KR20120016300A - Lifestyle correction method and device with improved calorie counting - Google Patents

Abstract

Discuss lifestyle modifications that enable feedback of activity programs in real time. The apparatus of the present invention determines a dynamic standardized activity pattern based on the activity detection unit for detecting a user's movement in the user's daily life and the detected activity information, and simultaneously sets an activity level for the atypical activity pattern. Determine the start time of the dynamic life activity section for each first activity pattern determination cycle based on the determined activity pattern and activity level, and for the life activity section in which the start time is detected, the first activity pattern determination period A start time detector configured to set an activity pattern determination cycle as a second activity pattern determination cycle, and an activity program generator for generating an activity program in the detected living activity section to fill an insufficient amount of activity relative to a target value when the start time is detected; And a user interface for presenting the generated activity program information to the user. Real-time daily lifestyle calibration device, characterized in that.
Therefore, in the present invention, since the calorie consumption level is calculated by applying the activity level to the atypical activity pattern, more adaptive feedback can be made by calculating the more accurate calories, so that the lifestyle can be more effectively corrected.

Description

Lifestyle correction method and apparatus with improved calorie counting {APPARATUS AND METHODS FOR REVISING LIVING HABITS}

The present invention relates to a lifestyle calibration method and apparatus, and more particularly, to a lifestyle calibration method and apparatus capable of more effective feedback for more accurate calorie calculation by assigning activity levels to a user's static lifestyle pattern. It is about.

Applicant has elected "Lifestyle Correction Method and Apparatus" as Patent Application No. 2008-0011270 and "Improved Lifestyle Correction Method" as No. 2008-0080779.

In determining the exercise pattern or life activity pattern using the acceleration sensor, it is somewhat difficult to judge the exact activity intensity because the movement of the person is too diverse to detect the activity pattern with only a few standardized movement patterns such as walking and running. There was a bunch. Therefore, if you decide the activity pattern per minute in real life, a total of 1440 activity patterns can be determined, but when using only the standard activity patterns, only about 20 to 25% of the activity patterns are used. Accurate calorie counting was difficult because calorie consumption in the remaining 75-80% was excluded.

Therefore, in order to determine the activity intensity of a person more accurately, the emergence of a technology capable of detecting and determining activities other than a formal activity pattern is required.

An object of the present invention for solving the above problems is to provide a life activity pattern detection device and method that can measure more precise and accurate activity intensity by measuring the user's life activity pattern in more detail.

The apparatus of the present invention determines a dynamic standardized activity pattern based on the activity detection unit for detecting a user's movement in the user's daily life and the detected activity information, and simultaneously sets an activity level for the atypical activity pattern. Determine the start time of the dynamic life activity section for each first activity pattern determination cycle based on the determined activity pattern and activity level, and for the life activity section in which the start time is detected, the first activity pattern determination period A start time detector configured to set an activity pattern determination cycle as a second activity pattern determination cycle, and an activity program generator for generating an activity program in the detected living activity section to fill an insufficient amount of activity relative to a target value when the start time is detected; And a user interface for presenting the generated activity program information to the user. It is characterized by.

In the present invention, the start point detection unit comprises an event generation module for generating a corresponding event in response to a signal detected by the activity detection unit, a determination module for determining a representative activity pattern for each first activity pattern determination period based on the generated event; In the case where it is not detected as an activity pattern in the determination module, an activity level determination module for determining an activity level, and a second activity pattern determination cycle different from the first activity pattern determination cycle for the living activity section in which the starting point is detected A period for determining an activity pattern and setting the activity pattern determination period to provide the decision module, an accumulation module for accumulating calories consumed corresponding to a representative activity pattern and activity level, and calculating the difference value by comparing the accumulation value with a target value. Compared to the calculation module and the average activity amount of each monitoring section based on the determined representative activity patterns, It includes a detection module for detecting the same monitoring period in which the start point.

The improved lifestyle correcting apparatus of the present invention calculates calories consumed by applying activity levels even for atypical activity patterns, so that more adaptive feedback can be made by calculating calories more accurately, thereby making it possible to correct lifestyles more effectively. have.

