an information providing system in the present disclosure. 1 is a block diagram showing an example of a configuration of an information providing system according to the present disclosure. 1 is a conceptual diagram illustrating an example of a configuration of an information generating device included in an information providing system according to the present disclosure. 1 is a conceptual diagram for explaining an example of estimation of a measure by an information providing system in the present disclosure. FIG. 11 is a flowchart for explaining an example of an operation of an information generating device included in the information providing system in the present disclosure. 1 is a conceptual diagram showing a display example of a policy proposal provided by an information providing system in the present disclosure. 1 is a block diagram showing an example of a configuration of an information generating device according to the present disclosure. 11 is a flowchart for explaining an example of an operation of the information generating device in the present disclosure. FIG. 2 is a conceptual diagram illustrating an example of a hardware configuration according to the present disclosure.

Below, the embodiments for implementing the present disclosure are described with reference to the drawings. In this disclosure, the drawings used in the description of each embodiment relate to one or more embodiments. Furthermore, the elements included in each drawing may apply to one or more embodiments. The embodiments described below are limited in a way that is technically preferable for implementing the present disclosure, but the scope of the disclosure is not limited to the following. In all drawings used in the description of the embodiments below, similar parts are given the same reference numerals unless there is a special reason. In the embodiments below, repeated description of similar configurations and operations may be omitted. The direction of the arrows in the drawings is an example and does not limit the direction of data, signals, etc.

First Embodiment
First, an example of an information providing system in the present disclosure will be described with reference to the drawings. The information providing system of this embodiment estimates the risk of contracting a specific disease (also called disease risk) using sensor data related to foot movements according to walking of a subject located in a target area for which a risk map is generated. The information providing system of this embodiment generates a risk map in which the distribution and locations of people at high disease risk are visualized. In this embodiment, an example is given in which a risk map is generated in which the disease risk for each disease is visualized.

(composition)
FIG. 1 is a block diagram showing an example of a configuration of an information provision system 1 in the present disclosure. The information provision system 1 includes a measurement device 10 and an information generation device 12. For example, the measurement device 10 is installed in the footwear of a subject whose disease risk is to be estimated. For example, the function of the information generation device 12 is installed in a mobile terminal carried by the subject. For example, the function of the information generation device 12 is installed in a server or cloud connected to the mobile terminal carried by the subject via a network. Below, the configurations of the measurement device 10 and the information generation device 12 will be described individually.

[Measuring equipment]
2 is a block diagram showing an example of the configuration of the measurement device 10. The measurement device 10 has a sensor 110, a control unit 113, a communication unit 115, and a power source 117. The sensor 110 has an acceleration sensor 111 and an angular velocity sensor 112. The sensor 110 may include sensors other than the acceleration sensor 111 and the angular velocity sensor 112. Descriptions of sensors other than the acceleration sensor 111 and the angular velocity sensor 112 that may be included in the sensor 110 will be omitted.

The acceleration sensor 111 is a sensor that measures acceleration in three axial directions (also called spatial acceleration). The acceleration sensor 111 measures acceleration (also called spatial acceleration) as a physical quantity related to foot movement. The acceleration sensor 111 outputs the measured acceleration to the control unit 113. For example, the acceleration sensor 111 can be a piezoelectric type, a piezo-resistive type, a capacitance type, or other type of sensor. There are no limitations on the sensor used as the acceleration sensor 111 as long as it can measure acceleration.

Angular velocity sensor 112 is a sensor that measures angular velocity (also called spatial angular velocity) around three axes. Angular velocity sensor 112 measures angular velocity (also called spatial angular velocity) as a physical quantity related to foot movement. Angular velocity sensor 112 outputs the measured angular velocity to control unit 113. For example, a vibration type, capacitance type, or other type of sensor can be used as angular velocity sensor 112. There are no limitations on the sensor used as angular velocity sensor 112 as long as it can measure angular velocity.

The sensor 110 is realized, for example, by an inertial measurement unit that measures acceleration and angular velocity. An example of an inertial measurement unit is an IMU (Inertial Measurement Unit). The IMU includes an acceleration sensor 111 that measures acceleration in three axial directions and an angular velocity sensor 112 that measures angular velocity around three axes. The sensor 110 may be realized by an inertial measurement unit such as a VG (Vertical Gyro) or an AHRS (Attitude Heading Reference System). The sensor 110 may also be realized by a GPS/INS (Global Positioning System/Inertial Navigation System). The sensor 110 may be realized by a device other than an inertial measurement unit as long as it can measure physical quantities related to foot movement.

3 is a conceptual diagram showing an example of the measurement device 10 being placed in the shoes 100 of both feet. In the example of FIG. 3, the measurement device 10 is placed at a position that corresponds to the back side of the arch of the foot. For example, the measurement device 10 is placed in an insole inserted into the shoe 100. For example, the measurement device 10 may be placed on the bottom surface of the shoe 100. For example, the measurement device 10 may be embedded in the body of the shoe 100. The measurement device 10 may be detachable from the shoe 100, or may not be detachable from the shoe 100. The measurement device 10 may be placed at a position other than the back side of the arch of the foot, as long as it can measure sensor data related to foot movement. The measurement device 10 may also be placed in socks worn by the user or in an accessory such as an anklet worn by the user. The measurement device 10 may also be attached directly to the foot or embedded in the foot. The measurement device 10 may also be placed in one of the shoes 100, as long as it can measure data that can be used to estimate disease risk.

In the example of FIG. 3, a local coordinate system is set with the measuring device 10 (sensor 110) as the reference, including an x-axis in the left-right direction, a y-axis in the front-back direction, and a z-axis in the up-down direction. FIG. 3 shows an example in which the same coordinate system is set for the left foot and the right foot. For example, when sensors 110 manufactured with the same specifications are placed in the left and right shoes 100, the up-down orientation (Z-axis orientation) of the sensors 110 placed in the left and right shoes 100 is the same. In this case, the three axes of the local coordinate system set for the sensor data derived from the left foot and the three axes of the local coordinate system set for the sensor data derived from the right foot are the same for the left and right. In this disclosure, the x-axis is positive to the left, the y-axis is positive backward, and the z-axis is positive upward.

FIG. 4 is a conceptual diagram for explaining the local coordinate system (x-axis, y-axis, z-axis) set in the measuring device 10 (sensor 110) installed on the back side of the arch, and the world coordinate system (x-axis, y-axis, z-axis) set with respect to the ground. FIG. 4 shows an example in which different coordinate systems are set for the left foot and the right foot. In the world coordinate system (x-axis, y-axis, z-axis), the user's lateral direction is set to the x-axis direction, the direction of the user's back is set to the y-axis direction, and the direction of gravity is set to the z-axis direction when the user is standing upright facing the direction of travel. Note that the example in FIG. 4 conceptually shows the relationship between the local coordinate system (x-axis, y-axis, z-axis) and the world coordinate system (x-axis, y-axis, z-axis), and does not accurately show the relationship between the local coordinate system and the world coordinate system, which changes according to the user's walking.

FIG. 5 is a conceptual diagram for explaining the planes (also called human body planes) set for the human body. In this embodiment, a sagittal plane that divides the body into left and right, a coronal plane that divides the body into front and back, and a horizontal plane that divides the body horizontally are defined. As shown in FIG. 5, when the user stands upright with the center line of the foot facing the direction of travel, the world coordinate system and the local coordinate system are assumed to match. FIG. 5 shows an example in which different coordinate systems are set for the left and right feet. In this embodiment, the rotation in the sagittal plane around the X-axis (x-axis) as the rotation axis is defined as roll, the rotation in the coronal plane around the Y-axis (y-axis) as the rotation axis is defined as pitch, and the rotation in the horizontal plane around the Z-axis (z-axis) as the rotation axis is defined as yaw. In addition, the rotation angle in the sagittal plane around the X-axis (x-axis) as the rotation axis is defined as roll angle, the rotation angle in the coronal plane around the Y-axis (y-axis) as the rotation axis is defined as pitch angle, and the rotation angle in the horizontal plane around the Z-axis (z-axis) as the rotation axis is defined as yaw angle.

The control unit 113 (control means) causes the acceleration sensor 111 and the angular velocity sensor 112 to measure sensor data. For example, the control unit 113 causes the acceleration sensor 111 and the angular velocity sensor 112 to start measurement in response to a measurement start signal transmitted from the information generating device 12. For example, the control unit 113 may cause the acceleration sensor 111 and the angular velocity sensor 112 to start measurement in response to detection of the user walking. For example, the control unit 113 starts measuring the step width starting from the point in time when it is detected that either the left or right foot has started to move in the forward direction after both feet have been at the same vertical height for a predetermined period of time. The control unit 113 may also be configured to start measuring the step width at a predetermined timing.

The control unit 113 acquires the acceleration in three axial directions from the acceleration sensor 111. The control unit 113 also acquires the angular velocity around three axes from the angular velocity sensor 112. For example, the control unit 113 performs analog-to-digital conversion (ADC) of the acquired physical quantities (analog data) such as angular velocity and acceleration. The physical quantities (analog data) measured by the acceleration sensor 111 and the angular velocity sensor 112 may be converted to digital data in each of the acceleration sensor 111 and the angular velocity sensor 112. For example, an ADC circuit that performs ADC of the physical quantities (analog data) such as angular velocity and acceleration may be provided. The control unit 113 outputs the converted digital data (also called sensor data) to the communication unit 115. For example, the control unit 113 may temporarily store the sensor data in a storage unit (not shown).

The sensor data includes at least acceleration data converted into digital data and angular velocity data converted into digital data. The acceleration data includes acceleration vectors in three axial directions. The angular velocity data includes angular velocity vectors about three axes. The acceleration data and angular velocity data are linked to the time at which they were acquired. The control unit 113 may also apply corrections such as corrections for mounting errors, temperature corrections, and linearity corrections to the acceleration data and angular velocity data.

For example, the control unit 113 may calculate at least one of the gait indices described below. In that case, the measurement device 10 outputs the calculated gait indices to the information generating device 12. For example, the control unit 113 may calculate a feature amount used to estimate physical ability described below. In that case, the measurement device 10 outputs the calculated feature amount to the information generating device 12.

For example, the control unit 113 is realized by a microcomputer or microcontroller that performs overall control of the measuring device 10 and performs data processing. For example, the control unit 113 has a CPU (Central Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), flash memory, etc.

The communication unit 115 (communication means) acquires sensor data from the control unit 113. The communication unit 115 transmits the acquired sensor data to the information generating device 12. The sensor data transmitted from the communication unit 115 is received by the information generating device 12. There are no particular limitations on the timing of transmitting the sensor data. For example, the communication unit 115 transmits the sensor data at a preset transmission timing. For example, the communication unit 115 transmits the sensor data in real time according to the measurement of the sensor data. For example, the communication unit 115 may store sensor data measured over a predetermined period of time and transmit the stored sensor data all at once at a preset timing. For example, the communication unit 115 (communication means) may be configured to receive a measurement start signal from the information generating device 12. In this case, the communication unit 115 outputs the received measurement start signal to the control unit 113.

For example, the communication unit 115 transmits the sensor data to the information generating device 12 via wireless communication. For example, the communication unit 115 transmits the sensor data to the information generating device 12 via a wireless communication function (not shown) that complies with standards such as Bluetooth (registered trademark) or WiFi (registered trademark). The communication function of the communication unit 115 may be compliant with standards other than Bluetooth (registered trademark) or WiFi (registered trademark). The communication unit 115 may transmit the sensor data to the information generating device 12 via a wired connection such as a cable.

The power source 117 is a battery that supplies power for the measurement device 10 to operate. For example, the power source 117 is realized by a thin battery such as a coin type or button type. For example, the power source 117 is realized by a primary battery such as a lithium primary battery, a silver oxide battery, an alkaline button battery, or an air zinc battery. When realized by a primary battery, the power source 117 is preferably realized by a long-life battery. The power source 117 may also be realized by a rechargeable secondary battery. When realized by a secondary battery, the power source 117 may be a battery that can be charged via a wired connection or a battery that can be wirelessly powered. If the power source 117 is capable of wireless power supply, a wireless power supply device may be placed in a place where footwear is placed, such as an entrance or a shoe cupboard. If footwear equipped with the measurement device 10 is placed on the wireless power supply device, the measurement device 10 can be appropriately charged when not in use.

[Information generating device]
6 is a block diagram showing an example of the configuration of the information generating device 12. The information generating device 12 has an acquiring unit 121, a waveform processing unit 122, a gait index calculating unit 123, a storage unit 124, a physical ability estimating unit 125, a disease risk estimating unit 126, a map generating unit 127, and an output unit 129. The waveform processing unit 122, the gait index calculating unit 123, the physical ability estimating unit 125, and the disease risk estimating unit 126 constitute the risk estimating unit 15. The waveform processing unit 122 and the gait index calculating unit 123 constitute the calculating unit 13. The physical ability estimating unit 125 and the disease risk estimating unit 126 constitute the estimating unit 14.

The acquisition unit 121 (acquisition means) acquires sensor data from the measurement device 10 mounted on the footwear of the subject who uses the information provision system 1. The acquisition unit 121 receives the sensor data from the measurement device 10 via wireless communication. The sensor data includes location information of the subject's mobile terminal (not shown), which is the source of the sensor data. For example, the location information is measured by a GPS (Global Positioning System) function mounted on the mobile terminal. For example, the acquisition unit 121 receives the sensor data from the measurement device 10 via a wireless communication function (not shown) that complies with standards such as Bluetooth (registered trademark) and WiFi (registered trademark). Note that the communication function of the acquisition unit 121 may be in accordance with standards other than Bluetooth (registered trademark) and WiFi (registered trademark) as long as it can communicate with the measurement device 10. The acquisition unit 121 may receive the sensor data from the measurement device 10 via a wired connection such as a cable. For example, the acquisition unit 121 may acquire gait indices and feature amounts calculated by the measurement device 10.

The acquisition unit 121 also acquires attribute data of the subject. The attribute data includes gender, date of birth, height, and weight. The date of birth is converted to age. The gender, date of birth (age), height, and weight included in the attribute data are also called physical information. The attribute data also includes the subject's residential address (location information). The subject's residential address (location information) is used to generate a risk map of the target area. Typically, the subject's residential address (location information) is not used to estimate physical ability or disease risk. For example, the attribute data is input via an input device (not shown). For example, the attribute data is input via a mobile terminal used by the subject. For example, the attribute data may be stored in advance in the storage unit 124. The attribute data may be updated at any time according to input by the subject.

The waveform processing unit 122 (waveform processing means) acquires sensor data from the acquisition unit 121. The waveform processing unit 122 extracts time series data for one walking cycle from the time series data of acceleration in three axial directions and angular velocity around three axes contained in the sensor data. The time series data for one walking cycle is also called walking waveform data. The waveform processing unit 122 extracts walking waveform data based on the timing of walking events detected from the time series data of the sensor data. For example, the waveform processing unit 122 extracts walking waveform data that starts at the timing of a heel strike and ends at the timing of the next heel strike.

