JP6852977B2 - Exercise support system - Google Patents

Description

The present invention relates to a technique of an exercise support system that supports the exercise of a subject.

Conventionally, the technology of an exercise support system that supports the exercise of a subject has been known. For example, as described in Patent Document 1.

In Patent Document 1, the vital data of the subject is detected by the sensor unit, the momentum of the subject is calculated based on the detected vital data, and the calculated momentum is within the set target value range. An exercise support system that changes to another exercise content with a different exercise load when there is no exercise is described. As a result, the technique described in Patent Document 1 can be used as an exercise content with an appropriate load for the subject.

However, in the technique described in Patent Document 1, the exercise content is only changed based on the exercise load, and it is not possible to analyze which exercise content has a positive effect on the health condition of the subject. I couldn't say it.

Japanese Unexamined Patent Publication No. 2006-255028

The present invention has been made in view of the above circumstances, and the problem to be solved is to provide an exercise support system capable of providing exercise support useful for improving the health condition of the subject. Is.

The problem to be solved by the present invention is as described above, and next, the means for solving this problem will be described.

That is, the biometric data acquisition unit that acquires the biometric data of the subject, the influential factor data acquisition unit that acquires the influential factor data on the biometric data of the subject including the exercise record data of the subject, and the biometric data. An analysis unit that calculates the contribution rate of the influential factor data to the biometric data by analyzing the biometric data acquired by the acquisition unit and the influential factor data acquired by the influential factor data acquisition unit, and an arbitrary The input unit includes an input unit capable of inputting items, and the input unit can select the purpose of any exercise under arbitrary conditions from the purpose of a plurality of exercises by the subject, and the analysis unit can select the purpose of the exercise. The influential factor data that matches the conditions including at least the selected sleep depth and the biometric data that is related to the purpose of the arbitrary exercise and corresponds to the influential factor data are extracted, and the extracted influential factor data. And the contribution rate is calculated based on the biometric data.

The analysis unit normalizes the influence factor data acquired by the influence factor data acquisition unit, and multiple regression analyzes the biometric data using the normalized values of the influence factor data to obtain the contribution rate. May be calculated.
With such a configuration, more accurate analysis can be performed.

The analysis unit may generate a ranking in which the influence factor data is ranked in descending order of the contribution rate.
With such a configuration, it is possible to calculate which influencing factor data has a great influence on the biometric data.

It includes an advice generation unit that generates advice for improving the health condition of the subject based on the ranking, and an advice providing device that provides the advice generated by the advice generation unit. May be good.
With such a configuration, it is possible to provide effective advice suitable for the target person.

The analysis unit includes linking information in which the purpose of the subject and the biometric data are linked, and extracts the biometric data related to the purpose of the subject based on the linking information. It may be.
With such a configuration, it is possible to perform analysis according to the purpose of the subject.

The analysis unit may extract the influencing factor data selected by the subject and calculate the contribution rate based on the extracted influencing factor data.
With such a configuration, it is possible to analyze the influential factor data for which the subject wants to know the contribution rate.

Exercise support that is useful for improving the health condition of the subject can be provided. In addition, it is possible to perform analysis according to the purpose of the subject. In addition, specialized analysis can be performed when the influential factor data meet the conditions.

The figure which shows the structure of the exercise support system which concerns on one Embodiment of this invention. Flow chart regarding accumulation of exercise record data, meal / sleep record data, and biological data. The flowchart which shows the control of the exercise support system which concerns on one Embodiment of this invention. The figure which shows an example of a data table. A flowchart showing a contribution rate calculation flow. A flowchart showing an advice generation flow. The flowchart which shows the advice generation flow which concerns on 2nd Embodiment of this invention. The flowchart which shows the advice generation flow which concerns on 3rd Embodiment of this invention.

Hereinafter, the outline of the configuration of the exercise support system 1 according to the embodiment of the present invention will be described with reference to FIG.