1 is a block diagram of a stand-alone type lifestyle calibration apparatus according to the present invention.
2 is a detailed block diagram of a preferred embodiment of the starting point detection unit 120 of FIG.
Figure 3 shows the activity pattern by the strength versus frequency relationship of the gravity direction component of the acceleration signal.
Figure 4 shows the activity pattern by the strength versus frequency relationship of the gravity direction component of the acceleration signal.
5 shows an example of a daily time zone unit activity pattern determined by the determining unit 124.
6 illustrates a relationship between x, y, z output waveforms of an acceleration sensor of atypical activity patterns.
FIG. 7 is a graph showing each calorie consumed by a solid line 9, a target value (a thin dashed line), and an accumulated value (thick solid line) corresponding to a daily activity time-based unit activity pattern.
8 is a diagram for describing a detection algorithm of the detection module 129.
9 shows a flowchart of a detection program of a preferred embodiment according to the present invention.
FIG. 10 is a flowchart for describing a subroutine of step 110 of FIG. 9.
11 is a flowchart for explaining an embodiment of automatically setting a decision cycle.
12 is a view comparing changes in the representative activity pattern for 20 minutes from 6:01 to 6:20 for the case of 10 minutes and 1 minute of decision periods.
FIG. 13 is a view showing activity sections and daily schedules from 04:37 to 12:40 of FIG.
14 is a view showing the daily activity intervals.
15 shows a four-day daily schedule corresponding to the activity section.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will now be described in more detail with reference to the accompanying drawings. As the inventive concept allows for various changes and numerous embodiments, particular embodiments will be illustrated in the drawings and described in detail in the text. However, this is not intended to limit the present invention to the specific disclosed form, it should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention.

< Example  >

The stand-alone type of the present invention may be implemented as a portable electronic product in a portable size, for example, combined with an electronic pedometer, a portable electronic blood pressure monitor, an MP3 player, a PMP, a DMB receiver, a portable radio, or a mobile phone.

1 is a block diagram of a stand-alone type lifestyle calibration apparatus according to the present invention.

The apparatus 100 of FIG. 1 includes an activity detector 110, a start time detector 120, an activity program generator 130, and a user interface 140.

Activity detector 110 is composed of a tilt sensor, a three-axis accelerometer sensor. The activity detector 110 outputs the detection signals of the sensors as activity information in response to the movement of the user. It can be combined with geomagnetic sensors or angular velocity sensors to detect more accurate activity patterns.

The start point detection unit 120 detects a start point of a dynamic and static life activity section in real time or periodically based on the detected activity information, and the activity program generating unit 130 detects an insufficient amount of activity compared to a target value when the start point is detected. An activity program in the detected living activity section is generated to induce filling. The start point detector 120 and the activity program generator 130 may be implemented in hardware on an ASIC chip or in software using a microprocessor or a microcomputer system.

The user interface unit 140 presents the generated activity program information to the user at visual (liquid crystal display), auditory (loudspeaker) or tactile (vibration generator). The user interface unit 140 includes input means such as a keypad as well as output means for outputting feedback information.

2 is a detailed block diagram of a preferred embodiment of the starting point detection unit 120 of FIG.

Referring to FIG. 2, the start point detection unit 120 includes an event generation module 122, an activity level determination module 123, a determination module 124, a determination cycle selection module 125, an accumulation module 126, and a calculation module. 128, a detection module 129.

The event generating module 122 uses the sensing signal provided from the activity detecting unit 110 to form an activity pattern, that is, standardized activity patterns, namely, slow walk and walk, by the gravity direction component and the horizontal direction component of the acceleration signal. It generates events corresponding to walk, fast walk, run, step up and step down.

3 shows an activity pattern based on the strength versus frequency relationship of the gravity direction component of the acceleration signal, and FIG. 4 shows an activity pattern based on the strength versus frequency relationship of the gravity direction component of the acceleration signal. The technology for determining the activity pattern by analyzing the acceleration signal uses the same or similar technology as that disclosed in the registered patent No. 601981 to which the present inventor has registered a detailed description.