Figure 7 is a conceptual diagram for explaining a step cycle based on the right foot. The step cycle based on the left foot is the same as that of the right foot. The horizontal axis of Figure 7 shows one walking cycle of the right foot, starting from the point when the heel of the right foot lands on the ground and ending at the point when the heel of the right foot lands on the ground. The horizontal axis of Figure 7 is normalized with the step cycle as 100%. Normalizing one walking cycle to 100% is called the first normalization. One walking cycle of one foot is broadly divided into a stance phase in which at least a part of the sole of the foot is in contact with the ground and a swing phase in which the sole of the foot is off the ground. The stance phase is a period in which at least a part of the sole of the foot is in contact with the ground. The stance phase is further divided into an early stance phase T1, a mid stance phase T2, a final stance phase T3, and an early swing phase T4. The swing phase is a period in which the sole of the foot is off the ground. The swing phase is further divided into early swing T5, mid swing T6, and final swing T7. The horizontal axis in FIG. 7 is normalized so that the stance phase is 60% and the swing phase is 40%. Normalizing the gait waveform data so that the stance phase is 60% and the swing phase is 40% is called second normalization. Note that the periods shown in FIG. 7 are merely examples, and do not limit the periods that make up a step cycle or the names of these periods.

As shown in Figure 7, multiple events occur during walking. Multiple events during walking are also called walking events. P1 represents the event of the heel of the right foot touching the ground (heel strike) (HS: Heel Strike). P2 represents the event of the toe of the left foot lifting off the ground (opposite toe off) while the sole of the right foot is on the ground (OTO: Opposite Toe Off). P3 represents the event of the right heel lifting off the ground (heel rise) while the sole of the right foot is on the ground (HR: Heel Rise). P4 represents the event of the left heel touching the ground (opposite heel strike) (OHS: Opposite Heel Strike). P5 represents the event of the right toe lifting off the ground (toe off) while the sole of the left foot is on the ground (TO: Toe Off). P6 represents an event in which the left and right feet cross (foot crossing) with the sole of the left foot touching the ground (FA: Foot Adjacent). P7 represents an event in which the tibia of the right foot is nearly perpendicular to the ground with the sole of the left foot touching the ground (TV: Tibia Vertical). P8 represents an event in which the heel of the right foot touches the ground (heel strike) (HS: Heel Strike). P8 corresponds to the end of the walking cycle that begins with P1, and corresponds to the starting point of the next walking cycle. Note that the walking events shown in Figure 7 are merely examples, and do not limit the events that occur during walking or the names of those events.

The timing of heel strike is the timing of the minimum peak immediately after the maximum peak that appears in the time series data of forward acceleration (Y-direction acceleration). The maximum peak that marks the timing of heel strike corresponds to the maximum peak of the gait waveform data for one step cycle. The section between successive heel strikes corresponds to one step cycle. The timing of toe off is the timing of the rise of the maximum peak that appears after the stance phase period in which no fluctuations appear in the time series data of forward acceleration (Y-direction acceleration). The midpoint between the timing of the minimum roll angle and the timing of the maximum roll angle corresponds to the mid-stance phase.

The waveform processing unit 122 normalizes (first normalization) the time of the extracted walking waveform data for one step cycle to a walking cycle of 0 to 100% (percent). The timing of 1%, 10%, etc. included in the 0 to 100% walking cycle is also called a walking phase. The waveform processing unit 122 also normalizes (second normalization) the first normalized walking waveform data for one step cycle so that the stance phase is 60% and the swing phase is 40%. By second normalizing the walking waveform data, it is possible to reduce the deviation of the walking phase from which the feature is extracted. The waveform processing unit 122 outputs the normalized walking waveform data to the gait index calculation unit 123.

For example, the waveform processing unit 122 extracts and normalizes walking waveform data for one step cycle using the forward acceleration (Y-direction acceleration). For accelerations/angular velocities other than the forward acceleration (Y-direction acceleration), the waveform processing unit 122 extracts and normalizes walking waveform data for one step cycle in accordance with the walking cycle of the forward acceleration (Y-direction acceleration). The waveform processing unit 122 may also generate time series data of angles around three axes by integrating time series data of angular velocities around three axes. In that case, the waveform processing unit 122 extracts and normalizes walking waveform data for one step cycle in accordance with the walking cycle of the forward acceleration (Y-direction acceleration) for angles around three axes as well.

The waveform processing unit 122 may extract/normalize the walking waveform data for one step cycle using acceleration/angular velocity other than the forward acceleration (Y-direction acceleration). For example, the waveform processing unit 122 may detect heel strike and toe lift from the time series data of vertical acceleration (Z-direction acceleration) (not shown). The timing of heel strike is the timing of a steep minimum peak that appears in the time series data of vertical acceleration (Z-direction acceleration). At the timing of the steep minimum peak, the value of the vertical acceleration (Z-direction acceleration) becomes almost 0. The minimum peak that marks the timing of heel strike corresponds to the minimum peak of the walking waveform data for one step cycle. The section between successive heel strikes is the one step cycle. The timing of toe lift is the timing of an inflection point in the middle of the time series data of vertical acceleration (Z-direction acceleration) gradually increasing after a section of small fluctuation following the maximum peak immediately after heel strike. The waveform processing unit 122 may also extract/normalize the walking waveform data for one step cycle using both the forward acceleration (Y-direction acceleration) and the vertical acceleration (Z-direction acceleration). The waveform processing unit 122 may also extract/normalize the walking waveform data for one step cycle using acceleration, angular velocity, angle, etc. other than the forward acceleration (Y-direction acceleration) and the vertical acceleration (Z-direction acceleration).

The waveform processing unit 122 extracts features (physical ability features) used to estimate physical abilities from the walking waveform data. The waveform processing unit 122 extracts physical ability features used to estimate at least one physical ability. For example, the waveform processing unit 122 extracts physical ability features used to estimate at least one of physical abilities such as grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. For example, the waveform processing unit 122 extracts physical ability features for each walking phase cluster according to preset conditions. A walking phase cluster is a cluster that integrates walking phases that are consecutive in time. A walking phase cluster includes at least one walking phase. A walking phase cluster also includes a single walking phase. The waveform processing unit 122 outputs the extracted physical ability features to the physical ability estimation unit 125.

The gait index calculation unit 123 (gait index calculation means) acquires normalized gait waveform data from the waveform processing unit 122. The gait index calculation unit 123 uses the normalized gait waveform data to calculate gait indices used to estimate physical ability. There are no particular limitations on the gait indices to be calculated, so long as they can be calculated using normalized gait waveform data. For example, the gait index calculation unit 123 calculates gait indices related to distance, height, angle, speed, time, frailty level, CPEI (Center of Pressure Exclusion Index), etc. Representative gait indices are listed below. Specific calculation methods for the following gait indices will be omitted.

For example, the gait index calculation unit 123 calculates indices related to distance and height as gait indices. For example, the gait index calculation unit 123 calculates stride length, turning distance, foot lift height, FTC (Foot Clearance), and MTC (Minimum Toe Clearance). Stride length indicates the distance between the front foot and the rear foot while walking. Turning distance indicates the maximum distance that the foot is moved outward in the direction of travel during the swing phase. Foot lift height indicates the maximum distance between the measuring device 10 (sensor 110) and the ground during the swing phase. FTC indicates the maximum distance between the heel and the ground during the swing phase. MTC indicates the minimum distance between the toe and the ground during the swing phase.

For example, the gait index calculation unit 123 calculates indexes related to angles as gait indices. For example, the gait index calculation unit 123 calculates the contact angle, the take-off angle, the toe direction, the heel contact roll angle, the toe off roll angle, the swing leg peak angular velocity, and the big toe angle. The contact angle indicates the maximum angle between the sole of the foot and the ground at heel contact. The take-off angle indicates the angle between the sole of the foot and the ground during the swing phase. The toe direction indicates the average value of the direction of the toe relative to the direction of travel during the swing phase. The heel contact roll angle is the angle between the ankle and the ground at heel contact as viewed from a rear perspective. The toe off roll angle is the angle between the ankle and the ground at push-off as viewed from a rear perspective. The swing leg peak angular velocity is the angular velocity in the ankle dorsiflexion direction in the section from immediately after push-off until the toe comes closest to the ground. The hallux angle indicates the angle at which the big toe is tilted toward the index toe. Specifically, the hallux angle is the angle between the center line of the first metatarsal bone and the center line of the first proximal phalanx.

For example, the gait index calculation unit 123 calculates an index related to speed as a gait index. For example, the gait index calculation unit 123 calculates walking speed, cadence, and maximum swing speed. Walking speed indicates the walking speed. Cadence indicates the number of steps per minute. Maximum swing speed indicates the speed at which the leg is swung out during the swing phase.

For example, the gait index calculation unit 123 calculates time-related indices as gait indices. For example, the gait index calculation unit 123 calculates stance time, load time, sole contact time, push-off time, swing time, and DST (Double Support Time). Stance time indicates the time that the foot is on the ground while walking. Stance time is the sum of load time, sole contact time, and push-off time. Load time is the time from when the heel touches the ground until the toe touches the ground during the stance phase. Sole contact time is the time during the stance phase when the entire sole of the foot is on the ground and the sole of the foot is horizontal to the ground. Push-off time is the time from when the sole of the foot is on the ground until the toe pushes off the ground during the stance phase. Swing time indicates the time that the foot is off the ground while walking. DST is divided into DST1 and DST2. DST1 indicates the time during which the foot on which the measuring device 10 (sensor 110) is mounted is in front of the other foot during a period when both feet are on the ground at the same time. DST2 indicates the time during which the foot on which the measuring device 10 (sensor 110) is mounted is behind the other foot during a period when both feet are on the ground at the same time.

For example, the gait index calculation unit 123 calculates the frailty level and CPEI (Center of Pressure Exclusion Index) as gait indices. The frailty level is an estimate of the frailty state according to the walking condition. For example, the gait index calculation unit 123 estimates indices such as a judgment result indicating health, a judgment result indicating the possibility of frailty, and a judgment result indicating a high possibility of frailty as the frailty level. The CPEI indicates an estimate of the rate of expansion of the movement of the center of foot pressure applied to the ground during the stance phase.

The memory unit 124 (storage means) stores a physical ability estimation model (described later) that estimates physical ability using physical ability features extracted from the walking waveform data. For example, the physical ability is at least one of grip strength, dynamic balance, lower limb muscle strength, mobility, and static balance. The physical ability may include other than grip strength, dynamic balance, lower limb muscle strength, mobility, and static balance. The memory unit 124 stores physical ability estimation models learned for multiple subjects. For example, the physical ability estimation model outputs an index related to physical ability (physical ability score) in response to input of physical ability features extracted from the walking waveform data.

The memory unit 124 also stores a disease risk estimation model (described later) that estimates disease risk using attribute data, gait index, and physical ability score. The disease risk indicates the risk of contracting a specific disease. For example, the specific diseases include gout, diabetes, hypertension, nephrolithiasis, liver cirrhosis, arteriosclerosis, thromboembolism, dyslipidemia, hypercholesterolemia, and hyperlipidemia. For example, the specific diseases include lower back pain, sleep apnea syndrome, insomnia, depression, osteoarthritis of the knee, and Parkinson's syndrome. The specific diseases may include diseases other than those mentioned above. The memory unit 124 stores a disease risk estimation model trained on multiple subjects. For example, the disease risk estimation model outputs an index related to disease risk (disease risk score) in response to input of attribute data, gait index, and physical ability score. Usually, the address (location information) of the subject's residence included in the attribute data is not used to estimate physical ability or disease risk. For example, the disease risk estimation model may be a model that outputs a disease risk score in response to input of gait indices and attribute data, without using a physical ability score. In that case, the physical ability estimation model does not need to be used.

For example, the physical ability estimation model and disease risk estimation model may be stored in the memory unit 124 when the product is shipped from the factory. The physical ability estimation model and disease risk estimation model may also be stored in the memory unit 124 at the time of calibration before the subject uses the information generating device 12. For example, a physical ability estimation model and disease risk estimation model stored in a storage device (not shown) such as an external server may be used. In this case, it is sufficient that the physical ability estimation model and disease risk estimation model can be accessed via an interface (not shown) connected to the storage device.

The memory unit 124 also stores the attributes of the subject. The attribute data includes gender, date of birth (age), height, and weight. The attribute data also includes the subject's residential address (location information). Typically, the subject's residential address (location information) is not used to estimate physical ability or disease risk. The attribute data may be updated at any time.

Furthermore, the storage unit 124 stores a map of the target area for which a risk map is to be generated. The map of the target area may be stored in advance in the storage unit 124. For example, the map of the target area may not be stored in the storage unit 124, but may be acquired from an external database by the acquisition unit 121.

The physical ability estimation unit 125 (physical ability estimation means) acquires physical ability features extracted from the walking waveform data from the waveform processing unit 122. The physical ability estimation unit 125 also acquires attributes stored in the memory unit 124. The physical ability estimation unit 125 estimates a physical ability score using the physical ability features and attributes. The physical ability estimation unit 125 inputs the physical ability features and the attributes of the subject to a physical ability estimation model stored in the memory unit 124. For example, the physical ability estimation unit 125 estimates a physical ability score related to at least one of the physical abilities of grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. The estimation of the physical ability score by the physical ability estimation unit 125 will be described later. The physical ability estimation unit 125 outputs the physical ability score output from the physical ability estimation model to the disease risk estimation unit 126.

Next, an example of the estimation of the physical ability score by the physical ability estimation unit 125 will be described. Here, an example of the feature values used to estimate grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance will be described. Note that the following example does not limit the physical ability estimated by the physical ability estimation unit 125. The physical ability estimated by the physical ability estimation unit 125 may be appropriately selected depending on the disease for which the disease risk is to be estimated. Note that the disease risk estimation unit 126 may be configured to estimate the disease risk using the gait index and attribute data without using the physical ability score. In that case, the physical ability estimation unit 125 may be omitted from the estimation unit 14.

<Grip strength (total muscle strength of the whole body)>
There is a correlation between grip strength, which is one of the physical abilities, and the total muscle strength of the whole body. Grip strength is also correlated with knee extension strength. For example, an estimated value of grip strength is an index of total muscle strength. For example, a score according to an estimated value of grip strength (also called a total muscle strength score) is an index of total muscle strength. The total muscle strength score is a value obtained by scoring grip strength, which is an index of total muscle strength, according to a preset criterion. Grip strength is affected by attributes such as gender, age, and height. Therefore, the total muscle strength score may be scored according to a criterion for each attribute. In particular, grip strength is affected by gender. Therefore, the total muscle strength score may be scored according to different criteria depending on gender. Note that the index of total muscle strength is not limited to grip strength as long as the total muscle strength can be scored.