The exercise support system 1 performs analysis for supporting the exercise of the subject. The exercise support system 1 includes an exercise record data acquisition device 10, a meal / sleep record data acquisition device 20, a biological data acquisition device 30, an advice providing device 40, an input device 50, and a server 100.

The exercise record data acquisition device 10 acquires data (exercise record data) related to the exercise record of the subject. The "exercise record data" can include various conditions (items) related to the exercise record, which are considered to have a possibility of affecting the biological data (being an influential factor on the biological data). Examples of the exercise record data items include an exercise menu, exercise start time, exercise time, exercise sequence, calories burned, and the like. As the exercise record data acquisition device 10, for example, a personal computer, a mobile phone, a smartphone, a tablet terminal, or a wearable terminal can be used. Exercise record data is automatically or manually input to the exercise record data acquisition device 10. Alternatively, the training machine may be used as the exercise record data acquisition device 10, and the training machine may automatically acquire the exercise record data.

The meal / sleep record data acquisition device 20 acquires data related to the meal record (meal record data) and sleep record data (sleep record data) of the subject. In the following, the meal record data and the sleep record data will be collectively referred to as meal / sleep record data. The "meal / sleep record data" can include various conditions (items) related to the diet / sleep record that may affect the biometric data (become an influential factor on the biometric data). .. Examples of the items of the meal / sleep record data include meal contents, meal time, presence / absence of supplement intake, supplement type, sleep time, bedtime, wake-up time, sleep depth, and the like. As the meal / sleep record data acquisition device 20, for example, a personal computer, a mobile phone, a smartphone, a tablet terminal, or a wearable terminal can be used. Exercise record data is automatically or manually input to the meal / sleep record data acquisition device 20.

The biological data acquisition device 30 measures data (biological data) related to the living body of the subject. Examples of biological data include body weight, body fat percentage, muscle mass, abdominal circumference, body temperature, blood pressure, blood glucose level, and the like. As the biological data acquisition device 30, for example, a weight scale, a body fat scale, a body composition meter, a thermometer, a sphygmomanometer, and a glucose meter can be used. The biological data acquisition device 30 acquires biological data by measurement. Alternatively, as the biometric data acquisition device 30, for example, a personal computer, a mobile phone, a smartphone, a tablet terminal, or a wearable terminal is used, and the target person inputs the biometric data acquisition device 30 to acquire the biometric data. You may.

The advice providing device 40 provides advice for improving the health condition of the subject. As the advice providing device 40, for example, a personal computer, a mobile phone, a smartphone, a tablet terminal, a wearable terminal, a television, and a speaker are used. The advice providing device 40 uses the exercise record data acquired by the exercise record data acquisition device 10, the meal / sleep record data acquired by the meal / sleep record data acquisition device 20, and the biometric data acquired by the biometric data acquisition device 30. Based on this, provide advice to the subject to improve their health.

The input device 50 is for the target person to input an arbitrary condition. The input device 50 is used for the purpose of exercise of the subject (for example, diet, improvement of metabolic syndrome, increase of muscle mass (strength training), etc.), or items of biological data that the subject wants to improve (eg, weight, muscle mass, etc.) It is configured so that blood pressure, etc.) can be selected. In addition, the input device 50 selects items of exercise record data and meal / sleep record data (for example, exercise time, exercise menu, presence / absence of supplement intake, sleep depth, etc.) for which the subject wants to know the effect on biological data. It is configured so that it can be done. Further, the input device 50 is configured so that the subject can select the conditions (for example, after 20:00 exercise time, sleep depth 70% or more, etc.) that the subject wants to narrow down with respect to the exercise record data and the meal / sleep record data. As the input device 50, for example, a mobile phone, a smartphone, or a wearable terminal is used. The input device 50 may be integrally configured with the advice providing device 40. That is, one device (terminal) can receive advice and input conditions.

The server 100 stores and analyzes data. The server 100 is connected to the exercise record data acquisition device 10, the meal / sleep record data acquisition device 20, the biological data acquisition device 30, the advice providing device 40, and the input device 50. The server 100 includes an exercise record data storage unit 110, a meal / sleep record data storage unit 120, a biological data storage unit 130, an analysis unit 140, and an advice generation unit 150.