The determination module 124 determines the representative activity pattern at a given decision cycle based on the generated event. For example, the events generated during the decision period are divided into activity pattern events, and the sum of the total time periods is determined during the decision periods of the divided events, and the largest event is determined as the representative activity pattern of the decision period. If the largest and same event exists, the activity pattern that consumes the most calories is determined as the unit activity pattern. For example, when the event sum value corresponding to the walking activity pattern is the same as the event sum value corresponding to the running activity pattern, the running activity pattern having more calories is determined as the representative activity pattern.

FIG. 5A shows an example of the daily activity representative time pattern determined by the decision means 124 when the decision period 1 minute is set, and FIG. 5B is determined by the decision means 124 when the decision period 10 minutes is set. An example of the representative activity pattern by daily time zone is shown.

Therefore, the shorter the decision cycle, the more detailed representative activity patterns are obtained, so the activity measurement is more accurate. However, since the data amount of the representative activity pattern obtained is increased, the data processing burden of the system is increased as a whole and sufficient storage space for storing these data should be secured. On the contrary, the longer the decision period, the less data is processed, so the data processing burden of the system is reduced, but the error of activity measurement becomes larger. Therefore, system designers have to compromise between the two according to the design specification.

The decision period selecting module 125 transmits the selected decision period to the decision module 124 in response to the selection control signal SC. The decision cycle selection module 125 selects a decision cycle short in a life activity section that needs to know the amount of activity in detail for feedback, and selects a decision cycle long in a life activity section in which there is no big change. Therefore, it is possible to determine the optimum amount of activity within the design specification of the proper system, thereby providing effective feedback. The selection control signal SC may be set by a user or a health manager through a user interface. In addition, the selection control signal SC may be automatically generated in response to the start point detection signal. That is, since the starting point detection signal is generated in the dynamic activity section, the feedback period is actively active, and the decision period is controlled to be selected shortly. In the non-dynamic activity period, the decision period is controlled to be selected long.

When the activity level determination module 123 is not a normalized activity pattern in the determination module 124, the activity level determination module 123 compares the xyz axis output waveform to determine activity levels in four stages of activity levels 0 to 3.

6 illustrates the relationship between the x, y, z output waveforms of the acceleration sensors of the atypical activity patterns. Referring to the drawing, in the case of the waist swivel 502, there is almost no change in the y-axis output waveform in the vertical direction and large changes in the x-axis and z-axis waveforms occur. In the case of shaking 504 to the left and right, the change in the x-axis output waveform is larger than the z-axis, and in the case of shaking 506, the change in the z-axis output waveform is larger than the x-axis. In the case of sitting and standing 508, the change of the y-axis output waveform is large, but the change time is remarkably slower than the typical output waveforms. In case of severe vibration, all of the xyz-axis output waveforms are It can be seen that it is correct and greatly changed.

Therefore, when the levels of the xyz axis output waveforms are all 0, it is determined as a rest state and the activity level is determined as 0 level. If any one of the levels of the xyz axis output waveform is above 0 and below 1, the activity level is detected as 1 level. For example, shaking left and right or shaking back and forth corresponds to the activity level 1 level. If any one of the levels of the xyz axis output waveform is greater than 1 and less than 2, the activity level is detected as 2 levels. For example, waist rotation corresponds to level 2 of activity. If any of the levels of the xyz axis output waveform level is 2 or more, the activity level is detected as 3 levels. For example, sitting or rising or severe vibration corresponds to level 3 of activity.

Accumulation module 126 is a calorie corresponding to the activity pattern and activity level, for example, 10 calories for resting or activity level 0, 50 calories for walking slowly or activity level 1, 70 calories for walking or activity level 2, The fast walking or activity level 3 generates a cumulative value by accumulating calories burned in real time with reference to a lookup table written in 100 calories or 200 calories for running.

