AT527469A1 - Method and system for monitoring a person in a room - Google Patents

Abstract

The invention relates to a method for monitoring a person in a room, wherein in a first step (1) a model of the room is created, in a second step (2) a system of sensors is arranged in the room, in a third step (3) the movements of the person in the room are recorded by the sensor system, in a fourth step (4) the recorded movements of the person are classified into at least a first class and a second class using a movement algorithm and in a fifth step (5) the movements assigned to a first class are categorized.

Description

Monitoring a person in a room.

As the world population ages, Alzheimer's disease is becoming more common. Because Alzheimer's disease causes cognitive decline, people who suffer from deterioration in memory, speech and other thinking skills due to the death of nerve cells in the brain require constant care for the rest of their lives. An appropriate therapeutic environment can help make decisions about how to carry out their daily activities and help residents

to help them cope with their everyday lives.

However, applying the same therapeutic environment design to different people with Alzheimer's disease (PWADs) is insufficient, as the health status and associated problems are different for each PWAD. Each resident has a different identity, background, habits and symptoms and therefore needs a specifically adapted environment to be able to navigate it well. For this reason, it is necessary to find a way to adapt new or existing environments to the specific needs of an individual PWAD.

to adapt.

In addition, it is necessary to monitor PWADs as closely as possible to ensure that they receive the best possible care. However, for ethical reasons, it is not

possible to use conventional surveillance cameras, since

personal data cannot be expected of anyone.

Systems are known from the state of the art in which infrared sensors are used to monitor Alzheimer's patients. The data obtained by the infrared sensors is then divided into movement categories

and issued.

The disadvantage here is that the evaluation of the recorded movements is essentially left to the user. Furthermore, with conventional methods it is difficult to

to distinguish individual people from one another in a room.

It is therefore an object of the invention to provide a method and a system for monitoring a person in a room, which enables continuous monitoring, whereby the specific situation

the monitored person can be recognized.

According to the invention, a method for monitoring a person in a room is provided, wherein in a first step a model of the room is created, in a second step a system of sensors is arranged in the room, in a third step the movements of the person in the room are detected by the sensor system, in a fourth step the detected movements of the person are classified by means of a movement algorithm into at least a first class and a

second class and in a fifth step

categorization algorithm.

In general, the various steps of the invention are carried out one after the other, e.g. the second step is carried out after the first step, the third step is carried out after the second step, the fourth step is carried out after the third step and the fifth step is carried out after the fourth step. However, some steps may overlap, e.g. the third step and the fourth step. In this case, a classification is already carried out, while the sensor system continues to collect movement data.

be recorded.

The method according to the invention uses sensors, e.g. in shadow observation, to continuously collect data on the person's movements in a room in order to be able to react quickly if the person has lost their way, for example. On the other hand, the movement data can be used to identify the person's self-disorientation points in their room and to determine the architectural barriers that form obstacles to the independent performance of their daily activities without the help of the carers. This supports the person's ability to orientate themselves when completing their daily tasks, e.g. in their room in a long-term health center, and consequently improves their quality of life. Nevertheless, the present invention can also be used in other contexts, e.g. in elderly care, other areas of the health care system or to optimize

company buildings or schools.

health center.

The various steps are preferably carried out with the aid of a computer and are therefore preferably

computer-implemented.

The movements recorded by the sensor system are assigned, for example, the attributes “object”, “time”, “space” and “duration”, which determine the exact

describe movement, assigned.

In the first step, a model of the room can be created using a Building Information Modeling (BIM) system. One of the following three methods can be used for this:

(1) Laser scanning or photogrammetry, best known as “scan-to-BIM” for highly detailed models. For most everyday facility management operations, the details provided by these models are not required;

(2) Automated conversion of 2D plans to 3D BIM; the aim is to enable smoother and more flexible workflows that do not require the manual re-creation of BIM models;

and (3) Translation of 3D geometry models into BIM: Translation of the 3D models created in a suitable software

Models into a BIM software for BIM modeling.

model.