The walking phase from which the features used to estimate grip strength are extracted differs depending on gender. For men, there is a correlation between quadriceps activity and grip strength. Therefore, to estimate men's grip strength, features extracted from walking phases in which the characteristics of quadriceps activity are apparent are used. For women, there is a correlation between grip strength and activity of the vastus lateralis, vastus intermedius, and vastus medialis muscles of the quadriceps. Therefore, to estimate women's grip strength, features extracted from walking phases in which the characteristics of vastus lateralis, vastus intermedius, and vastus medialis muscles are apparent are used.

Features AM1, AM2, AM3, and AM4 are used to estimate the grip strength of a man. Feature AM1 is extracted from the 3% walking phase section of the walking waveform data related to the time series data of the acceleration in the forward direction (acceleration in the Y direction). The 3% walking phase is included in the initial stance phase T1. Feature AM1 mainly includes features related to the movement of the vastus lateralis, vastus intermedius, and vastus medialis, which are among the quadriceps muscles. Feature AM2 is extracted from the 59-62% walking phase section of the walking waveform data related to the time series data of the acceleration in the forward direction (acceleration in the Y direction). The 59-62% walking phase is included in the early swing phase T4. Feature AM2 mainly includes features related to the movement of the rectus femoris, which is among the quadriceps muscles. Feature AM3 is extracted from the 59-62% walking phase section of the walking waveform data related to the time series data of the acceleration in the vertical direction (acceleration in the Z direction). 59-62% of the walking phase is included in the early swing phase T4. Feature AM3 mainly includes features related to the movement of the rectus femoris, which is one of the quadriceps muscles. Feature AM4 is the proportion of the period from heel-contact to toe-off of the opposite foot during the period when both feet are simultaneously on the ground (DST1). DST1 is the proportion of the period from heel-contact to toe-off of the opposite foot during one stride cycle. Feature AM4 mainly includes features attributable to the quadriceps muscles.

Feature AF1, feature AF2, and feature AF3 are used to estimate the grip strength of women. Feature AF1 is extracted from a 13% section of the walking phase of the walking waveform data related to the time series data of lateral acceleration (X-direction acceleration). The 13% walking phase is included in the mid-stance phase T2. Feature AF1 mainly includes features related to the movement of the vastus lateralis, vastus intermedius, and vastus medialis of the quadriceps. Feature AF2 is extracted from a 7-10% section of the walking phase of the walking waveform data related to the time series data of the angular velocity (pitch angular velocity) in the coronal plane (around the Y-axis). The 7-10% walking phase is included in the early stance phase T1. Feature AF2 mainly includes features related to the movement of the vastus lateralis, vastus intermedius, and vastus medialis. Feature AF3 is the proportion of the period from heel contact to toe-off of the opposite foot to the period during which both feet are simultaneously on the ground (DST2). DST2 is the ratio of the period from heel contact to toe-off of the opposite foot in a gait cycle. The sum of DST1 and DST2 corresponds to the period during which both feet are simultaneously in contact with the ground in a gait cycle. Feature AF3 mainly includes features related to the movements of the vastus lateralis, vastus intermedius, and vastus medialis.

<Dynamic balance>
Dynamic balance, which is one of the physical abilities, can be evaluated by the results of a Functional Reach Test (FRT). In the present disclosure, the results of the FRT are evaluated by the distance between the fingertips (also called the functional reach distance) when the upper limbs are moved forward as far as possible from a standing position with both hands raised at 90 degrees relative to the horizontal plane. The functional reach distance (hereinafter, called the FR distance) is the FRT performance value. The larger the FR distance, the higher the FRT performance. The dynamic balance may be evaluated by something other than the FRT performed with both hands. For example, the dynamic balance may be evaluated by the performance of the FRT performed with one hand or other variations of the FRT.

The index of dynamic balance is the FR distance. For example, an estimated value of the FR distance is the index of dynamic balance. For example, a score according to the estimated value of the FR distance (also called the dynamic balance score) is the index of dynamic balance. The dynamic balance score is a value obtained by scoring the FR distance, which is an index of dynamic balance, using a preset criterion. Dynamic balance is affected by attributes such as height. Therefore, the dynamic balance score may be scored using a criterion for each attribute. Note that the index of dynamic balance is not limited to the FR distance as long as dynamic balance can be scored. The FR distance is correlated with the activity of the gluteus medius, iliac muscle, hamstrings (long head of biceps femoris), tibialis anterior muscle, etc., and the magnitude of the compensatory movement of turning the toes outward. Therefore, the feature quantity extracted from the walking phase in which these features appear is used to estimate the FR distance.

Features B1, B2, B3, B4, and B5 are used to estimate the FR distance. Feature B1 is extracted from the 75-79% walking phase of the gait waveform data related to the time series data of the acceleration in the forward direction (acceleration in the Y direction). The 75-79% walking phase is included in the mid-swing phase T6. Feature B1 mainly includes features related to the movement of the tibialis anterior and the short head of the biceps femoris. Feature B2 is extracted from the 62% walking phase of the gait waveform data related to the time series data of the acceleration in the vertical direction (acceleration in the Z direction). The 62% walking phase is included in the early swing phase T5. Feature B2 mainly includes features related to the movement of the iliacus. Feature B3 is extracted from the 7-8% walking phase of the gait waveform data related to the time series data of the angular velocity in the coronal plane (around the Y axis). The 7-8% walking phase is included in the early stance phase T1. The feature B3 mainly includes features related to the movement of the gluteus medius. The feature B4 is extracted from the section of the walking phase 57-58% of the walking waveform data related to the time series data of the angle (posture angle) in the horizontal plane (around the Z axis). The walking phase 57-58% is included in the early swing phase T4. The feature B4 mainly includes features related to the compensatory movement. The compensatory movement is a movement to change the foot angle to obtain stability in order to compensate for the deterioration of balance ability and muscle function that occurs with aging. The feature B5 is the average value of the foot angle in the horizontal plane during the swing phase. For example, the feature B5 is the average value in the swing phase of the walking waveform data. In other words, the feature B5 is the integral value of the walking waveform data related to the time series data of the angular velocity in the horizontal plane (around the Z axis). The feature B5 mainly includes features related to the compensatory movement.

<Lower limb strength>
Lower limb muscle strength, which is one of the physical abilities, can be evaluated by the results of a chair stand test. In the present disclosure, the results of the 5-times chair stand test, in which the person stands up and sits down on a chair five times, are evaluated. The 5-times chair stand test is also called the SS-5 (Sit to Stand-5) test. The results of the 5-times chair stand test are evaluated based on the time it takes to stand up and sit down on a chair five times (also called the sit-to-stand time). The sit-to-stand time is the score value of the SS-5 test. The shorter the sit-to-stand time, the higher the score of the SS-5 test. The results may also be evaluated based on the results of a 30-second chair stand (CS-30) test, which measures the number of times the person stands up and sits down on a chair in 30 seconds.

The index of lower limb muscle strength is the sit-stand time. For example, an estimate of the sit-stand time five times is an index of lower limb muscle strength. For example, a score according to the estimate of the sit-stand time (also called the lower limb muscle strength score) is an index of lower limb muscle strength. The lower limb muscle strength score is a value obtained by scoring the sit-stand time, which is an index of lower limb muscle strength, using a preset criterion. Lower limb muscle strength is affected by attributes such as age. Therefore, the lower limb muscle strength score may be scored using a criterion for each attribute. Note that the index of lower limb muscle strength is not limited to the sit-stand time, as long as the lower limb muscle strength can be scored. The sit-stand time is correlated with the quadriceps, hamstrings, tibialis anterior, and gastrocnemius. Therefore, feature values extracted from the walking phase in which these features appear are used to estimate the sit-stand time.

The estimation of lower limb muscle strength includes feature C1, feature C2, feature C3, and feature C4. Feature C1 is extracted from the section of walking phase 42-54% of the walking waveform data related to the time series data of angular velocity in the sagittal plane (around the X-axis). Walking phase 42-54% is the section from the end of stance phase T3 to the early swing phase T4. Feature C1 mainly includes features related to the movement of the gastrocnemius. Feature C2 is extracted from the section of walking phase 99-100% of the walking waveform data related to the time series data of angular velocity in the coronal plane (around the Y-axis). Walking phase 99-100% is the end of the end of swing phase T7. Feature C2 mainly includes features related to the movement of the quadriceps, hamstrings, and tibialis anterior. Feature C3 is extracted from the 10% to 12% walking phase section of the walking waveform data related to the time series data of angular velocity in the coronal plane (around the Y-axis). The 10% to 12% walking phase is the beginning of mid-stance phase T2. Feature C3 mainly includes features related to the movement of the quadriceps, hamstrings, and gastrocnemius. Feature C4 is extracted from the 99% walking phase section of the walking waveform data related to the time series data of angles (posture angles) in the horizontal plane (around the Z-axis). The 99% walking phase is the end of end-swing phase T7. Feature C4 mainly includes features related to the movement of the quadriceps, hamstrings, and tibialis anterior.

<Movement Ability>
Mobility, which is one of the physical abilities, can be evaluated by the results of a TUG (Time Up and Go) test. In the present disclosure, the results of the TUG test are evaluated based on the time it takes to stand up from a chair, walk to a landmark 3 meters away, change direction, and sit back down on the chair (also called the TUG time). The TUG time is the score value of the TUG test. The shorter the TUG time, the higher the score of the TUG test. Mobility may be evaluated by the score of a test related to mobility other than the TUG test.

The index of mobility is the time required for TUG. For example, an estimate of the time required for TUG is an index of mobility. For example, a score according to the estimate of the time required for TUG (also called a mobility score) is an index of mobility. The mobility score is a value obtained by scoring the time required for TUG, which is an index of mobility, using a preset criterion. Mobility is affected by attributes such as age. Therefore, the mobility score may be scored using a criterion for each attribute. Note that the index of mobility is not limited to the time required for TUG, as long as mobility can be scored. The time required for TUG is correlated with the quadriceps, gluteus medius, and tibialis anterior. Therefore, feature quantities extracted from the walking phase in which these features appear are used to estimate the time required for TUG.

Feature amount D1, feature amount D2, feature amount D3, feature amount D4, feature amount D5, and feature amount D6 are used to estimate mobility. Feature amount D1 is extracted from the section of walking phase 64-65% of walking waveform data related to time series data of lateral acceleration (X-direction acceleration). Walking phase 64-65% is included in early swing phase T5. Feature amount D1 mainly includes features related to the movement of the quadriceps in the standing and sitting movements. Feature amount D2 is extracted from the section of walking phase 57-58% of walking waveform data related to time series data of angular velocity in the sagittal plane (around the X-axis). Walking phase 57-58% is included in early swing phase T4. Feature amount D2 mainly includes features related to the movement of the quadriceps related to the kicking speed of the foot. The feature amount D3 is extracted from a section of the walking phase 19-20% of the walking waveform data related to the time series data of the angular velocity in the coronal plane (around the Y axis). The walking phase 19-20% is included in the mid-stance phase T2. The feature amount D3 mainly includes features related to the movement of the gluteus medius muscle in the change of direction. The feature amount D4 is extracted from a section of the walking phase 12-13% of the walking waveform data related to the time series data of the angular velocity in the horizontal plane (around the Z axis). The walking phase 12-13% is the beginning of the mid-stance phase T2. The feature amount D4 mainly includes features related to the movement of the gluteus medius muscle in the change of direction. The feature amount D5 is extracted from a section of the walking phase 74-75% of the walking waveform data related to the time series data of the angular velocity in the horizontal plane (around the Z axis). The walking phase 74-75% is the beginning of the mid-swing phase T6. Feature D5 mainly includes features related to the movement of the tibialis anterior muscle when standing up, sitting down, and changing direction. Feature D6 is extracted from the section of the walking phase 76-80% of the walking waveform data related to the time series data of the angle (posture angle) in the coronal plane (around the Y axis). The walking phase 76-80% is included in the mid-swing phase T6. Feature D6 mainly includes features related to the movement of the tibialis anterior muscle when standing up, sitting down, and changing direction.

<Static balance>
Static balance, which is one of the physical abilities, can be evaluated by the performance of a one-leg standing test. In the present disclosure, the performance of the one-leg standing test is evaluated based on the time (also called one-leg standing time) during which the eyes are closed and one leg is raised 5 cm (centimeters) from the ground. The one-leg standing time is a performance value of static balance. The longer the one-leg standing time, the higher the performance of static balance. Static balance may be evaluated by a performance other than the one-leg standing test with eyes closed. For example, static balance may be evaluated by a one-leg standing test with eyes open (one-leg standing test with eyes open) or other variations of the one-leg standing test.

The static balance index is the single leg standing time. For example, an estimate of the single leg standing time is an index of static balance. For example, a score according to the estimate of the single leg standing time (also called the static balance score) is an index of static balance. The static balance score is a value obtained by scoring the single leg standing time, which is an index of static balance, using a preset criterion. Static balance is affected by attributes such as age and height. Therefore, the static balance score may be scored using a criterion for each attribute. Note that the static balance index is not limited to the single leg standing time as long as the static balance can be scored. The single leg standing time is correlated with the gluteus medius, adductor longus, sartorius, and abductor and adductor muscles. Therefore, the feature values extracted from the walking phase in which these features appear are used to estimate the single leg standing time.

Features E1, E2, E3, E4, E5, E6, and E7 are used to estimate static balance. Feature E1 is extracted from the 13-19% gait phase section of the gait waveform data related to the time series data of lateral acceleration (X-direction acceleration). The 13-19% gait phase is included in the mid-stance phase T2. Feature E1 mainly includes features related to the movement of the gluteus medius. Feature E2 is extracted from the 95% gait phase section of the gait waveform data related to the time series data of vertical acceleration (Z-direction acceleration). The 95% gait phase is the end of the end-swing phase T7. Feature E2 mainly includes features related to the movement of the gluteus medius. Feature E3 is extracted from the 64-65% gait phase section of the gait waveform data related to the time series data of angular velocity in the coronal plane (around the Y-axis). The walking phase 64-65% is included in the early swing phase T5. The feature amount E3 mainly includes features related to the movement of the adductor longus and sartorius. The feature amount E4 is extracted from the section of the walking phase 11-16% of the walking waveform data related to the time series data of the angular velocity in the horizontal plane (around the Z axis). The walking phase 11-16% is included in the mid-stance phase T2. The feature amount E4 mainly includes features related to the movement of the gluteus medius. The feature amount E5 is extracted from the section of the walking phase 57-58% of the walking waveform data related to the time series data of the angular velocity in the horizontal plane (around the Z axis). The walking phase 57-58% is included in the early swing phase T4. The feature amount E5 mainly includes features related to the movement of the adductor longus and sartorius. The feature amount E6 is extracted from the section of the walking phase 100% of the walking waveform data related to the time series data of the angle (posture angle) in the horizontal plane (around the Z axis). The 100% walking phase corresponds to the timing of heel contact when switching from the final swing phase T7 to the initial stance phase T1. The feature value of the walking waveform data in the 100% walking phase corresponds to the foot angle when the sole of the foot is in contact with the ground. Feature value E6 mainly includes features related to the movement of the gluteus medius. Feature value E7 is the distance between the axis of motion and the foot (circumflex over). Feature value E7 is the amount of circular motion normalized by the subject's height. Feature value E7 mainly includes features related to the movement of the abductor and adductor muscles.