The exercise record data storage unit 110 stores the history of the exercise record data acquired by the exercise record data acquisition device 10.

The meal / sleep record data storage unit 120 stores the history of the meal / sleep record data acquired by the meal / sleep record data acquisition device 20.

The biometric data storage unit 130 stores the history of biometric data acquired by the biometric data acquisition device 30.

The analysis unit 140 analyzes the data. Specifically, the analysis unit 140 uses the exercise record data acquired by the exercise record data acquisition device 10, the meal / sleep record data acquired by the meal / sleep record data acquisition device 20, and the biological data acquisition device 30. Based on the acquired biometric data, the relationship between the influencing factor data (exercise record data and diet / sleep record data) and the biometric data is analyzed. In the following, exercise record data and diet / sleep record data will be collectively referred to as influencing factor data.

More specifically, the analysis unit 140 analyzes how the biological data fluctuates due to fluctuations (differences) in the influencing factor data. Then, the analysis unit 140 calculates the degree of influence (difference) of the item of the influencing factor data on the variation of the item of the biometric data (contribution rate of each item of the influencing factor data to each item of the biometric data). Based on the calculated contribution rate, the analysis unit 140 generates a ranking ranked in descending order of the contribution rate.

The advice generation unit 150 generates advice for improving the health condition of the subject. The advice generation unit 150 generates advice based on the ranking calculated by the analysis unit 140. The advice generator 150 advises which item of the influencing factor data has a great influence on the biometric data or the purpose of exercise related to the biometric data (for example, diet, improvement of metabolic syndrome, increase in muscle mass). To generate.

Hereinafter, with reference to FIG. 2, a specific flow regarding the accumulation of influencing factor data (exercise record data and diet / sleep record data) and biological data in the exercise support system 1 will be described. In the following description, steps S11 and S12 are performed during sleep and after waking up, steps S13 and S14 are performed before exercise, steps S15 and S16 are performed during or after exercise, and steps S17 and S18 are performed after exercise. However, the timing at which data is actually accumulated can be changed as appropriate.

In step S11, the meal / sleep record data acquisition device 20 acquires the sleep record data of the subject. In the present embodiment, the meal / sleep record data acquisition device 20 acquires the sleep time and sleep depth of the subject.

In step S12, the sleep record data (sleep time and sleep depth) acquired by the meal / sleep record data acquisition device 20 is transmitted to the server 100 and stored in the meal / sleep record data storage unit 120 of the server 100. ..

In step S13, the biometric data acquisition device 30 acquires the biometric data of the subject. In the present embodiment, the biological data acquisition device 30 acquires the body weight, body fat percentage, and muscle mass (arms, legs, and trunk) of the subject.

In step S14, the biometric data (body weight, body fat percentage and muscle mass) acquired by the biometric data acquisition device 30 is transmitted to the server 100 and stored in the biometric data storage unit 130 of the server 100.

In step S15, the exercise record data acquisition device 10 acquires the exercise record data of the subject. In the present embodiment, the exercise record data acquisition device 10 acquires the exercise start time, exercise time, exercise menu, and exercise sequence of the subject.

In step S16, the exercise record data (exercise start time, exercise time, exercise menu, and exercise order) acquired by the exercise record data acquisition device 10 is transmitted to the server 100 and stored in the exercise record data storage unit 110 of the server 100. Will be done.

In step S17, the meal / sleep record data acquisition device 20 acquires the meal record data of the subject. In the present embodiment, the meal / sleep record data acquisition device 20 acquires the presence / absence of supplement intake of the subject.

In step S18, the meal record data (presence or absence of supplement intake) acquired by the meal / sleep record data acquisition device 20 is transmitted to the server 100 and stored in the meal / sleep record data storage unit 120 of the server 100.

By repeating steps S11 to S18 every day, the history of exercise record data is accumulated in the exercise record data storage unit 110, and the history of meal / sleep record data is stored in the meal / sleep record data storage unit 120. It is accumulated, and the history of biological data is accumulated in the biological data storage unit 130.