FIG. 7 is a graph showing the calories burned (solid thin line), the target value (thin dotted line), and the accumulated value (thick solid line) corresponding to the daily activity pattern for each time period.

The calculation module 128 calculates the difference value of FIG. 6 by comparing the accumulated value with the target value. Standard exercise indicators include the user's standard weight and the user's daily goal recommended calories burned. For example, if a user is 40 years old and is 172cm tall and weighs 70Kg, the standard weight is 66.24Kg and the obesity degree is 10.57%, which corresponds to overweight. Therefore, it is determined that mild exercise is necessary, and 2856 Kcal plus 70% of the basic metabolism becomes the daily recommended activity calories burned. The daily calories burned was measured at 565 Kcal, based on daily calories burned, so a personal standard exercise index was generated, excluding the basic metabolism of 1680 Kcal and 611 Kcal being the daily target recommended calories burned.

The detection module 129 may measure the amount of activity for each monitoring section based on the determined representative activity patterns, eg, rest or activity level 0 is 1, walk slowly or activity level 1 is 2, walk or activity level 2 is 3, walks fast or At the activity level 3, the average of each monitoring section is calculated by averaging the weighted values such as 4 and the running 5, and comparing the obtained average values to detect the monitoring section whose activity changes rapidly at the start or end point. If it changes suddenly and increases, it is the starting point. If it changes suddenly, it is detected as the end point.

8 is a diagram for describing a detection algorithm of the detection module 129. Referring to FIG. 8, when the average amount of activity A (t4) in the current t4 monitoring section fluctuates more than 150% compared to the average amount of activity A (t3) in the previous t3 monitoring section, a dynamic living activity section, for example, The t4 section is detected as the starting point by recognizing it as the starting point of the moving section or the exercise section. Alternatively, the starting time point may be detected by comparing the average amount of activity over a certain period of time such as one week, two weeks, one month, and the like with the average amount of activity in the current monitoring section t4.

9 shows a flowchart of a detection program of a preferred embodiment according to the present invention. One embodiment is a case where a user or a health care provider inputs a determination period value of a time zone corresponding to an activity section.

When the power is turned on, the determination period selection module 125 measures the current time (S102) and inputs the current time as the selection control signal SC to select the determination period P1 (S104). If the current time is a dynamic activity section that requires feedback, a short decision period is selected. If the current time is a static activity period such as class time, working hours or bedtime, a long decision period is selected.

The event generating module 122 uses the parameter T as an initial value (S106), obtains a detection signal from the activity detecting unit 110 (S108), and determines an activity pattern to generate an event (S110). The determination module 124 checks whether the value of the parameter T is larger than the determination period P1 (S112), and if it is not large, the determined activity pattern is stored for each pattern type (S114). Return to step 106. As such, the process of determining and storing the activity pattern is performed while increasing the parameter T value during the determination cycle.

In step 110, if the activity pattern is not determined during the activity pattern determination process, the activity level is determined. Details will be described later.

If the value of the parameter T is greater than the determination period P1 in step 112, the representative activity pattern in the determination period P1 is determined (S116). When the representative activity pattern of the determination period P1 is determined, it is stored (S118) and the activity pattern data stored during the determination period is deleted (S120).

Accumulation module 126 calculates the calorie consumption per activity pattern and activity level in step 110 (S122) and adds to the existing calorie consumption (S124). The calculation module 128 checks the determination cycle (S126) and determines the total calories burned for each determination cycle after the determination cycle (S128) and stores the calories burned for each determination cycle (S130).

Therefore, representative activity patterns and total calories burned are stored for each decision cycle.

FIG. 10 is a flowchart for describing a subroutine of step 110 of FIG. 9.

The determination module 124 analyzes the signal (S1002) and determines whether it is the designated activity pattern (S1004). If it is the activity pattern, the pattern type is determined (S1006) and returned. If it is not the regular activity pattern specified in step S1004, the activity level determination module 123 determines whether the signal level of each axis is 0 (S1008). It determines with the level and returns (S1010). In step S1008, it is determined whether any one level of signals of each axis is greater than 1 (S1012). Otherwise, the activity level is determined and returned (S1014). In step S1012 it is determined whether any one level of the signal of each axis is greater than 2 (S1016), or determines the activity level 2 and returns (S1018). If the level of any one of the signals of each axis is greater than 2 in step S1016 and determines the activity level 2 and returns (S1020).