In the second step of the present invention, before arranging the sensors, a step is preferably carried out to find the optimal placement of the sensors in the room. The room is first analyzed and, based on this, an appropriate selection and arrangement of sensors is suggested. For this purpose, an appropriate algorithm can be used, for example. The placement of the sensors can be an essential factor in monitoring the movement and activity of PWAD in order to avoid the collection of misleading information. A preferred method that should be carried out in the second step before arranging the sensors in the room is the following, which enables the user to have an optimal solution displayed on a screen after a few analysis steps. These analysis features are preferably

(1) The size of the room;

(2) the sensor functions;

(3) the neighboring zones that must be covered by two different sensor combinations to distinguish the target location according to the sensor activation; and

(4) the determination of a sensor number for each zone, which is

field of view of the sensor and the zone size. After this analysis, the sensors of the

Sensor system optimally arranged according to the analysis

to record the person's movements in the room.

, the recorded movement is classified as “done”.

Movements classified as “completed”, on the other hand, do not need to be further categorized, but are, for example, classified together with the attributes “object”, “time”, “space” and “duration”, which

describe exact movement, output.

In the fifth step, the person's movements classified in a first class, e.g. as "not completed", are classified into different categories in order to enable a quick and processed output of the recorded movements. For example, it is possible to determine human behavior at different levels of granularity.

based on, for example, the distance travelled and the

Water every morning after breakfast.

In addition, the behavior of PWADs is characterized by recurrent incoherences in their actions, especially when they perform complex activities that require cognitive skills. For example, the incoherence behavior of PWADs can be classified into six error categories: (1) initiation (failure to start a task); (2) organization (performing some steps of an activity inappropriately); (3) realization (skipping the main task and completing another one); (4) sequencing (disorganization of the steps of the

activity); (5) judgment (performs a task

“Conclusion”.

The movement algorithm and/or the categorization algorithm can, for example, be generated using person-specific data. The movement algorithm or the categorization algorithm is designed individually and differently for each person observed. The person-specific activity patterns, which are divided into “action,” “activity,” and “habit,” are used as the basis for the movement algorithm or the categorization algorithm. A person’s person-specific activity patterns are preferably used in the respective movement algorithm in order to be able to classify the movements into one of two classes. A person’s person-specific activity patterns are preferably used in the respective categorization algorithm in order to be able to classify the movements into one of six error categories. The plan here is to first collect a person’s movement data, e.g. using a sensor system, and then divide this movement data into categories, e.g. “action,” “activity,” and “habit.” These classified movement data are then

used to control the movement algorithm and/or the

in order to detect deviations and thus errors.

To perform classification in the fourth step and categorization in the fifth step, machine learning approaches and algorithms can be used. Such approaches are usually more compatible with the four main phases of activity (object, time, space, and duration) and anomaly detection than classical mathematics and statistical techniques in extracting knowledge and discovering, learning, and inferring activities from data. These algorithms include (1) rule-based reasoning methods and providing adaptive learning mechanisms to make them more personalized, and (2) machine learning techniques to learn the activities/patterns of PWADs and detect unusual behavior. It is advantageous that such pattern recognition algorithms provide a holistic view of PWADs' activities and routines. They analyze PWADs' data at different temporal granularities to develop automated procedures for macro and micro assessments. In addition, the micro-assessment framework focuses on identifying underlying activity patterns that can be used to infer the well-being of PWADs and to guide them according to the

six errors mentioned above.

Preferably, the sensor system comprises at least one 3D

Infrared sensor, e.g. a 3D infrared camera. This

Sensors detect the thermal radiation (infrared radiation) that changes in time and space due to the movement of people in a defined angular range, which allows the precise determination of the area in which and at what time a person is located. A particularly advantageous feature here is that no picture of the person is taken, which also means that

no problems arise regarding ethical issues.

Furthermore, the sensor system preferably comprises at least one wearable sensor. Wearable sensors are one of the most useful sensor types for monitoring the movements of PWADs. For example, such a wearable sensor can comprise an accelerometer and/or a GPS sensor. Wearable sensors are preferably smart watches, wristbands and/or smart steps. These sensors measure acceleration changes that can be used for motion detection. Due to the typically small size of wearable sensors, it is easy to attach them to a belt, wristband, watch, etc. to monitor the activity of the PWAD and collect corresponding movement data.

to provide.