Next, the control of the exercise support system 1 according to the present embodiment will be described with reference to FIGS. 3 to 5. In the following description, the processes from step S11 to step S18 have already been repeated, the history of the exercise record data is accumulated in the exercise record data storage unit 110, and the meal / sleep record data storage unit 120 has the meal / sleep record data storage unit 120. It is assumed that the history of sleep record data is accumulated and the history of biological data is accumulated in the biological data storage unit 130.

In step S110 shown in FIG. 3, the analysis unit 140 has a history of exercise record data stored in the exercise record data storage unit 110, a history of meal / sleep record data stored in the meal / sleep record data storage unit 120, and a living body. The data table shown in FIG. 4 is created using the history of biometric data accumulated in the data storage unit 130. Various data on the day of exercise are input to the data table. Specifically, the exercise start time, the exercise time, the exercise menu, and the exercise order are input as the exercise record data in the data table. In addition, the presence / absence of supplement intake, sleep time, and sleep depth are input to the data table as meal / sleep record data. Further, in the data table, body weight, body fat percentage, arm muscle mass, leg muscle mass and trunk muscle mass are input as biometric data. The depth of sleep is input by being scored by a predetermined index based on subjective data (sense of the subject) or objective data (brain waves, etc.).

In step S120 shown in FIG. 3, the analysis unit 140 analyzes how the biological data fluctuates due to fluctuations (differences) in the influencing factor data. Specifically, in the analysis unit 140, the difference between the numerical value (content) of the influential factor data of one day of exercise, the numerical value of the biological data of that day, and the numerical value of the biological data of the day of the next exercise. The analysis is performed in association with (increase / decrease in the numerical value of the biological data on the day of the next exercise). For example, from the data table shown in FIG. 4, the days when the dumbbell exercise was performed (December 9th, December 15th) are extracted. Here, we focus on December 9. Since the body weight on December 9 is 65.3 kg and the body weight on December 11, which is the next measurement day, is 65.1 kg, the increase / decrease in body weight is -0.2 kg. Therefore, the analysis unit 140 associates the content of the influencing factor data (dumbbell exercise) with the fluctuation of the biological data (body weight −0.2 kg). Do this for all the data entered in the data table.
Then, using this correspondence, the analysis unit 140 uses the contribution rate calculation flow shown in FIG. 5 to determine the contribution rate of the influencing factor data to the biological data (the influence of each item of the influencing factor data on each item of the biological data). Degree) is calculated.

Hereinafter, a method of calculating the contribution ratio will be described with reference to FIG.

In step S121, the analysis unit 140 normalizes the influencing factor data in the data table. Specifically, the influential factor data (exercise time, sleep time, etc.) are normalized so that the mean is 0 and the variance is 1. If the influencing factor data is not shown numerically, normalize it according to appropriate rules.

In step S122, the analysis unit 140 performs multiple regression analysis of the biometric data using the values of the normalized influencing factor data. Specifically, multiple regression analysis is performed with the value of the normalized influential factor data as the explanatory variable and the increase / decrease in the biological data corresponding to the influential factor data as the objective variable.

In step S123, the analysis unit 140 calculates the contribution rate of the influencing factor data (each item) to the biometric data (each item) from the absolute value of the coefficient of the normalized influencing factor data (explanatory variable).

Next, the advice generation flow will be described with reference to FIG.

In step S210, the subject selects the purpose of the exercise (for example, diet, improvement of metabolic syndrome, increase of muscle mass, etc.) and inputs it to the input device 50. Here, the analysis unit 140 includes associative information in which the purpose of the exercise and the biological data are linked, and in the associative information, the living body that is strongly related to the purpose of the exercise and the purpose of the exercise. Data items are linked. When the purpose of exercise is input to the input device 50, the analysis unit 140 extracts biometric data associated with the purpose of exercise based on the association information. For example, when the purpose of exercise of the subject is a diet, the body weight and body fat percentage associated with the diet are extracted. In this way, when the subject selects the purpose of the exercise, the item of biometric data suitable for the purpose is automatically selected. In the following, it will be explained that the purpose of the exercise of the subject is diet.