8 is a conceptual diagram showing an example of a physical ability estimation model 150 that estimates physical ability. The feature values extracted from the walking waveform data are input to the physical ability estimation model 150 that estimates physical ability. In addition to the feature value data extracted from the walking waveform data, the attributes of the subject are input. In FIG. 8, the attributes input to the physical ability estimation model 150 are omitted. In response to the input of the physical ability feature values extracted from the walking waveform data, the physical ability estimation model 150 outputs a physical ability score related to the physical ability. In the example of FIG. 8, the physical ability estimation model 150 includes a grip strength estimation model 151, a dynamic balance estimation model 152, a lower limb muscle strength estimation model 153, a mobility estimation model 154, and a static balance estimation model 155. Each of the grip strength estimation model 151, the dynamic balance estimation model 152, the lower limb muscle strength estimation model 153, the mobility estimation model 154, and the static balance estimation model 155 outputs a score for each estimation target of the model. The physical ability estimation model 150 may be configured by a single model, not by a model for each physical ability. Also, the physical ability estimation model 150 may be a physical ability value such as grip strength, FR distance, standing and sitting time, TUG time, and one-legged standing time, instead of a physical ability score.

The grip strength estimation model 151 outputs a grip strength score S1 related to grip strength (total muscle strength of the whole body) in response to the input of the feature amounts AM1 to AM4 or the feature amounts AF1 to AF3. For example, the grip strength estimation model 151 may be a model that outputs grip strength in response to the input of the feature amounts AM1 to AM4 or the feature amounts AF1 to AF3. For example, the grip strength estimation model 151 may be a different model for men and women. There are no limitations on the estimation result of the grip strength estimation model 151 as long as an estimation result related to a grip strength index is output in response to the input of a physical ability feature amount for estimating total muscle strength. For example, the grip strength estimation model 151 may be a model that outputs grip strength in response to the input of the feature amounts AM1 to AM4 or the feature amounts AF1 to AF3. For example, the grip strength estimation model 151 may be a model that estimates grip strength using attribute data such as age and height in addition to the feature amounts AM1 to AM4 or the feature amounts AF1 to AF3.

The dynamic balance estimation model 152 outputs a dynamic balance score S2 related to dynamic balance in response to the input of the features B1 to B5. There are no limitations on the estimation results of the dynamic balance estimation model 152, so long as an estimation result related to a dynamic balance index is output in response to the input of the physical ability features for estimating dynamic balance. For example, the dynamic balance estimation model 152 may be a model that outputs the FR distance in response to the input of the features B1 to B5. For example, the dynamic balance estimation model 152 may be a model that estimates dynamic balance using attribute data such as height in addition to the features B1 to B5.

The lower limb muscle strength estimation model 153 outputs a lower limb muscle strength score S3 related to lower limb muscle strength in response to input of the features C1 to C4. There are no limitations on the estimation result of the lower limb muscle strength estimation model 153, so long as an estimation result related to an index of lower limb muscle strength is output in response to input of the physical ability features for estimating lower limb muscle strength. For example, the lower limb muscle strength estimation model 153 may be a model that outputs a lower limb muscle strength score S3 related to lower limb muscle strength in response to input of the features C1 to C4. For example, the lower limb muscle strength estimation model 153 may be a model that estimates dynamic balance using attribute data such as age in addition to the features C1 to C4.

The mobility estimation model 154 outputs a mobility score S4 related to mobility in response to the input of the features D1 to D6. There are no limitations on the estimation results of the mobility estimation model 154, so long as an estimation result related to a mobility index is output in response to the input of the physical ability features for estimating mobility. For example, the mobility estimation model 154 may be a model that outputs the TUG required time in response to the input of the features D1 to D6. For example, the mobility estimation model 154 may be a model that estimates mobility using attribute data such as age in addition to the features D1 to D6.

The static balance estimation model 155 outputs a static balance score S5 related to static balance in response to the input of the features E1 to E7. There are no limitations on the estimation results of the static balance estimation model 155, so long as an estimation result related to a static balance index is output in response to the input of the physical ability features for estimating static balance. For example, the static balance estimation model 155 may be a model that outputs one-leg standing time in response to the input of the features E1 to E7. For example, the static balance estimation model 155 may be a model that estimates static balance using attribute data such as age and height in addition to the features E1 to E7.

The physical ability estimation model 150 may be stored in an external storage device constructed in a cloud or a server. In this case, the physical ability estimation unit 125 uses the physical ability estimation model 150 via an interface (not shown) connected to the storage device. The physical ability estimation model 150 is a machine learning model. For example, the physical ability estimation model 150 is a model trained on a data set using teacher data in which attributes and gait indices of multiple subjects are explanatory variables and a score on physical ability is an objective variable. The physical ability estimation model 150 may be a model trained on a data set using teacher data in which attributes and gait waveform data of multiple subjects are explanatory variables and a score on physical ability is an objective variable. For example, the physical ability estimation model 150 may be a model trained on teacher data in which gait waveform data of acceleration in three axial directions, angular velocity around three axes, and angle (posture angle) around three axes are included in explanatory variables.

For example, the physical ability estimation model 150 may be generated by learning using a linear regression algorithm. For example, the physical ability estimation model 150 may be generated by learning using a support vector machine (SVM) algorithm. For example, the physical ability estimation model 150 may be generated by learning using a Gaussian process regression (GPR) algorithm. For example, the physical ability estimation model 150 may be generated by learning using a random forest (RF) algorithm. For example, the physical ability estimation model 150 may be generated by unsupervised learning that classifies the subject from whom the physical ability feature was generated according to the input of the physical ability feature. There are no particular limitations on the algorithm used to train the physical ability estimation model 150.

The disease risk estimation unit 126 (disease risk estimation) acquires the estimation result of the physical ability (physical ability score) estimated by the physical ability estimation unit 125. The disease risk estimation unit 126 also acquires the gait index from the gait index calculation unit 123. Furthermore, the disease risk estimation unit 126 acquires the attributes of the subject from the storage unit 124. The disease risk estimation unit 126 estimates the disease risk for each disease using the physical ability score, the gait index, and the attributes. For example, the disease risk estimation unit 126 may be configured to estimate the disease risk for each disease using at least the gait index. The disease risk estimation unit 126 associates the estimated disease risk for each disease with the subject and stores it in the storage unit 124. The estimated disease risk for each disease may be accumulated in a dedicated database (not shown) for generating a risk map.

9 is a conceptual diagram showing an example of disease risk estimation by the disease risk estimation unit 126. The disease risk estimation unit 126 inputs attribute data, gait index, and physical ability score used to estimate the disease risk for a specific disease to the disease risk estimation model 160. The attribute data, gait index, and physical ability score used to estimate the disease risk for a specific disease are input to the disease risk estimation model 160. In response to the input of the attribute data, gait index, and physical ability score, the disease risk estimation model 160 outputs a disease risk score for a specific disease. In the example of FIG. 9, a disease risk score is estimated for each of a plurality of diseases. The disease risk estimation model 160 may be configured with a model for each disease, or may be configured with a single model. As the amount of data used for estimation increases, the accuracy of the disease risk score estimation by the disease risk estimation model 160 improves.

For example, the disease risk estimation model 160 outputs a disease risk score for a specific disease such as a lifestyle-related disease. For example, the disease risk estimation model 160 outputs a disease risk score for a specific disease such as gout, diabetes, hypertension, nephrolithiasis, liver cirrhosis, arteriosclerosis, thromboembolism, dyslipidemia, hypercholesterolemia, and hyperlipidemia. For example, the disease risk estimation model 160 includes lower back pain, sleep apnea syndrome, insomnia, depression, osteoarthritis of the knee, and Parkinson's syndrome. The disease risk estimation model 160 may be configured to output a disease risk score for a disease other than those mentioned above. For example, the disease risk estimation model 160 may be configured to estimate a disease risk score including test item data from a health checkup.

The disease risk estimation model 160 may be stored in an external storage device constructed in a cloud, a server, or the like. In this case, the disease risk estimation unit 126 uses the disease risk estimation model 160 via an interface (not shown) connected to the storage device. The disease risk estimation model 160 is a machine learning model. For example, the disease risk estimation model 160 is a model trained using a data set in which attributes, gait indices, and physical abilities of multiple subjects are used as explanatory variables, and a disease risk score for a specific disease is used as a target variable as training data. For example, the disease risk estimation model 160 may be a model trained using training data in which gait waveform data of acceleration in three axial directions, angular velocity around three axes, and angles around three axes (posture angles) are included as explanatory variables.

For example, the disease risk estimation model 160 is generated by learning using a linear regression algorithm. For example, the disease risk estimation model 160 is generated by learning using a support vector machine (SVM) algorithm. For example, the disease risk estimation model 160 is generated by learning using a Gaussian process regression (GPR) algorithm. For example, the disease risk estimation model 160 is generated by learning using a random forest (RF) algorithm. For example, the disease risk estimation model 160 may be generated by unsupervised learning that classifies the subject from whom the feature data was generated according to the feature data. There are no particular limitations on the algorithm used to train the disease risk estimation model 160.

For example, the disease risk estimation model 160 may be a machine learning model such as an incomplete heterogeneous variational autoencoder or a random forest. If an incomplete heterogeneous variational autoencoder is used, the disease risk of a subject can be estimated even if there are some missing data in the attribute data, gait index, physical ability score, etc.

10 is a conceptual diagram showing an example of a disease risk estimation model 165 that estimates the annual average number of receipts issued. The disease risk estimation unit 126 inputs attribute data, gait index, and physical ability score to the disease risk estimation model 165. The disease risk estimation model 165 receives attribute data, gait index, and physical ability score used to estimate the disease risk for a specific disease. In response to the input of the attribute data, gait index, and physical ability score, the disease risk estimation model 165 outputs the annual average number of receipts issued for a specific disease. In the example of FIG. 10, the annual average number of receipts issued is estimated for each of a plurality of diseases. The disease risk estimation unit 126 calculates a disease risk score using the annual average number of receipts issued output from the disease risk estimation model 165. The annual average number of receipts issued may be used as the disease risk score.

Here, an example will be described in which the disease risk estimation unit 126 calculates a disease risk score using the average annual number of receipts issued. Three calculation examples will be given below. It is assumed that the average annual number of receipts issued for a standard person μ ₀ has been obtained in advance. The disease risk estimation model 165 outputs the average annual number of receipts issued μ for a specific disease in response to input of attribute data, gait index, and physical ability score for a person whose disease risk is to be estimated.

In the first method, the disease risk estimation unit 126 calculates, as the disease risk score, the ratio of the average annual number of medical receipts issued for a standard person μ ₀ to the average annual number of medical receipts issued for the subject μ. The disease risk estimation unit 126 calculates the disease risk score RS ₁ using the following formula 1.

第２の手法において、疾病リスク推定部１２６は、特定疾病に関する年平均レセプト発行数がポアソン分布に従うという仮定の下で、疾病リスクスコアを計算する。第２の手法において、疾病リスク推定部１２６は、標準的な人の年平均レセプト発行数の確率質量関数Ｐ₀（Ｘ＝ｋ）と、対象者に関して推定された年平均レセプト発行数の確率質量関数Ｐ（Ｘ＝ｋ）との比を、疾病リスクスコアとして計算する（ｋは自然数）。疾病リスク推定部１２６は、以下の式２を用いて、疾病リスクスコアＲＳ₂を計算する。

In the second method, the disease risk estimation unit 126 calculates the disease risk score under the assumption that the annual average number of receipts issued for a specific disease follows a Poisson distribution. In the second method, the disease risk estimation unit 126 calculates the disease risk score as the ratio of the probability mass function _P0 (X=k) of the annual average number of receipts issued for a standard person to the probability mass function P(X=k) of the annual average number of receipts issued estimated for the subject (k is a natural number). The disease risk estimation unit 126 calculates the disease risk score _RS2 using the following formula 2.

第３の手法において、疾病リスク推定部１２６は、特定疾病に関する年平均レセプト発行数のオッズ比を計算する。疾病リスク推定部１２６は、以下の式３を用いて、疾病リスクスコアＲＳ₃を計算する。

In the third method, the disease risk estimation unit 126 calculates the odds ratio of the annual average number of receipts issued for a specific disease. The disease risk estimation unit 126 calculates a disease risk score _RS3 using the following formula 3.

なお、上記の３通りの計算例は、一例であって、年平均レセプト発行数を用いた疾病リスクスコアの計算方法を限定するものではない。疾病リスク推定部１２６は、年平均レセプト発行数以外の指標を用いて、疾病リスクスコアを計算するように構成されてもよい。

The above three calculation examples are merely examples, and do not limit the method of calculating the disease risk score using the annual average number of medical receipts issued. The disease risk estimation unit 126 may be configured to calculate the disease risk score using an index other than the annual average number of medical receipts issued.

The map generation unit 127 acquires from the memory unit 124 the disease risk of the disease for which the risk map is to be generated, for the subject associated with the target district. For example, the location of the subject is identified by the address of the subject's residence. For example, the location of the subject may be identified by location information acquired from a mobile device that is the source of sensor data measured according to the subject's foot movements. The map generation unit 127 also acquires a map of the target district.

The map generating unit 127 sets the display conditions of the image showing the distribution of the acquired disease risk. The map generating unit 127 generates an image (heat map) in which the display state of the disease risk distribution is set by color coding, shading, etc., in association with the positions of areas, residences, etc. included in the target district. The map generating unit 127 sets the display state of an indicator according to the degree of disease risk in association with the positions of areas, residences, etc. included in the target district. For example, the map generating unit 127 sets a display state indicator such as hue, brightness, saturation, etc. in accordance with the degree of disease risk. The display state of the indicator according to the degree of disease risk may be set according to criteria other than hue, brightness, and saturation. For example, the map generating unit 127 may set the display state such as shape, size, color, etc. for each disease.