Particularly preferably, the sensor system comprises at least one wearable sensor which is designed to detect at least one physiological characteristic of the person wearing the sensor. This sensor can, for example, be designed to detect the blood pressure, heart rate and/or body temperature of the wearer. These detected values can be used in particular to distinguish one person from another person based on these values. Furthermore, these values provide

a good overview of the person's health status.

In a preferred embodiment, the sensor system comprises at least one environmental sensor, for example an RFID sensor (Radio Frequency Identification), which can be used to locate misplaced objects, a switch sensor and/or a pressure sensor. Switch sensors are sensors based on magnetic properties, for example, and are attached to doors to monitor whether the door is open or closed. These sensors can help to ensure the safety and well-being of the PWADs.

ensure.

The sensors are connected to each other or to a control element by wire or wirelessly in order to exchange data and in particular to transfer the measured values from the sensors to the

control to send.

To use a modified model of the room, it is preferably provided that in a sixth step a suggestion for modifying the room is issued based on the classified movement data. In this embodiment, the movement data is used to create a modified model of the room based on the model of the room and to use it as a template for modifying the room. For example, furniture can be moved to another location in the room in order to

to improve the movement of the respective PWAD.

For this purpose, the movement data is fed into the spatial model system, e.g. the BIM system. The movement data is processed in the spatial model system or the BIM system in order to

to create a modified model of the room. By

By combining the model of the room and the movement data, the architectural obstacles in the room can be recorded and a suggestion can then be issued before modifying the room. The suggestion can, for example, be a modified model of the room or include one. This enables the room to be optimized with regard to the

living person.

In the intelligent therapeutic environment, the vast majority of recognition systems focus mainly on logical or temporal constraints and overlook the constraints of spatial aspects. Nevertheless, these aspects can significantly influence recognition. A spatial constraint is the spatial configuration of an object in relation to its environment and other objects. For example, since every object has a size that represents the spatial area in 2D/3D, the area of a first object (dining table) should not overlap the area of a second object (wardrobe) during a step (opening the wardrobe door) of an activity. Based on this premise, constraints can be set on the objects involved in an activity and thus the model

of the room can be improved.

As a next step after the fourth or, if applicable, fifth step, the method according to the invention can be repeated, e.g. starting with the second or third step of the method according to the invention, in order to create an improvement cycle. The movement of the PWAD in the new, modified environment is recorded and based on this a new

modified model and finally a new modified

Space is created. This helps to improve the environment in which the

PWAD lives to continue to improve.

In order to simplify the method, it is preferably provided that the data recorded in the third step are sent to a control unit, where the movement data are classified using the movement algorithm and then categorized using the categorization algorithm if necessary (fourth and fifth steps). A control unit is provided which collects and classifies the data of the PWAD in the third step. The control unit is further preferably designed to carry out the sixth step and/or the

to output movement data.

Preferably, a method is provided in which a neural network is used in the fourth step and/or in the fifth step to classify or categorize the recorded movement data. Furthermore, the neural network is preferably part of the control unit. Neural networks are powerful when learning, e.g. the behavior of the respective PWAD, and can be used to classify or categorize the movement data accordingly and reliably.

categorize.

In addition, it is preferred that the classified or categorized movement data, for example in the form of a report, are output on an output device. The output device is preferably part of a control unit or connected to a control unit. The output device is, for example, a screen. Alternatively and/or additionally,

The output device may comprise a printer to print a

To print a report containing movement data. The output data can be used by a treating doctor and/or a nurse to gain insight into

to preserve the condition or movements of the person.

The invention further relates to a system for monitoring a person in a room, in particular for carrying out a method according to the invention, comprising a control unit, a system of sensors arranged in a room, which are designed to detect the movements of the person in the room and to send the movement data to the control unit, wherein the control unit is designed to receive the movement data detected by the sensor system and to classify the movement data into at least a first class and a second class using a movement algorithm, wherein the movements assigned to a first class are then classified using a

categorization algorithm.

Preferably, the sensor system is at least

includes a 3D infrared sensor.

Furthermore, it is preferably provided that the control unit has a

output unit.

In addition, the system can include the above components

The invention will now be described with reference to a drawing

schematically illustrated embodiment in more detail

In this, Fig. 1 shows a schematic

Representation of the method according to the invention and Fig. 2 a

Part of the inventive method in detail.