In step S210, the biometric data item is not automatically selected for the purpose of exercise, but the subject can arbitrarily select the biometric data item. For example, the subject can directly select the items of biological data (for example, body weight and body fat percentage) for which he / she wants to receive advice (who wants to know the contribution rate).

In step S220, the analysis unit 140 generates a ranking in which the influential factor data are ranked in descending order of contribution rate based on the contribution rate calculated in step S120. Here, the analysis unit 140 ranks the influential factor data having a large contribution rate (high effect) to the body weight and the body fat percentage, respectively, and generates a ranking. Then, the analysis unit 140 has a large contribution rate to the diet based on the weight ranking and the body fat percentage ranking (by comprehensively judging the body weight ranking and the body fat percentage ranking, etc.) (the effect is effective). High) Influential factor data is ranked and a ranking is generated.

In step S230, the advice generation unit 150 generates advice based on the diet ranking created by the analysis unit 140. The advice generation unit 150 generates advice such as, for example, "For you, what is effective for dieting is 1st place: exercise time, 2nd place: sleep time." When the biometric data item is selected in step S210, the advice generation unit 150 generates advice based on the ranking of the biometric data item.

In step S240, the advice generated by the advice generation unit 150 is provided to the target person by the advice providing device 40 in response to a request from the target person or the like or automatically.

From this, the subject can know that increasing the exercise time is effective for himself / herself in the diet. In this way, the subject can know the items of the influential factor data that are effective for dieting. Then, the subject can grasp what to do in the future for the diet. In addition, even if it is an item of influence factor data that is generally considered to be effective for dieting, if the ranking is low, it can be seen that the effect on dieting is small for the subject. Therefore, the subject can select and practice the items of the influential factor data depending on whether or not it is effective for the diet.

In this way, in the exercise support system 1 according to the present embodiment, it is analyzed for each subject how the influence factor data (each item) actually affects the biometric data (each item). Provide advice to the target person based on the analysis result. Although the influence of the influencing factor data on the biological data differs depending on the subject, the exercise support system 1 according to the present embodiment can perform an effective analysis suitable for the subject. As a result, the subject can improve his / her health condition by improving exercise, diet, sleep, etc. based on the advice.

As described above, the exercise support system 1 according to the present embodiment includes the biological data acquisition device 30 (biological data acquisition unit) for acquiring the biological data of the subject, and the exercise record data of the subject. The exercise record data acquisition device 10 and the meal / sleep record data acquisition device 20 (influence factor data acquisition unit) that acquire the influence factor data on the biological data, and the biological data and the exercise acquired by the biological data acquisition device 30. It includes an analysis unit 140 that calculates the contribution rate of the influential factor data to the biological data by analyzing the influential factor data acquired by the recording data acquisition device 10 and the meal / sleep recording data acquisition device 20. It is a thing.
With such a configuration, it is possible to provide exercise support useful for improving the health condition of the subject.

Further, the analysis unit 140 normalizes the influence factor data acquired by the exercise record data acquisition device 10 and the meal / sleep record data acquisition device 20, and uses the normalized value of the influence factor data to describe the influence factor data. The contribution rate is calculated by performing multiple regression analysis of biological data.
With this configuration, more accurate analysis can be performed.

Further, the analysis unit 140 generates a ranking in which the influence factor data is ranked in descending order of the contribution rate.
With this configuration, it is possible to calculate which influencing factor data has a large influence on the biometric data.

Further, the exercise support system 1 according to the present embodiment has an advice generation unit 150 that generates advice for improving the health condition of the subject based on the ranking, and the advice generation unit 150 that generates the advice. It is provided with the advice providing device 40 for providing the above.
With this configuration, it is possible to provide effective advice suitable for the target person.