The map generating unit 127 sets the display state of an indicator indicating the degree of disease risk in association with an area included in the target district. For example, the map generating unit 127 calculates disease risk score statistics for multiple subjects associated with an area included in the target district, and sets the display condition of the indicator according to the calculated statistical value for that area. For example, the statistical amount includes the total value or average value of the disease risk score. There are no particular limitations on the division of the target district into areas. For example, the map generating unit 127 divides the map of the target district at equal intervals. For example, the map generating unit 127 divides the map of the target district into a grid pattern. For example, the map generating unit 127 sets the area of the map of the target district in city, town, and village units. For example, the map generating unit 127 sets the area of the map of the target district in units of house address indication (block, street address, etc.) divided by the block method or road method.

The map generating unit 127 may set the display state of an indicator indicating the degree of disease risk in association with a residence included in the target area. For example, the map generating unit 127 sets the display state for a subject associated with a residence included in the target area. For example, the map generating unit 127 calculates disease risk score statistics for multiple subjects associated with residences included in the target area, and sets the display conditions of the indicator for the residence according to the calculated statistics. For example, the statistics include the total value or average value of the disease risk score.

The map generating unit 127 may set the display state of the indicator indicating the degree of disease risk in association with the location of the subject staying in the target area. For example, the map generating unit 127 sets the display state of the indicator indicating the degree of disease risk in association with the location specified by the location information of the subject staying in the target area. For example, the map generating unit 127 updates the display state of the indicator indicating the degree of disease risk in association with a change in the location specified by the location information of the subject. In this case, the risk map can be updated in accordance with the movement of the subject.

The map generator 127 generates a risk map by superimposing the generated image (heat map) on a map of the target area according to the set display conditions. It is preferable that the map of the target area is visible through the superimposed image (heat map). If it is known that it corresponds to an area, residence, or subject included in the target area, the indicator showing the degree of disease risk may be shifted from the location of the identified area, residence, or subject.

FIGS. 11 to 16 are conceptual diagrams for explaining risk maps generated by the map generation unit 127. FIG. 11 is a conceptual diagram showing an example of a map (Map M) of a target area. Map M in FIG. 11 shows the locations of facilities (Facility A, Facility B, Facility C, Facility D) in the target area. As shown in FIG. 11, even if a facility is in the target area, it may be difficult to access depending on the location of the subject's residence relative to the facility. For example, a facility located on the other side of a railroad track or a river from the subject's location is difficult to access, even though it is close in distance.

Figure 12 is an example of a risk map (risk map RM1) on which an indicator showing the degree of diabetes disease risk is displayed. In risk map RM1, the indicator showing the degree of diabetes disease risk is displayed as a circle (dashed line). The indicator showing the degree of diabetes disease risk may be expressed as a continuous change across the entire risk map RM1, rather than as a closed figure such as a circle. In risk map RM1, the degree of diabetes disease risk is shown in shades of gray. For example, the greater the degree of diabetes disease risk, the darker the indicator is set to be displayed. For example, the higher the degree of diabetes disease risk, the larger the indicator may be displayed.

In the example of FIG. 12, a diabetes specialist is resident at facility D. On the other side of the railroad tracks from facility D is region _R1 , which has a large indicator area. There is a high possibility that the number of subjects living in this region _R1 who will receive diabetes treatment in the future will increase. Therefore, if an overbridge is provided on the railroad tracks between facility D and region _R1 , even if the number of people receiving diabetes treatment increases, it will be easier for them to visit facility D from region _R1 . For example, by referring to the risk map RM1, a staff member of the town hall of the target district can get an opportunity to plan a plan to provide an overbridge on the railroad tracks between facility D and region _R1 . Also, in the example of FIG. 12, if a diabetes specialist is resident at facility B near region _R1 , it will be easier for subjects living in region _R1 to visit facility B for diabetes treatment. For example, a staff member of the town hall of the target district can get an opportunity to plan a plan to attract a diabetes specialist to facility B by referring to the risk map RM1. In other words, the risk map RM1 can provide information to support planning decisions for the target area.

Figure 13 is an example of a risk map (risk map RM2) on which an indicator showing the degree of disease risk of lower back pain is displayed. In risk map RM2, the indicator showing the degree of disease risk of lower back pain is displayed as a hexagon (dash line). The indicator showing the degree of disease risk of lower back pain may be expressed as a continuous change across the entire risk map RM2, rather than as a closed figure such as a hexagon. In risk map RM2, the degree of disease risk of lower back pain is shown in shades of gray. For example, the greater the degree of disease risk of lower back pain, the darker the indicator is set to. For example, the higher the degree of disease risk of lower back pain, the larger the indicator may be set to be displayed.

In the example of FIG. 13, facility C is a hospital specializing in lower back pain. Facility C is far from a station and currently far from a bus route. For example, transportation such as a taxi is used to go to facility C from area _R1 . Transportation such as a taxi is used to go to facility C from other areas. For example, by referring to the risk map RM2, a staff member of the town hall of the target area can get an opportunity to plan a plan to set up a bus route near facility C. Also, in the example of FIG. 13, if there is a bus that travels inside the target area and heads to facility C, it will be easier to go to facility C. For example, by referring to the risk map RM2, a staff member of the town hall of the target area can get an opportunity to plan a plan to build a bus route that travels inside the target area. In other words, the risk map RM2 can provide information that supports decision-making on plans for the target area.

14 is an example of a risk map (risk map RM3) on which an indicator showing the degree of disease risk of knee osteoarthritis is displayed. In risk map RM3, the indicator showing the degree of disease risk of knee osteoarthritis is displayed as a star polygon (two-dot chain line). The indicator showing the degree of disease risk of knee osteoarthritis may be expressed as a continuous change in the entire risk map RM3, rather than as a closed figure such as a star polygon. In risk map RM3, the degree of disease risk of knee osteoarthritis is shown in shades. For example, the higher the degree of disease risk of knee osteoarthritis, the darker the indicator is set to a display state. For example, the higher the degree of disease risk of knee osteoarthritis, the larger the indicator may be set to a display state. For example, the higher the degree of disease risk of knee osteoarthritis, the greater the number of points of the star polygon may be set to a display state.

In the example of FIG. 14, facility A is a rehabilitation facility for osteoarthritis of the knee. Facility A is close to a station, so it is easy for residents living in the area near the station to commute to the facility. However, transportation such as a taxi is used to go to facility A from area _R3 . Transportation such as a taxi is used to go to facility A from other areas. For example, by referring to the risk map RM3, a staff member of the town hall of the target area can get an opportunity to plan a plan to attract a rehabilitation facility for osteoarthritis of the knee to a neighborhood of an area with a large indicator. Also, in the example of FIG. 14, if there is a bus that travels inside the target area and goes to facility A, it will be easier to visit facility A. For example, by referring to the risk map RM3, a staff member of the town hall of the target area can get an opportunity to plan a plan to build a bus route that travels inside the target area. In other words, the risk map RM3 can provide information that supports decision-making on plans for the target area.

15 is an example of a risk map (risk map RM4) on which indicators showing the degree of disease risk for multiple diseases are displayed. Indicators showing the degree of disease risk for diabetes, back pain, and osteoarthritis of the knee, as exemplified in Figs. 12 to 14, are displayed on risk map RM4. For example, if indicators showing the disease risk for each of diabetes, back pain, and osteoarthritis of the knee are displayed in different shapes and colors, the distribution of these diseases can be distinguished. For example, if there is no distinction between diabetes, back pain, and osteoarthritis of the knee, the indicators showing the disease risk for each of these diseases may be displayed in the same shape or color. In risk map RM3, the degree of disease risk is shown in shades. For example, the higher the degree of disease risk, the darker the indicator is set to be displayed. For example, the higher the degree of disease risk, the larger the indicator may be set to be displayed. For example, by referring to risk map RM4, city hall staff in the target area can get an opportunity to consider measures that can comprehensively deal with multiple diseases. In other words, risk map RM4 can provide information to support decision-making regarding policies for target areas.

16 is an example of a risk map (risk map RM5) in which an indicator showing the degree of disease risk of a disease is displayed in association with the location of a subject in a target area. The location of the subject is identified using location information of a mobile terminal (not shown) carried by the subject. An indicator showing the degree of disease risk of the subject is displayed at the subject's location. For example, the higher the degree of disease risk, the darker the indicator may be set to. For example, the higher the degree of disease risk, the larger the indicator may be set to. For example, by referring to the risk map RM5, a staff member at the town hall in the target area can grasp the disease risk of subjects in the target area in real time. For example, the staff member at the town hall in the target area can be given an opportunity to consider measures according to the movement status of subjects at risk of disease. For example, the staff member at the town hall in the target area can be given an opportunity to consider some kind of measure by comparing the trend of daytime stay locations with the trend of nighttime stay locations. For example, the staff member at the town hall in the target area can be given an opportunity to consider measures such as delivery of deliveries and crime prevention by examining the proportion of time spent at home and time outside. In other words, Risk Map RM5 can provide information to support decision-making regarding policies for target areas.

The output unit 129 (output means) outputs risk information including the risk map estimated by the map generation unit 127. For example, the output unit 129 outputs the risk information to a terminal device or server managed by the town office of the target district. For example, the output unit 129 may display the risk information on the screen of the target person's mobile terminal. For example, the output unit 129 may output the risk information to an external system that uses the risk information. There are no particular limitations on the use of the output risk information. For example, the risk information is used for statistical analysis, research into disease prevention, etc.

For example, the information generating device 12 is connected to an external system built on a cloud or a server via a mobile terminal (not shown) carried by the subject. The mobile terminal (not shown) is a portable communication device. For example, the mobile terminal is a portable communication device having a communication function such as a smartphone, a smart watch, or a mobile phone. For example, the information generating device 12 is connected to the mobile terminal via wireless communication. For example, the information generating device 12 is connected to the mobile terminal via a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the information generating device 12 may conform to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark). For example, the information generating device 12 may be connected to the mobile terminal via a wire such as a cable. The disease risk information may be used by an application installed on the mobile terminal. In that case, the mobile terminal executes a process using the risk information by application software or the like installed on the mobile terminal.

(Operation)
Next, the operation of the information providing system 1 will be described with reference to the drawings. The operation of the information generating device 12 included in the information providing system 1 will be described below. Fig. 17 is a flowchart for explaining an example of the operation of the information generating device 12. In the explanation of the process according to the flowchart of Fig. 17, the components of the information generating device 12 will be explained as the subject of the operation. The subject of the process according to the flowchart of Fig. 17 may be the information generating device 12.

In FIG. 17, first, the acquisition unit 121 acquires time series data of sensor data measured by the measurement device 10 mounted on the footwear (step S11). The sensor data includes acceleration in three axial directions and angular velocity around three axes.

Next, the calculation unit 13 executes a gait index calculation process using the acquired sensor data (step S12). In the gait index calculation process, the calculation unit 13 calculates a gait index used to estimate physical ability. Details of the gait index calculation process in step S12 will be described later ( FIG. 18 ).

Next, the physical ability estimation unit 125 estimates physical ability using the attribute data and gait index (step S13). For example, the physical ability estimation unit 125 estimates physical ability scores such as grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. If disease risk is estimated without using physical ability, step S13 can be omitted.

Next, the disease risk estimation unit 126 estimates the disease risk for each disease using the attribute data, gait index, and physical ability (step S14). When the disease risk is estimated without using physical ability, the disease risk estimation unit 126 estimates the disease risk for each disease using the attribute data and gait index. The disease risk estimation unit 126 estimates a disease risk score for each disease. For example, the disease risk estimation unit 126 estimates a disease risk score for each disease, such as gout, diabetes, hypertension, nephrolithiasis, liver cirrhosis, arteriosclerosis, thromboembolism, dyslipidemia, hypercholesterolemia, and hyperlipidemia. For example, the disease risk estimation unit 126 estimates a disease risk score for each disease, such as lower back pain, sleep apnea syndrome, insomnia, depression, osteoarthritis of the knee, and Parkinson's syndrome.

Next, the memory unit 124 accumulates the estimated disease risk (step S15). The disease risk accumulated in the memory unit 124 is used to generate a risk map.

Next, the map generation unit 127 executes a risk map generation process using the disease risks stored in the storage unit 124 (step S16). In the risk map generation process, the map generation unit 127 generates a risk map of the target area. Details of the risk map generation process of step S16 will be described later (FIG. 19).

Next, the output unit 129 outputs the risk information including the generated risk map (step S17). For example, the output unit 129 outputs the risk information to a terminal device or server managed by the town office of the target district. For example, the output unit 129 outputs the risk information to an external system that uses the risk information. For example, the output unit 129 may display the risk information on the screen of the target person's mobile terminal.

Note that it is not necessary to obtain attribute data on the subject. If attribute data is not obtained from the subject, a model can be used that estimates disease risk without using attribute data. Also, the subject may be asked in advance for consent to obtaining the attribute data. At that time, the benefits of obtaining the attribute data may be communicated to the subject, encouraging them to consent to obtaining the attribute data. An example of a benefit here is that more accurate risk estimation results can be obtained.

[Walking index calculation processing]
Next, the gait index calculation process (step S12 in FIG. 17) by the calculation unit 13 of the information generating device 12 will be described with reference to the drawings. FIG. 18 is a flowchart for explaining an example of the operation of the calculation unit 13. In the explanation of the process according to the flowchart in FIG. 18, the components of the calculation unit 13 will be described as the subject of the operation. The subject of the operation of the process according to the flowchart in FIG. 18 may be the information generating device 12 or the calculation unit 13.

In FIG. 18, first, the waveform processing unit 122 extracts walking waveform data from the time series data of the sensor data (step S121). The walking waveform data corresponds to the time series data of the sensor data for one walking cycle.

Next, the waveform processing unit 122 normalizes the extracted walking waveform data (step S122). The waveform processing unit 122 performs first normalization on the walking waveform data so that the step period is 100%. The waveform processing unit 122 also performs second normalization on the walking waveform data so that the stance phase is 60% and the swing phase is 40%.

Next, the gait index calculation unit 123 uses the normalized walking waveform data to calculate gait indices used to estimate physical ability (step S123). For example, the gait index calculation unit 123 calculates gait indices related to distance, height, angle, speed, time, frailty level, CPEI, etc.

[Risk map generation process]
Next, the risk map generation process (step S16 in FIG. 17 ) by the map generation unit 127 of the information generation device 12 will be described with reference to the drawings. FIG. 19 is a flowchart for explaining an example of the operation of the map generation unit 127. In the explanation of the process according to the flowchart in FIG. 19 , the map generation unit 127 will be described as the subject of the operation. The subject of the operation of the process according to the flowchart in FIG. 19 may be the information generation device 12.

In FIG. 19, first, the map generator 127 identifies the position of the subject (step S161).

Next, the map generation unit 127 calculates the distribution of disease risk for the target disease for each area included in the target district according to the location of the identified subjects (step S162).