Fig. 1 shows a method according to the invention for monitoring a person in a room. In a first step 1, a model of the room is created, e.g. by recording the dimensions and inventory of an existing room or by creating a new room using a software program. The model of the room is stored digitally on a control unit, for example. In a second step 2, a system of sensors is arranged in the room, e.g. 3D infrared sensors and/or switching sensors. In addition, a person living in the room is preferably equipped with one or more portable sensors, which are designed, for example, to determine the person's blood pressure or heart rate. The sensors are each connected to a control unit. In a third step 3 of the method, the movements of the person in the room are recorded by the sensor system. In order to distinguish individual people from one another, the data recorded by the portable sensors can be used, for example. The movement data recorded by the sensor system are then processed in a fourth step 4 using a

Movement algorithm classified into at least two classes.

Based on the classification carried out in the fourth step 4, a categorization is carried out in the fifth step 5.

of the movement data. The fourth step 4 and the fifth step 5 can be carried out, for example, in

a control unit connected to the sensor system

is connected to receive the recorded movement data.

The control unit is connected, for example, to an output unit on which the classified movement data

be issued.

In addition, in a sixth step, a suggestion for modifying the room can be issued based on the classified movement data. For example, a modified model of the room can be issued. Based on this suggestion or the modified model, the room can be adapted so that it corresponds to the suggestion or the modified model. The process can then be started again, e.g. in the second or third step, in order to determine the influences of the changed room on the behavior and movement of the

person living in the room.

In Fig. 2, an embodiment of steps 4 and 5 is explained in detail. In step 3, the movement data is recorded and in step 4 the movement data is divided into a first class 7 and a second class 8. In this embodiment, movements that were not completed are divided into the first class 7 and movements that were completed are divided into the second class 8. The movements assigned to the first class 7 are then divided or categorized into one of the six error categories at 9. The movements are then assigned the respective values of the attributes "object", "duration", "time" and "location" at 10, with the values being taken from the recorded movement data.

become.

The categorized movement data is output at 11

and presented in a report 12 and/or in 13 a

Room model system, e.g. a BIM system. The report 12 can be used, for example, to assess the health of the person being monitored. The data from the room model system can then be processed by a planning algorithm 14 to create the model of the room based on the

to adjust movement data.

The movement data classified as “completed” in step 4 are assigned the respective values of the attributes “object”, “duration”, “time” and “location” at 15, whereby the values are taken from the recorded movement data. The movement data are then output at 16 and in a report 17

shown.

Claims (9)

Patent claims: 1. Method for monitoring a person in a room, wherein in a first step (1) a model of the room is created, in a second step (2) a system of sensors is arranged in the room, in a third step (3) the movements of the person in the room are recorded by the sensor system, in a fourth step (4) the recorded movements of the person are classified into at least a first class and a second class using a movement algorithm and in a fifth step (5) the movements assigned to a first class are classified into at least a first class and a second class using a categorization algorithm. 2. Method according to claim 1, characterized in that the Sensor system comprises at least one 3D infrared sensor. 3. Method according to claim 1 or 2, characterized in that in a sixth step a proposal for modifying the room based on the classified movement data is issued. 4. Method according to one of claims 1 to 3, characterized in that the data acquired in the third step are sent to a control unit, where the movement data are classified using the movement algorithm and then, if necessary, using the categorization algorithm be categorized... 5. Method according to one of claims 1 to 4, characterized characterized by the fact that in the fourth step a neuronal Network is used to collect the recorded movement data classify. 6. Method according to one of claims 1 to 5, characterized in that the detected movements on a output device. 7. System for monitoring a person in a room, in particular for carrying out a method according to one of claims 1 to 6, comprising a control unit, a system of sensors arranged in a room, which are designed to detect the movements of the person in the room and to send the movement data to the control unit, wherein the control unit is designed to receive the movement data detected by the sensor system and to classify the movement data into at least a first class and a second class using a movement algorithm, wherein the movements assigned to a first class are then classified using a categorization algorithm. 8. System according to claim 7, characterized in that the Sensor system comprises at least one 3D infrared sensor. 9. System according to claim 7 or 8, characterized in that the control unit comprises an output unit.