In addition, the analysis unit 140 extracts the biometric data related to the purpose of the subject and calculates the contribution rate based on the extracted biometric data.
With this configuration, it is possible to perform analysis according to the purpose of the subject.

In addition, the analysis unit 140 includes associative information in which the purpose of the subject and the biometric data are linked, and based on the associative information, the biometric data related to the purpose of the subject is obtained. It is to be extracted.
With this configuration, it is possible to perform analysis according to the purpose of the subject.

The exercise record data acquisition device 10 and the meal / sleep record data acquisition device 20 according to the present embodiment are one embodiment of the influence factor data acquisition unit.
Further, the biological data acquisition device 30 according to the present embodiment is an embodiment of the biological data acquisition unit.

Although the embodiments of the present invention have been described above, the present invention is not limited to the above configuration, and various modifications can be made within the scope of the invention described in the claims.

For example, in the present embodiment, both exercise record data and diet / sleep record data are used as influence factor data, but the influence factor data is not limited to this, and is only exercise record data. It may also contain other data that may affect biometric data.

Further, in the data table shown in FIG. 4, the items of exercise record data, meal / sleep record data, and biometric data are examples, and are not limited thereto.

Further, the means for acquiring the exercise record data, the meal / sleep record data, and the biological data are not limited, and any means can be adopted.

Further, in the present embodiment, the analysis unit 140 shall perform analysis using the difference between the numerical value of the biometric data of one day when the exercise was performed and the numerical value of the biometric data of the day when the exercise was performed next. However, the analysis may be performed using the increase or decrease in the numerical value of the biometric data when the first data and the last data in a certain period (for example, one week) are compared. In this case, as the influencing factor data corresponding to the increase / decrease of the numerical value of the biological data, the average value or the number of times in the fixed period can be used.

Further, in the present embodiment, the advice generation unit 150 is supposed to generate advice on the contribution rate of the influential factor data item to the biometric data item, but the influential factor data is used by using the analysis result by the analysis unit 140. You may generate advice on the specific content of the item (eg, which exercise menu is most effective for your diet).

Next, the advice generation flow according to the second embodiment of the present invention will be described with reference to FIG. 7.

In step S310, as in step S210 according to the first embodiment, the subject selects the purpose of exercise (here, increasing muscle mass).

In step S320, the subject further selects arbitrary influence factor data items (here, exercise time, exercise menu, presence / absence of supplement intake, and sleep depth) and inputs them to the input device 50. The subject can select an item that he / she wants to know about the degree of influence (contribution rate) on the purpose of the selected exercise.

In step S330, the analysis unit 140 ranks the influential factor data selected in step S320 in descending order of contribution rate based on the contribution rate calculated in step S120. Here, it is assumed that the purpose of the exercise of increasing muscle mass is associated with the muscle mass of the arm, the muscle mass of the leg, and the muscle mass of the trunk among the items of the biological data. The analysis unit 140 ranks the influence factor data having a large contribution rate (high effect) to the muscle mass of the arm, the muscle mass of the leg, and the muscle mass of the trunk, and generates a ranking. Then, the analysis unit 140 ranks the influential factor data having a large contribution rate (high effect) to the increase in muscle mass based on the ranking, and generates a ranking.

In step S340, the advice generation unit 150 generates advice based on the ranking of muscle mass increase created by the analysis unit 140. The advice generator 150 says, for example, "For you, the most effective way to increase muscle mass is 1st place: exercise menu, 2nd place: sleep depth. Exercise time and supplement intake are not very effective." Generate advice.

In step S350, the advice generated by the advice generation unit 150 is provided to the target person by the advice providing device 40 in response to a request from the target person or the like or automatically.

In this way, the analysis unit 140 calculates the contribution rate by focusing only on the items of the desired influential factor data of the subject. Therefore, the subject can know the ranking of the contribution rate for the desired item.

As described above, in the second embodiment, the analysis unit 140 extracts the influential factor data selected by the subject and calculates the contribution rate based on the extracted influential factor data.
With this configuration, it is possible to analyze the influential factor data for which the subject wants to know the contribution rate.