Therefore, even if it is not a standardized activity pattern, it is possible to calculate calorie level more accurately by determining activity level and accumulating and applying it in calorie calculation.

11 is a flowchart for explaining an embodiment of automatically setting a decision cycle. The detection module 129 initially generates a long decision period selection control signal and provides it to the decision period selection module 125 (S132). When a start point of the dynamic activity section is detected (S134), a short decision period selection control signal is generated. Generated and provided to the decision period selection module 125 (S136). In step 136, the detection module 129 returns to step 132 when the end point of the dynamic activity section is detected (S138). Therefore, in response to the detection of the dynamic activity section, the short decision period is selected in the dynamic activity section, and the long decision period is selected in the other static activity section.

12 is a view comparing changes in the representative activity pattern for 20 minutes from 6:01 to 6:20 for the case of 10 minutes and 1 minute of decision periods. That is, in the case of 10 minutes, only three representative activity patterns are obtained as data in 20 minutes, and in one minute, 20 representative activity patterns are obtained. Therefore, if the decision period is long, the amount of data decreases but the exact activity cannot be analyzed. However, if the decision period is short, it is possible to analyze the activity state of walking normally and walking slowly for about 3 minutes. However, the data stored will increase to 20.

Therefore, by applying a shorter decision cycle to the dynamic activity section, it is more effective in correcting habits because feedback can be given to the current activity state.

Activity program generation unit 130 in consideration of the daily activity schedule of the user, if the detected start time is within the start time and the error range of the life activity section on the schedule and presents a corresponding activity program, if the error is out of the range Warns that they have deviated from their regular life patterns. In response to the detected start time, the activity program generator 130 generates a lifestyle correction message corresponding to the calorie difference value by voice, text, image, or vibration with reference to the lookup table created in FIG. 11. For example, you can present an activity program that says, “At the current 6 o'clock at work, there are not enough 200 calories compared to the target.

FIG. 13 is a view showing activity sections and daily schedules from 04:37 to 12:40 of FIG. The user's activity interval is determined by the duration of the activity state or the cumulative value of calories burned. For example, a dynamic activity interval can be defined as an activity state over walking slowly for more than 5 minutes, or a sum of calories consumed over a certain amount. Based on the user's personal information, the criteria for the cumulative value of the duration and calories burned may be defined differently.

Referring to FIG. 14, a user can select a total of seven dynamic activity sections (203, 205, 207, 209, 211, 213, and 215) and eight static activity sections (202, 204, 206, 208, 210, 212, 214, and 216). ) If the user inputs home, work, and gym as a plurality of activity points, the activity points are based on the duration of the activity section, the difference between the end time of the first activity section and the start time of the second activity section, and the time of the activity section. Decide whether it is an activity section in the field or a moving section between activities. In the morning (202) before the start of the activity, the house 220, the long walk activity state (207) is a long-lasting health club 224, the long period of static activity interval 212 is a workplace (228) 232). The other sections are called activity sections 222, 226, and 230 that move among the activity places.

However, the user interface unit may receive a means of movement between the activity places as well as the plurality of activity places as well as the residence time in the plurality of activity places according to the user's selection. By inputting the residence time in the activity place, the user can distinguish the activity section more accurately than inferred to the activity state, and when the means of transportation between the activity places are input, lifestyle correction information can be provided in detail.

It is determined that sections 206 and 208 are static activity sections that move between activities when there is no entry of the residence time from the user, but if the user inputs the residence time in the gym from section 206 to 208 In other words, it will be determined as a static activity section at the activity sites. Therefore, it is possible to know the movement process of the user more accurately, so that the lifestyle can be corrected by using the static activity section that moves between the activity places, so that effective health care is possible. In addition, when a means of movement between the activity sites is input, a better movement method can be presented to the user in the static activity section moving between the activity places.