Next, the map generation unit 127 sets the display conditions for the indicator that shows the degree of disease risk for the target disease (step S163).

If a risk map of the target area has not been generated (No in step S164), the map generation unit 127 acquires a map of the target area (step S165). If a risk map of the target area has already been generated (Yes in step S164), the process proceeds to step S166.

After step S165, or if the answer is Yes in step S164, the map generation unit 127 superimposes an indicator indicating the degree of disease risk of the target disease on the map of the target area according to the set display conditions (step S166). The map of the target area on which the indicator indicating the degree of disease risk of the target disease is superimposed is the risk map.

(Application example)
Next, application examples according to the present embodiment will be described with reference to the drawings. In the following application examples, the relationship between a business providing a service using the information provision system 1, a local government using the service, and residents (target persons) living in an area managed by the local government is shown.

Figure 20 is a correlation diagram showing the relationship between businesses, local governments, and residents (subjects). Businesses provide services to local governments using the information provision system 1. Based on a contract concluded with the local government, businesses provide risk information, including a risk map, to the local government. The local government pays the business a fee for the service using the information provision system 1. When resident health checkup data is used to generate a risk map, the local government provides the resident health checkup data to the business. In the contract between the business and the local government, rules regarding the handling of personal information and appropriate data management are clarified. The business clearly explains that the risk information is for reference only, and does not guarantee medical accuracy or completeness.

Local governments will fully explain the details of their personal information protection policies and data management to residents and obtain their consent. If there are any changes to the details of their personal information protection policies or data management, local governments will explain the changes to residents and obtain their consent. For example, consent from residents will be obtained electronically. Local governments will implement measures for the areas where residents live depending on the risk information provided by businesses.

Residents are the entities that pay taxes to the local government. Residents are loaned or provided with special insoles equipped with a measuring device 10 by a business that has a contract with the local government. The resident wears shoes equipped with the special insoles and walks around carrying a mobile terminal capable of communicating with the measuring device 10. The mobile terminal uploads the sensor data measured by the measuring device 10 to the business's cloud server.

The terminal device used by the local government downloads risk information for the target area from the operator's cloud server. The local government refers to the risk information. The local government refers to the risk map contained in the risk information and considers measures for residents. The local government regularly refers to the risk map for the target area and considers measures in response to changes in the risk map. For example, the local government holds consultation sessions and events to incorporate residents' opinions and requests regarding measures in response to changes in the risk map.

FIG. 21 shows an example of a risk map displayed on the screen of a terminal device 180 used by a local government. Risk information including a risk map generated for a target area managed by the local government is displayed on the screen of the terminal device 180 in an optimized manner for each local government. Staff members who check the risk information including the risk map displayed on the screen of the terminal device 180 can consider measures for the target area.

As described above, the information provision system of this embodiment includes a measuring device and an information generating device. The measuring device is installed in the footwear of at least one of the subjects. The measuring device measures spatial acceleration and spatial angular velocity. The measuring device generates sensor data using the measured spatial acceleration and spatial angular velocity. The measuring device transmits the generated sensor data to the information generating device. The information generating device includes an acquiring unit, a risk estimation unit, a map generating unit, and an output unit. The acquiring unit acquires sensor data measured by the measuring device mounted in the footwear of the at least one subject. The risk estimation unit estimates a disease risk for each disease for the at least one subject using the acquired sensor data. The map generating unit generates a risk map in which an indication according to the disease risk of the target disease for the at least one subject is superimposed on a map of the target area. The output unit outputs risk information including the generated risk map.

The information generating device of this embodiment estimates the disease risk of a target disease using sensor data measured by a measuring device mounted on the subject's footwear. The information generating device of this embodiment generates a risk map in which a display according to the estimated disease risk of the target disease is superimposed on a map of the target area. Therefore, according to this embodiment, it is possible to generate a risk map in which the locations of people at risk of contracting the target disease are visualized.

In one aspect of this embodiment, the risk estimation unit has a calculation unit and an estimation unit. The calculation unit calculates a gait index using sensor data. The estimation unit inputs data including the gait index calculated using the sensor data to a disease risk estimation model, and estimates disease risk information corresponding to the disease risk score output from the disease risk estimation model. The disease risk estimation model outputs a disease risk score indicating the degree of disease risk for each disease in response to the input of data including the gait index. According to this aspect, disease risk information corresponding to the disease risk score can be estimated by inputting data including the gait index calculated using sensor data to the disease risk estimation model.

In one aspect of this embodiment, the map generation unit identifies the location of at least one subject. The map generation unit calculates the distribution of disease risk of the target disease for each area included in the target district according to the location of the identified at least one subject. The map generation unit sets display conditions for an indicator indicating the degree of disease risk of the target disease for each area included in the target district. The map generation unit superimposes an indicator indicating the degree of disease risk of the target disease on a map of the target district in accordance with the set display conditions. According to this aspect, a risk map can be generated on which an indicator indicating the degree of disease risk of the target disease is superimposed.

In one aspect of this embodiment, the map generation unit sets display conditions for indicators indicating the degree of disease risk for multiple target diseases for each area included in the target district. The map generation unit superimposes indicators indicating the degree of disease risk for multiple target diseases on a map of the target district in accordance with the set display conditions. According to this aspect, a risk map can be generated in which indicators related to multiple target diseases are displayed.

In one aspect of this embodiment, the map generation unit calculates disease risk score statistics for at least one subject associated with a position within an area included in the target district. The map generation unit sets display conditions for an indicator corresponding to the calculated disease risk score statistics for the area. According to this aspect, a risk map can be generated in which an indicator corresponding to the disease risk score statistics is displayed.

In one aspect of this embodiment, the map generation unit identifies the subject's location based on the location of the subject's residence. According to this aspect, a risk map can be generated in which an indicator showing the degree of disease risk for the target disease is displayed for each area corresponding to the subject's residence.

In one aspect of this embodiment, the map generation unit identifies the subject's location based on location information of a mobile device carried by the subject. According to this aspect, a risk map can be generated in which an indicator showing the degree of disease risk for the target disease is displayed for each area corresponding to the location of the mobile device carried by the subject.

In one aspect of this embodiment, the map generation unit identifies the subject's location based on location information of a mobile device carried by the subject. The map generation unit updates the risk map in response to changes in the subject's location. According to this aspect, the risk map displaying an indicator showing the degree of disease risk for the target disease can be updated in response to changes in the location of the mobile device carried by the subject.

In one aspect of this embodiment, the policy estimation model and the disease risk estimation model are models trained using machine learning techniques. The disease risk estimation model includes an incomplete heterogeneous variational autoencoder. According to this aspect, even if there is some loss of data such as gait indicators, the disease risk of the subject can be estimated.

In one aspect of this embodiment, the information generating device displays risk information about the target area on the screen of a terminal device used by the local government that manages the target area, optimized for each local government. According to this aspect, risk information including a risk map and policy proposals for the target area can be provided in an optimized manner for each local government that manages the target area.

Second Embodiment
Next, an information generating device according to a second embodiment will be described with reference to the drawings. The information generating device according to this embodiment generates a policy proposal according to a risk map. The information generating device according to this embodiment outputs risk information including a policy proposal for a local government.

(composition)
22 is a block diagram showing an example of the configuration of the information provision system 2 in the present disclosure. The information provision system 2 includes a measurement device 20 and an information generation device 22. For example, the measurement device 20 is installed in the footwear of a subject whose disease risk is to be estimated. The measurement device 20 has a similar configuration to the measurement device 10 of the first embodiment. In the following, a description of the measurement device 20 will be omitted, and only the information generation device 22 will be described. Note that the main configuration of the information generation device 22 is similar to the configuration of the information generation device 12 of the first embodiment, and therefore the description may be omitted.

[Information generating device]
23 is a block diagram showing an example of the configuration of the information generating device 22. The information generating device 22 has an acquiring unit 221, a calculating unit 23, an estimating unit 24, a memory unit 224, a map generating unit 227, a policy proposal generating unit 228, and an output unit 229. The calculating unit 23 and the estimating unit 24 configure a risk estimating unit 25.

The acquisition unit 221 (acquisition means) has the same configuration as the acquisition unit 121 of the first embodiment. The acquisition unit 221 acquires sensor data from the measurement device 20 mounted on the footwear of the subject who uses the information provision system 2. The acquisition unit 221 receives the sensor data from the measurement device 20 via wireless communication. The sensor data includes location information of the subject's mobile terminal (not shown) that is the source of the sensor data. For example, the acquisition unit 221 receives the sensor data from the measurement device 20 via a wireless communication function (not shown) that complies with standards such as Bluetooth (registered trademark) and WiFi (registered trademark). Note that the communication function of the acquisition unit 221 may be in accordance with standards other than Bluetooth (registered trademark) and WiFi (registered trademark) as long as it can communicate with the measurement device 20. The acquisition unit 221 may receive the sensor data from the measurement device 20 via a wired connection such as a cable. For example, the acquisition unit 221 may acquire gait indices and feature amounts calculated by the measurement device 20.

The acquisition unit 221 also acquires attributes of the subject. The attribute data includes gender, date of birth, height, and weight. The date of birth is converted to age. The attribute data also includes the subject's residential address (location information). The subject's residential address (location information) is used to generate a risk map of the target area. Typically, the subject's residential address (location information) is not used to estimate physical ability or disease risk. For example, the attribute data is input via an input device (not shown). For example, the attribute data is input via a mobile terminal used by the subject. For example, the attribute data may be stored in advance in the storage unit 224. The attribute data may be updated at any time in response to input by the subject.

The calculation unit 23 (calculation means) has the same configuration as the calculation unit 13 of the first embodiment. The calculation unit 23 has the functions of the waveform processing unit 122 and gait index calculation unit 123 of the first embodiment. The calculation unit 23 acquires sensor data from the acquisition unit 221. The calculation unit 23 extracts time series data for one walking cycle (gait waveform data) from the time series data of acceleration in three axial directions and angular velocity about three axes included in the sensor data. The calculation unit 23 extracts gait waveform data based on the timing of walking events detected from the time series data of the sensor data. For example, the calculation unit 23 extracts gait waveform data that starts from the timing of a heel strike and ends with the timing of the next heel strike.

The calculation unit 23 normalizes (first normalization) the time of the extracted walking waveform data for one step cycle to a walking cycle of 0 to 100% (percent). The calculation unit 23 also normalizes (second normalization) the first normalized walking waveform data for one step cycle so that the stance phase is 60% and the swing phase is 40%.

The calculation unit 23 extracts features (physical ability features) used to estimate physical abilities from the walking waveform data. The calculation unit 23 extracts physical ability features used to estimate at least one physical ability. For example, the calculation unit 23 extracts physical ability features used to estimate at least one of physical abilities such as grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. For example, the calculation unit 23 extracts physical ability features for each walking phase cluster according to preset conditions.

The calculation unit 23 uses the normalized walking waveform data to calculate gait indices used to estimate physical ability. For example, the calculation unit 23 calculates gait indices related to distance, height, angle, speed, time, frailty level, CPEI (Center of Pressure Exclusion Index), etc.

The storage unit 224 (storage means) has the same configuration as the storage unit 124 of the first embodiment. The storage unit 224 stores a physical ability estimation model that estimates physical ability using physical ability features extracted from the walking waveform data. For example, the physical ability estimation model outputs an index related to physical ability (physical ability score) in response to the input of the physical ability features extracted from the walking waveform data. The storage unit 224 also stores a disease risk estimation model that estimates disease risk using attribute data, gait index, and physical ability score. For example, the disease risk estimation model outputs an index related to disease risk (disease risk score) in response to the input of attribute data, gait index, and physical ability score. Usually, the address (location information) of the subject's residence included in the attribute data is not used to estimate physical ability or disease risk. For example, the disease risk estimation model may be a model that outputs a disease risk score in response to the input of the gait index and attribute data without using the physical ability score. In that case, the physical ability estimation model does not need to be used.

The storage unit 224 also stores a policy estimation model that outputs policies related to a target area in response to input of a risk map generated by the map generation unit 227. For example, a policy is information in which keywords related to the policy are linked to locations included in the target area. For example, a policy proposal is information in which keywords related to a policy are linked to facilities included in the target area. For example, the policy estimation model may include a large-scale language model that outputs sentences including policies in response to input of a risk map.

The storage unit 224 stores the physical ability estimation model, disease risk estimation model, and measure estimation model learned for multiple subjects. For example, the physical ability estimation model, disease risk estimation model, and measure estimation model may be stored in the storage unit 224 when the product is shipped from the factory. The physical ability estimation model, disease risk estimation model, and measure estimation model may be stored in the storage unit 224 at a timing such as at the time of calibration before the subject uses the information generating device 22. For example, the physical ability estimation model, disease risk estimation model, and measure estimation model stored in a storage device (not shown) such as an external server may be used. In that case, it is sufficient that the physical ability estimation model, disease risk estimation model, and measure estimation model can be accessed via an interface (not shown) connected to the storage device.

The memory unit 224 also stores the attributes of the subject. The attribute data includes gender, date of birth (age), height, and weight. The attribute data also includes the subject's residential address (location information). Typically, the subject's residential address (location information) is not used to estimate physical ability or disease risk. The attribute data may be updated at any time.

Furthermore, the storage unit 224 stores a map of the target area for which a risk map is to be generated. The map of the target area may be stored in the storage unit 224 in advance. For example, the map of the target area may not be stored in the storage unit 224, but may be acquired from an external database by the acquisition unit 221.

The estimation unit 24 (estimation means) has the same configuration as the estimation unit 14 of the first embodiment. The estimation unit 24 includes the functions of the physical ability estimation unit 125 and the disease risk estimation unit 126 of the first embodiment. The estimation unit 24 acquires the physical ability feature extracted from the walking waveform data from the calculation unit 23. The estimation unit 24 also acquires the attributes stored in the memory unit 224. The estimation unit 24 estimates a physical ability score using the physical ability feature and the attributes. The estimation unit 24 inputs the physical ability feature and the attributes of the subject to a physical ability estimation model stored in the memory unit 224. For example, the estimation unit 24 estimates a physical ability score related to at least one of the physical abilities of grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. The estimation unit 24 estimates a disease risk score for each disease using the physical ability score, gait index, and attributes. The estimation unit 24 inputs the physical ability score, gait index, and attributes into a disease risk model to estimate a disease risk score. The estimation unit 24 outputs the estimated disease risk score.

The map generating unit 227 has the same configuration as the map generating unit 127 of the first embodiment. The map generating unit 227 acquires, from the storage unit 224, the disease risk of the disease for which the risk map is to be generated, for the subject associated with the target district. The map generating unit 227 also acquires a map of the target district.