Next, the advice generation flow according to the third embodiment of the present invention will be described with reference to FIG.

In step S410, as in step S210 according to the first embodiment, the subject selects the purpose of exercise (here, increasing muscle mass).

In step S420, the subject further specifies desired conditions for the influencing factor data (for example, after 20:00 exercise time, sleep depth 70% or more, etc.) and inputs them to the input device 50. The target person can specify the conditions that can be practiced in the future and the conditions that he / she wants to practice in the future.

In step S430, the analysis unit 140 extracts the influential factor data that matches the designated conditions and the biological data corresponding to the influential factor data.

In step S440, the analysis unit 140 calculates the contribution rate of the influential factor data (each item) to the biological data (each item) using the data extracted in step S430. The method of calculating the contribution rate is the same as that of the first embodiment (see FIG. 5).

In step S450, the analysis unit 140 generates a ranking in which the influential factor data are ranked in descending order of contribution rate based on the contribution rate calculated in step S440. Here, the analysis unit 140 ranks the influential factor data having a large contribution rate (high effect) to the increase in muscle mass.

In step S460, the advice generation unit 150 generates advice based on the ranking created by the analysis unit 140. The advice generation unit 150, for example, said, "As for the effect on your muscle mass increase, only when the sleep depth is 70% or more after 20:00 of the exercise time, the effect on the muscle mass increase is the first place: Exercise menu, 2nd place: supplements. "

In step S470, the advice generated by the advice generation unit 150 is provided to the target person by the advice providing device 40 in response to a request from the target person or the like or automatically.

In this way, the subject can receive advice under specific conditions. Therefore, the subject can receive advice that reflects more detailed analysis results.

As described above, in the third embodiment, the analysis unit 140 extracts the influencing factor data matching the conditions and the biometric data corresponding to the influencing factor data, and extracts the influencing factor data and the biometric data. The contribution rate is calculated based on the above.
With this configuration, specialized analysis can be performed when the influential factor data meet the conditions.

1 Exercise support system 10 Exercise record data acquisition device 20 Meal / sleep record data acquisition device 30 Biological data acquisition device 40 Advice providing device 140 Analysis unit 150 Advice generation unit

Claims (6)

The biometric data acquisition unit that acquires the biometric data of the target person,
An influence factor data acquisition unit that acquires influence factor data on the biological data of the subject, including exercise record data of the subject, and an influence factor data acquisition unit.
An analysis unit that calculates the contribution rate of the influential factor data to the biometric data by analyzing the biometric data acquired by the biometric data acquisition unit and the influential factor data acquired by the influential factor data acquisition unit. ,
An input section where you can enter any item,
Equipped with
The input unit is
From the purpose of a plurality of exercises, the purpose of any exercise under any condition can be selected by the subject.
The analysis unit
The influential factor data that matches the conditions including at least the selected sleep depth and the biometric data that is related to the purpose of the arbitrary exercise and corresponds to the influential factor data are extracted, and the extracted influential factor data. And the contribution rate is calculated based on the biometric data.
Exercise support system. The analysis unit
The influence factor data acquired by the influence factor data acquisition unit is normalized, and the influence factor data is normalized.
The contribution rate is calculated by performing multiple regression analysis of the biometric data using the normalized values of the influential factor data.
The exercise support system according to claim 1. The analysis unit generates a ranking in which the influence factor data is ranked in descending order of the contribution rate.
The exercise support system according to claim 1 or 2. An advice generation unit that generates advice for improving the health condition of the subject based on the ranking,
An advice providing device that provides the advice generated by the advice generating unit, and
Equipped with
The exercise support system according to claim 3. The analysis unit
It has linking information in which the purpose of the arbitrary exercise and the biometric data are linked.
Based on the association information, the biometric data related to the purpose of the arbitrary exercise is extracted.
The exercise support system according to any one of claims 1 to 4. The analysis unit
The influence factor data selected by the subject is extracted, and the contribution rate is calculated based on the extracted influence factor data.
The exercise support system according to any one of claims 1 to 5.

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