According to FIG. 14, 8 hours from 4:31 to 12:31 was observed in the user's activity section, and thus about 203 Kcal, which is 1/3 of the daily target recommended calories, 611 Kcal should be consumed. Among them, if the duration of the dynamic activity section of the user is distributed according to the time 120 minutes, the duration of the dynamic activity section 207 in the health club is 80 minutes, it can be said that the consumption of about 136Kcal is required. In addition, as mentioned above, the health club may require more activities to increase the calories burned and generate lifestyle correction information according to the characteristics of the activity. In addition, lifestyle correction information may be generated to consume calories in the section 204 of moving from home to the gym but not exercising-the time when the activity is possible but the inactivity.

15 shows a four-day daily schedule corresponding to the activity section. Referring to FIG. 15, level 1 is a case where only an activity location is input and user input of level 2 to level 4 is required to give an effective feedback for maintaining a more active lifestyle. Input of these activities is a reasonably effective method without the addition of components such as GPS or RFID.

Level 2 is an activity location such as the first place where the user lives, the second place where life is high (e.g., an office worker or a school for a student), and another third place (e.g., a health club or a school). Enter the start and end point information of the dwell time to ensure a more accurate activity interval.

Level 3 is used to calculate the amount of activity by inputting the type of transportation between places, for example, by car, public transportation (subway, bus), walking, and the like.

Level 4 allows you to measure the amount of activity in more detail by entering information related to the detailed sub-activity section for each activity section.

For lifestyle correction, it is desirable to effectively use not only dynamic activities but also static activities that move between activities. Therefore, it is desirable to require the user to be active at the beginning of the static activity section that moves between the user's activities. Therefore, it is desirable to present lifestyle correction information regardless of the static activity section or the dynamic activity section. In addition, when the present invention is applied to the students can detect the activity state of the class time of the students to measure the concentration of the class time and feedback to help improve the learning ability.

The user can minimize the effort and hassle of data input, and the user can eliminate the trouble of data input in real time or periodically to facilitate the data input, thereby improving user convenience and compliance. In addition, it can be configured at low cost without using expensive parts, and can be easily purchased and used by anyone, thereby contributing to the improvement of national health. In addition, it can contribute to improving the analysis and concentration of children's learning habits, and can be directly instructed to move the current movement in the direction of increasing the amount of activity at the time when the user actually moves, which is very effective for the user's habit correction.

122: event generation module
123: activity level determination module
124: decision module
125: decision cycle selection module
126: Accumulation Module
128: output module
129: detection module

Claims (2)

An activity detector for detecting a user's movement in a user's daily life;
A dynamic formalized activity pattern is determined based on the detected activity information. At the same time, an activity level is determined for an atypical activity pattern, and the dynamic activity is determined for each first activity pattern determination cycle based on the determined activity pattern and activity level. A start time detection unit for detecting a start time of the human life activity section, and setting an activity pattern determination cycle with a second activity pattern determination cycle different from the first activity pattern determination cycle for the life activity section from which the start time was detected;
An activity program generator configured to generate an activity program in the detected living activity section to fill an amount of activity insufficient to a target value when the start time is detected; And
And a user interface unit for presenting the generated activity program information to the user. The method of claim 1, wherein the start time detection unit
An event generation module for generating a corresponding event in response to the signal detected by the activity detection unit;
A determination module that determines a representative activity pattern for each first activity pattern determination period based on the generated event;
An activity level determination module for determining an activity level when it is not detected as an activity pattern in the determination module;
A determination period selection module for setting the activity pattern determination period as a second activity pattern determination period different from the first activity pattern determination period for the living activity section in which the start time is detected;
Accumulation module for accumulating the calories burned corresponding to the representative activity pattern and the activity level;
A calculation module for comparing the accumulated value with a target value and calculating a difference value; And
And a detection module which detects a monitoring section in which the amount of activity changes abruptly as the starting point by comparing the average amount of activity for each monitoring section based on the determined representative activity patterns.

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