The map generation unit 227 sets the display conditions for an image showing the distribution of the acquired disease risk. The map generation unit 227 generates an image (heat map) in which the distribution of disease risk is set in a display state, such as color coding or shading, in association with the positions of areas, residences, etc. included in the target district. The map generation unit 227 sets the display state of an indicator according to the degree of disease risk in association with the positions of areas, residences, etc. included in the target district.

The map generating unit 227 sets the display state of an indicator indicating the degree of disease risk in association with an area included in the target district. The map generating unit 227 may set the display state of an indicator indicating the degree of disease risk in association with a residence included in the target district. The map generating unit 227 may set the display state of an indicator indicating the degree of disease risk in association with the location of a subject staying in the target district. The map generating unit 227 generates a risk map by superimposing the generated image (heat map) on a map of the target district according to the set display conditions.

The policy proposal generation unit 228 acquires a risk map for the target area. The policy proposal generation unit 228 inputs the acquired risk map into the policy estimation model. The policy proposal generation unit 228 uses the policy output from the policy estimation model in response to the input of the risk map to generate risk information including policy proposals related to the policy.

24 is a conceptual diagram showing an example of estimating a policy using the policy estimation model 260. The policy proposal generation unit 228 inputs a risk map of the target area to the policy estimation model 260. The risk map of the target area is input to the policy estimation model 260. In response to the input of the risk map of the target area, the policy estimation model 260 outputs a policy for the target area. In the example of FIG. 24, multiple policies (Policy 1, Policy 2, ..., Policy N) are estimated for the target area (N is a natural number). In addition to the risk map of the target area, a map of the target area may be input to the policy estimation model 260. In that case, the policy estimation model 260 can estimate a policy after extracting the degree of disease risk corresponding to the indicator according to the difference between the risk map of the target area and the map.

For example, the policy proposal generation unit 228 generates risk information including policy proposals related to a target area for the local government that manages the target area. For example, the policy proposal generation unit 228 generates policy proposals by applying measures to a preset document format. For example, the policy proposal generation unit 228 may generate policy proposals using a large-scale language model. Upon acquiring risk information including policy proposals, the local government can take action according to the policy proposals. In other words, the policy proposal generation unit 228 generates risk information that supports the decision-making of the local government.

The output unit 229 (output means) has the same configuration as the output unit 129 of the first embodiment. The output unit 229 outputs risk information including the policy proposal generated by the policy proposal generation unit 228. The output unit 229 may also output risk information including a risk map. For example, the output unit 229 outputs risk information including the policy proposal to an external system that uses the policy proposal. For example, the output unit 229 outputs risk information including the policy proposal to a terminal device (not shown) used by the local government. There are no particular limitations on the use of the output risk information including the policy proposal. For example, the risk information including the policy proposal is used to consider policies to be implemented by the local government for the target area.

(Operation)
Next, the operation of the information providing system 2 will be described with reference to the drawings. The operation of the information generating device 22 included in the information providing system 2 will be described below. Fig. 25 is a flowchart for explaining an example of the operation of the information generating device 22. In explaining the process according to the flowchart of Fig. 25, the components of the information generating device 22 will be described as the subject of the operation. The subject of the process according to the flowchart of Fig. 25 may be the information generating device 22.

In FIG. 25, first, the acquisition unit 221 acquires time series data of sensor data measured by the measurement device 20 mounted on the footwear (step S21). The sensor data includes acceleration in three axial directions and angular velocity around three axes.

Next, the calculation unit 23 executes a gait index calculation process using the acquired sensor data (step S22). In the gait index calculation process, the calculation unit 23 calculates the gait index used to estimate physical ability. The gait index calculation process in step S22 is similar to the gait index calculation process in the first embodiment ( FIG. 18 ).

Next, the estimation unit 24 estimates physical ability using the attribute data and gait index (step S23). For example, the estimation unit 24 estimates physical ability scores such as grip strength (total muscle strength of the entire body), dynamic balance, lower limb muscle strength, mobility, and static balance. If disease risk is estimated without using physical ability, step S23 can be omitted.

Next, the estimation unit 24 estimates the disease risk for each disease using the attribute data, gait index, and physical ability (step S24). When the disease risk is estimated without using physical ability, the estimation unit 24 estimates the disease risk for each disease using the attribute data and gait index. The estimation unit 24 estimates a disease risk score for each disease. For example, the estimation unit 24 estimates a disease risk score for each disease, such as gout, diabetes, hypertension, nephrolithiasis, liver cirrhosis, arteriosclerosis, thromboembolism, dyslipidemia, hypercholesterolemia, and hyperlipidemia. For example, the estimation unit 24 estimates a disease risk score for each disease, such as lower back pain, sleep apnea syndrome, insomnia, depression, osteoarthritis of the knee, and Parkinson's syndrome.

Next, the memory unit 224 accumulates the estimated disease risk (step S25). The disease risk accumulated in the memory unit 224 is used to generate a risk map.

Next, the map generation unit 227 executes a risk map generation process using the disease risks stored in the memory unit 224 (step S26). In the risk map generation process, the map generation unit 227 generates a risk map of the target area. The risk map generation process of step S26 is similar to the risk map generation process of the first embodiment (Figure 19).

Next, the policy proposal generation unit 228 generates a policy proposal according to the risk map (step S27). The policy proposal generation unit 228 uses the policy output from the policy estimation model in response to the input of the risk map to generate risk information including a policy proposal related to that policy.

Next, the output unit 229 outputs the risk information including the policy proposal (step S28). For example, the output unit 229 outputs the risk information to a terminal device or server managed by the town office of the target district. For example, the output unit 229 outputs the risk information to an external system that uses the risk information. For example, the output unit 229 may display the risk information on the screen of the target person's mobile terminal.

(Application example)
Next, an application example according to the present embodiment will be described with reference to the drawings. FIG. 26 is an example in which a policy proposal generated by the information generating device 22 is displayed on the screen of a terminal device 280 used by a local government. Risk information including proposal information generated for a target district managed by a local government is optimized for each local government and displayed on the screen of the terminal device 280. In the example of FIG. 26, multiple policy proposals are displayed on the screen. The first policy proposal is a proposal that "We propose to install an overpass on the railway between the hospital and an area S where there are many residents with a high risk of knee joint osteoarthritis." The second policy proposal is a proposal that "We propose to open a day care facility in an area T where there are many residents with a high risk of frailty." The third policy proposal is a proposal that "We propose to attract a sports gym to an area U where there are many residents with a high risk of diabetes." An employee who has confirmed the risk information including the policy proposal displayed on the screen of the terminal device 280 can consider a policy for the target district.

The policy proposals generated by the information generating device 22 are not limited to the above examples, so long as they include policies related to the target district. For example, the policy proposals include policies related to the opening and relocation of medical facilities such as new clinics and pharmacies. For example, the policy proposals include policies related to holding healthcare events and seminars that will lead to a reduction in the health risks of residents living in the target district. For example, the policy proposals include policies related to optimizing the placement of medical institutions. For example, the policy proposals include city design policies related to the number and geographical placement of commercial facilities, the installation of fitness equipment in parks, and the like. For example, the policy proposals include policies related to improving public facilities such as buses and taxis to improve access to medical institutions. For example, the policy proposals include policies related to the opening and widening of roads.

As described above, the information provision system of this embodiment includes a measuring device and an information generating device. The measuring device is installed on the footwear of at least one of the subjects. The measuring device measures spatial acceleration and spatial angular velocity. The measuring device generates sensor data using the measured spatial acceleration and spatial angular velocity. The measuring device transmits the generated sensor data to the information generating device. The information generating device includes an acquisition unit, a risk estimation unit, a map generating unit, a policy proposal generating unit, and an output unit. The acquisition unit acquires sensor data measured by a measuring device mounted on the footwear of at least one of the subjects. The risk estimation unit estimates a disease risk for each disease for at least one of the subjects using the acquired sensor data. The map generating unit generates a risk map in which an indication corresponding to the disease risk of the target disease for at least one of the subjects is superimposed on a map of the target area. The policy proposal generating unit generates policy proposals corresponding to the risk map of the target area using a policy estimation model that outputs policies for the target area in response to an input of the risk map. The output unit outputs risk information including the generated policy proposals.

The information generating device of this embodiment estimates the disease risk of a target disease using sensor data measured by a measuring device mounted on the subject's footwear. The information generating device of this embodiment generates a risk map in which an indication corresponding to the estimated disease risk of the target disease is superimposed on a map of the target area. The information generating device of this embodiment generates policy proposals for the target area using the generated risk map. Therefore, according to this embodiment, it is possible to generate policy proposals that reflect the disease risk of at least one subject located in the target area.

Third Embodiment
Next, an information generating device according to a third embodiment will be described with reference to the drawings. The information generating device according to this embodiment has a simplified configuration of the information generating device included in the information providing system according to the first and second embodiments.

(composition)
27 is a block diagram showing an example of a configuration of the information generating device 30 in the present disclosure. The information generating device 30 includes an acquiring unit 31, a risk estimating unit 35, a map generating unit 37, and an output unit 39.

The acquisition unit 31 acquires sensor data measured by a measuring device mounted on the footwear of at least one subject. The risk estimation unit 35 uses the acquired sensor data to estimate a disease risk for each disease for at least one subject. The map generation unit 37 generates a risk map in which an indication corresponding to the disease risk of a target disease for at least one subject is superimposed on a map of a target area. The output unit 39 outputs risk information including the generated risk map.

(Operation)
Next, the operation of the information generating device 30 will be described with reference to the drawings. Fig. 28 is a flowchart for explaining an example of the operation of the information generating device 30. In the explanation of the process according to the flowchart of Fig. 28, the components of the information generating device 22 will be described as the subject of the operation. The subject of the process according to the flowchart of Fig. 28 may be the information generating device 30.

In FIG. 28, first, the acquisition unit 31 acquires sensor data measured by a measuring device mounted on the footwear of at least one subject (step S31).

Next, the risk estimation unit 35 uses the acquired sensor data to estimate the disease risk for each disease for at least one subject (step S32).

Next, the map generation unit 37 generates a risk map in which an indication according to the disease risk of the target disease for at least one subject is superimposed on a map of the target area (step S33).

Next, the output unit 39 outputs risk information including the generated risk map (step S34).

As described above, the information generating device of this embodiment estimates the disease risk of a target disease using sensor data measured by a measuring device mounted on the subject's footwear. The information generating device of this embodiment generates a risk map in which a display according to the estimated disease risk of the target disease is superimposed on a map of the target area. Therefore, according to this embodiment, it is possible to generate a risk map in which the locations of people at risk of contracting the target disease are visualized.

(Hardware)
Next, a hardware configuration for executing control and processing according to each embodiment of the present disclosure will be described with reference to the drawings. Here, an information processing device 90 (computer) in Fig. 29 is given as an example of such a hardware configuration. The information processing device 90 in Fig. 29 is an example of a configuration for executing the control and processing according to each embodiment, and does not limit the scope of the present disclosure.

As shown in FIG. 29, the information processing device 90 includes a processor 91, a main memory device 92, an auxiliary memory device 93, an input/output interface 95, and a communication interface 96. In FIG. 29, the interface is abbreviated as I/F (Interface). The processor 91, the main memory device 92, the auxiliary memory device 93, the input/output interface 95, and the communication interface 96 are connected to each other via a bus 98 so as to be able to communicate data with each other. In addition, the processor 91, the main memory device 92, the auxiliary memory device 93, and the input/output interface 95 are connected to a network such as the Internet or an intranet via the communication interface 96.

The processor 91 expands a program (instructions) stored in the auxiliary storage device 93 or the like into the main storage device 92. For example, the program is a software program for executing the control and processing of each embodiment. The processor 91 executes the program expanded into the main storage device 92. The processor 91 executes the program to execute the control and processing of each embodiment.

The main memory 92 has an area in which programs are expanded. Programs stored in the auxiliary memory 93 or the like are expanded in the main memory 92 by the processor 91. The main memory 92 is realized by a volatile memory such as a DRAM (Dynamic Random Access Memory). In addition, a non-volatile memory such as an MRAM (Magneto-resistive Random Access Memory) may be configured/added to the main memory 92.

The auxiliary storage device 93 stores various data such as programs. The auxiliary storage device 93 is realized by a local disk such as a hard disk or flash memory. Note that it is also possible to omit the auxiliary storage device 93 by configuring the various data to be stored in the main storage device 92.

The input/output interface 95 is an interface for connecting the information processing device 90 to peripheral devices based on standards and specifications. The communication interface 96 is an interface for connecting to external systems and devices via a network such as the Internet or an intranet based on standards and specifications. The input/output interface 95 and the communication interface 96 may be a common interface for connecting to external devices.

If necessary, input devices such as a keyboard, mouse, or touch panel may be connected to the information processing device 90. These input devices are used to input information and settings. When a touch panel is used as the input device, a screen having the function of a touch panel becomes the interface. The processor 91 and the input devices are connected via an input/output interface 95.

The information processing device 90 may be equipped with a display device for displaying information. If a display device is equipped, the information processing device 90 is equipped with a display control device (not shown) for controlling the display of the display device. The information processing device 90 and the display device are connected via an input/output interface 95.

The information processing device 90 may be equipped with a drive device. The drive device acts as an intermediary between the processor 91 and a recording medium (program recording medium) to read data and programs stored on the recording medium and to write the processing results of the information processing device 90 to the recording medium. The information processing device 90 and the drive device are connected via an input/output interface 95.

The above is an example of a hardware configuration for enabling the control and processing in this disclosure. The hardware configuration in FIG. 29 is an example of a hardware configuration for executing the control and processing according to each embodiment, and does not limit the scope of this disclosure. Programs that cause a computer to execute the control and processing according to each embodiment are also included in the scope of this disclosure.

Program recording media on which the programs according to each embodiment are recorded are also included within the scope of this disclosure. The recording media can be realized, for example, as optical recording media such as CDs (Compact Discs) and DVDs (Digital Versatile Discs). The recording media may also be realized as semiconductor recording media such as USB (Universal Serial Bus) memories and SD (Secure Digital) cards. The recording media may also be realized as magnetic recording media such as flexible disks, or other recording media. When the program executed by the processor is recorded on a recording medium, the recording medium corresponds to a program recording medium.

The components of each embodiment may be combined in any manner. The components of each embodiment may be realized by software. The components of each embodiment may be realized by circuitry.

The present disclosure has been described above with reference to the embodiments, but the present disclosure is not limited to the above-mentioned embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

A part or all of the above-described embodiments can be described as, but is not limited to, the following supplementary notes.
(Appendix 1)
An acquisition unit that acquires sensor data measured by a measurement device mounted on footwear of at least one subject;
A risk estimation unit that estimates a disease risk for each disease for at least one of the subjects using the acquired sensor data;
A map generating unit that generates a risk map in which an indication according to a disease risk of a target disease for at least one of the subjects is superimposed on a map of a target district;
An information generating device comprising: an output unit that outputs risk information including the generated risk map.
(Appendix 2)
The risk estimation unit is
a calculation unit that calculates a gait index using the sensor data;
an estimation unit that inputs data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating the degree of disease risk for each disease in response to input of data including the gait index, and estimates disease risk information corresponding to the disease risk score output from the disease risk estimation model.
(Appendix 3)
The map generation unit is
determining a location of at least one of said subjects;
Calculating a distribution of disease risk of the target disease for each area included in the target district according to the location of at least one of the identified subjects;
setting display conditions for an indicator showing the degree of disease risk of the target disease for each of the areas included in the target district;
3. The information generating device according to claim 2, wherein the indicator showing the degree of disease risk of the target disease is superimposed on the map of the target area in accordance with the set display conditions.
(Appendix 4)
The map generation unit is
setting the display conditions of the indicators indicating the degree of disease risk of the plurality of target diseases for each of the areas included in the target district;
4. The information generating device according to claim 3, wherein the indicator showing the degree of disease risk for the plurality of target diseases is superimposed on the map of the target area in accordance with the set display conditions.
(Appendix 5)
The map generation unit is
calculating a statistic of the disease risk score for at least one of the subjects associated with a location within the area included in the geographic area;
4. The information generating device according to claim 3, wherein the display condition of the indicator is set in the area according to a statistical value of the calculated disease risk score.
(Appendix 6)
The map generation unit is
4. An information generating device as described in claim 3, which identifies the location of the subject based on the location of the subject's residence.
(Appendix 7)
The map generation unit is
4. An information generating device as described in claim 3, which identifies the location of the subject based on location information of a mobile device carried by the subject.
(Appendix 8)
The map generation unit is
Identifying the location of the subject based on location information of a mobile device carried by the subject;
4. The information generating device of claim 3, wherein the risk map is updated in response to a change in the position of the subject.
(Appendix 9)
a policy proposal generation unit that generates policy proposals according to the risk map of the target district by using a policy estimation model that outputs policies for the target district in response to an input of the risk map;
The output unit is
An information generating device according to claim 3, which outputs risk information including the generated policy proposal.
(Appendix 10)
the policy estimation model and the disease risk estimation model are models trained using a machine learning technique,
The disease risk estimation model is
10. The information generating apparatus of claim 9, comprising an incomplete heterogeneous variational autoencoder.
(Appendix 11)
An information generating device according to any one of Supplementary Notes 1 to 10;
An information provision system comprising: a measuring device that is installed in the footwear of at least one of the subjects, measures spatial acceleration and spatial angular velocity, generates the sensor data using the measured spatial acceleration and spatial angular velocity, and transmits the generated sensor data to the information generation device.
(Appendix 12)
The information generating device includes:
An information provision system as described in Appendix 11, which displays the risk information optimized for the target area on the screen of a terminal device used by a local government that manages the target area.
(Appendix 13)
The computer
Acquire sensor data measured by a measurement device mounted on the footwear of at least one subject;
Using the acquired sensor data, estimate a disease risk for each disease for at least one of the subjects;
generating a risk map in which an indication according to a disease risk of a target disease for at least one of the subjects is superimposed on a map of a target district;
An information generating method for outputting risk information including the generated risk map.
(Appendix 14)
The computer,
Calculating a gait index using the sensor data;
inputting data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating a degree of disease risk for each disease in response to input of data including the gait index;
An information generating method described in Appendix 13, which estimates disease risk information according to the disease risk score output from the disease risk estimation model.
(Appendix 15)
The computer,
determining a location of at least one of said subjects;
Calculating a distribution of disease risk of the target disease for each area included in the target district according to the location of at least one of the identified subjects;
setting display conditions for an indicator showing the degree of disease risk of the target disease for each of the areas included in the target district;
An information generating method as described in Appendix 14, in which the indicator showing the degree of disease risk of the target disease is superimposed on the map of the target area in accordance with the set display conditions.
(Appendix 16)
The computer,
setting display conditions for indicators showing the degree of disease risk of the plurality of target diseases for each of the areas included in the target district;
An information generating method as described in Appendix 15, in which the indicators showing the degree of disease risk for multiple target diseases are superimposed on the map of the target area in accordance with the set display conditions.
(Appendix 17)
The computer,
Calculating a disease risk score statistic indicative of the disease risk for at least one of the subjects associated with a location within the area included in the target district;
An information generating method according to claim 15, wherein the display conditions of the indicator are set in the area according to the statistical value of the calculated disease risk score.
(Appendix 18)
The computer,
16. An information generation method according to claim 15, in which the location of the subject is identified based on the location of the subject's residence.
(Appendix 19)
The computer,
An information generating method according to claim 15, which identifies the location of the subject based on location information of a mobile device carried by the subject.
(Appendix 20)
The computer,
Identifying the location of the subject based on location information of a mobile device carried by the subject;
16. The information generation method of claim 15, wherein the risk map is updated in response to changes in the position of the subject.
(Appendix 21)
The computer,
generating a policy proposal corresponding to the risk map of the target district using a policy estimation model that outputs a policy for the target district in response to an input of the risk map;
An information generation method as described in Appendix 15, which outputs risk information including the generated policy proposal.
(Appendix 22)
the policy estimation model and the disease risk estimation model are models trained using a machine learning technique,
The disease risk estimation model is
22. The information generation method of claim 21 including an incomplete heterogeneous variational autoencoder.
(Appendix 23)
A process of acquiring sensor data measured by a measurement device mounted on the footwear of at least one subject;
A process of estimating a disease risk for each disease for at least one of the subjects using the acquired sensor data;
A process of generating a risk map in which an indication according to a disease risk of a target disease for at least one of the subjects is superimposed on a map of a target district;
A non-transitory computer-readable recording medium having recorded thereon a program for causing a computer to execute the process of outputting risk information including the generated risk map.
(Appendix 24)
A process of calculating a gait index using the sensor data;
A process of inputting data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating a degree of disease risk for each disease in response to input of data including the gait index;
A non-transitory computer-readable recording medium described in Appendix 23, having a program recorded thereon to cause a computer to execute a process of estimating disease risk information according to the disease risk score output from the disease risk estimation model.
(Appendix 25)
determining a location of at least one of the subjects;
A process of calculating a distribution of disease risk of the target disease for each area included in the target district according to the location of at least one of the identified subjects;
A process of setting a display condition of an indicator showing a degree of disease risk of the target disease for each of the areas included in the target district;
A non-transitory computer-readable recording medium as described in Appendix 24, having recorded thereon a program that causes a computer to execute the following process: superimposing the indicator indicating the degree of disease risk of the target disease on the map of the target area in accordance with the set display conditions.
(Appendix 26)
A process of setting display conditions for indicators showing the degree of disease risk of the plurality of target diseases for each of the areas included in the target district;
A non-transitory computer-readable recording medium as described in Appendix 25, having recorded thereon a program that causes a computer to execute the following process: superimposing the indicators indicating the degree of disease risk of the multiple target diseases on the map of the target area in accordance with the set display conditions.
(Appendix 27)
A process of calculating a disease risk score statistic indicating the disease risk for at least one of the subjects associated with a location within the area included in the target district;
A non-transitory computer-readable recording medium as described in Appendix 25, having recorded thereon a program for causing a computer to execute the following process: setting the display conditions of the indicator in the area according to the statistical value of the calculated disease risk score.
(Appendix 28)
A non-transitory computer-readable recording medium as described in Appendix 25, having a program recorded thereon to cause a computer to execute a process of identifying the location of the subject based on the location of the subject's residence.
(Appendix 29)
A non-transitory computer-readable recording medium as described in Appendix 15, having a program recorded thereon to cause a computer to execute a process of identifying the location of the subject based on location information of a mobile device carried by the subject.
(Appendix 30)
A process of identifying a location of the subject based on location information of a mobile device carried by the subject;
A non-transitory computer-readable recording medium according to claim 25, having a program recorded thereon to cause a computer to execute a process of updating the risk map in response to changes in the position of the subject.
(Appendix 31)
A process of generating a policy proposal corresponding to the risk map of the target district using a policy estimation model that outputs a policy for the target district in response to an input of the risk map;
A non-transitory computer-readable recording medium as described in Appendix 25, having recorded thereon a program for causing a computer to execute the process of outputting risk information including the generated policy proposal.
(Appendix 32)
the policy estimation model and the disease risk estimation model are models trained using a machine learning technique,
The disease risk estimation model is
32. The non-transitory computer-readable storage medium of claim 31 comprising an incomplete heterogeneous variational autoencoder.

1, 2 Information provision system 10, 20 Measurement device 12, 22 Information generation device 13, 23 Calculation unit 14, 24 Estimation unit 15, 25 Risk estimation unit 30 Information generation device 31 Acquisition unit 35 Risk estimation unit 37 Map generation unit 39 Output unit 110 Sensor 111 Acceleration sensor 112 Angular velocity sensor 113 Control unit 115 Communication unit 117 Power supply 121, 221 Acquisition unit 122 Waveform processing unit 123 Gait index calculation unit 124, 224 Memory unit 125 Physical ability estimation unit 126 Disease risk estimation unit 127, 227 Map generation unit 129, 229 Output unit 228 Policy proposal generation unit

Claims (20)

An acquisition unit that acquires sensor data measured by a measurement device mounted on footwear of at least one subject;
A risk estimation unit that estimates a disease risk for each disease for at least one of the subjects using the acquired sensor data;
A map generating unit that generates a risk map in which an indication according to a disease risk of a target disease for at least one of the subjects is superimposed on a map of a target district;
An information generating device comprising: an output unit that outputs risk information including the generated risk map. The risk estimation unit
a calculation unit that calculates a gait index using the sensor data;
2. The information generating device according to claim 1, further comprising: an estimation unit that inputs data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating the degree of disease risk for each disease in response to input of data including the gait index, and estimates disease risk information according to the disease risk score output from the disease risk estimation model. The map generation unit is
determining a location of at least one of said subjects;
Calculating a distribution of disease risk of the target disease for each area included in the target district according to the location of at least one of the identified subjects;
setting display conditions for an indicator showing the degree of disease risk of the target disease for each of the areas included in the target district;
The information generating device according to claim 2 , wherein the indicator showing the degree of disease risk of the target disease is superimposed on the map of the target area in accordance with the set display conditions. The map generation unit is
setting the display conditions of the indicators indicating the degree of disease risk of the plurality of target diseases for each of the areas included in the target district;
The information generating device according to claim 3 , wherein the indicators showing the degree of disease risk for the plurality of target diseases are superimposed on the map of the target area in accordance with the set display conditions. The map generation unit is
calculating a statistic of the disease risk score for at least one of the subjects associated with a location within the area included in the geographic area;
The information generating device according to claim 3 , wherein the display condition of the indicator is set in the area according to a statistical amount of the calculated disease risk score. The map generation unit is
The information generating device according to claim 3 , wherein the position of the subject is identified based on a location of the subject's residence. The map generation unit is
The information generating device according to claim 3 , wherein the position of the subject is identified based on position information of a portable terminal carried by the subject. The map generation unit is
Identifying the location of the subject based on location information of a mobile device carried by the subject;
The information generating device according to claim 3 , wherein the risk map is updated in response to a change in the position of the subject. a policy proposal generation unit that generates policy proposals according to the risk map of the target district by using a policy estimation model that outputs policies for the target district in response to an input of the risk map;
The output unit is
The information generating device according to claim 3 , further comprising: an information generating device that outputs risk information including the generated policy proposal. the policy estimation model and the disease risk estimation model are models trained using a machine learning technique,
The disease risk estimation model is
10. The information generating apparatus of claim 9, comprising an incomplete heterogeneous variational autoencoder. An information generating device according to any one of claims 1 to 10;
An information provision system comprising: a measuring device that is installed in the footwear of at least one of the subjects, measures spatial acceleration and spatial angular velocity, generates the sensor data using the measured spatial acceleration and spatial angular velocity, and transmits the generated sensor data to the information generation device. The information generating device includes:
The information providing system according to claim 11, wherein the risk information optimized for the target area is displayed on a screen of a terminal device used by a local government that manages the target area. The computer
Acquire sensor data measured by a measurement device mounted on the footwear of at least one subject;
Using the acquired sensor data, estimate a disease risk for each disease for at least one of the subjects;
generating a risk map in which an indication according to a disease risk of a target disease for at least one of the subjects is superimposed on a map of a target district;
An information generating method for outputting risk information including the generated risk map. The computer,
Calculating a gait index using the sensor data;
inputting data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating a degree of disease risk for each disease in response to input of data including the gait index;
The information generating method according to claim 13 , further comprising estimating disease risk information according to the disease risk score output from the disease risk estimation model. The computer,
determining a location of at least one of said subjects;
Calculating a distribution of disease risk of the target disease for each area included in the target district according to the location of at least one of the identified subjects;
setting display conditions for an indicator showing the degree of disease risk of the target disease for each of the areas included in the target district;
The information generating method according to claim 14 , further comprising the step of superimposing the indicator showing the degree of disease risk of the target disease on the map of the target area in accordance with the set display conditions. The computer,
generating policy proposals corresponding to the risk map of the target district using a policy estimation model that outputs policies for the target district in response to an input of the risk map;
The information generating method according to claim 15, further comprising the step of outputting risk information including the generated policy proposal. A process of acquiring sensor data measured by a measurement device mounted on the footwear of at least one subject;
A process of estimating a disease risk for each disease for at least one of the subjects using the acquired sensor data;
A process of generating a risk map in which an indication according to a disease risk of a target disease for at least one of the subjects is superimposed on a map of a target district;
A non-transitory computer-readable recording medium having recorded thereon a program for causing a computer to execute the process of outputting risk information including the generated risk map. A process of calculating a gait index using the sensor data;
A process of inputting data including the gait index calculated using the sensor data into a disease risk estimation model that outputs a disease risk score indicating a degree of disease risk for each disease in response to input of data including the gait index;
A non-transitory computer-readable recording medium as described in claim 17, having a program recorded thereon to cause a computer to execute the process of estimating disease risk information according to the disease risk score output from the disease risk estimation model. determining a location of at least one of the subjects;
A process of calculating a distribution of disease risk of the target disease for each area included in the target district according to the location of at least one of the identified subjects;
A process of setting a display condition of an indicator showing a degree of disease risk of the target disease for each of the areas included in the target district;
A non-transitory computer-readable recording medium as described in claim 18, having recorded thereon a program that causes a computer to execute the following process: superimposing the indicator indicating the degree of disease risk of the target disease on the map of the target area in accordance with the set display conditions. A process of generating a policy proposal corresponding to the risk map of the target district using a policy estimation model that outputs a policy for the target district in response to an input of the risk map;
20. The non-transitory computer-readable recording medium according to claim 19, having recorded thereon a program for causing a computer to execute the steps of:

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