Body condition evaluation method, and body condition evaluation system

Abstract

To provide a body condition evaluation method and a body condition evaluation system for accurately evaluating the body condition of a person to be evaluated in a high temperature environment with high environmental temperature.SOLUTION: A body condition evaluation system for evaluating the body condition of an operator as a person to be evaluated uses a biological sensor including heartbeat detection means that detects heartbeat data of the person to be evaluated, acceleration detection means that detects acceleration data with the motion of the person to be evaluated, and temperature detection means that detects temperature data indicating temperature around the person to be evaluated, to generate a heartbeat prediction model of the person to be evaluated in advance on the basis of the obtained heartbeat data, acceleration data, and temperature data, and evaluates the body condition of the person to be evaluated on the basis of the difference between heartbeat data 52 detected during evaluation and a predicted value 51 calculated from the heartbeat prediction model.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present application relates to a health condition evaluation method and a health condition evaluation system that evaluate the health condition of a person being evaluated based on biometric information obtained from the person being evaluated, and in particular, to a health condition evaluation method and a health condition evaluation system that evaluate the health condition of a person being evaluated in a high environmental temperature and under a high heat load. [Background technology]

[0002] In recent years, with the development of Internet connection environments such as wireless LAN, the development of means of short-range information transmission such as Bluetooth (registered trademark), and the spread of high-performance mobile devices such as smartphones and small sensor devices that can measure physical data such as body temperature, heart rate, and sweat rate, evaluation systems that evaluate the physical condition of the person being evaluated based on biometric information obtained by sensor devices, and health management systems that manage the health of the person being evaluated based on the evaluation results and reduce the risk of developing heatstroke, which has become a problem in recent years, have been put into practical use.

[0003] As a health evaluation system for evaluating and managing such physical condition, the inventors have proposed a health evaluation and management system that uses a wearable detection device for detecting biological signals, equipped with a three-dimensional acceleration sensor that grasps the physical movements of the person being evaluated and a bio-information acquisition unit that detects the heart rate, to calculate a work strain index that indicates the intensity of the person being evaluated's work, the person's heart rate index and physical fitness index, and a heat stroke risk index, evaluates the person's risk of developing heat stroke and physical condition, and feeds back the evaluation results to the person being evaluated (see Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2019-185386 A Summary of the Invention [Problem to be solved by the invention]

[0005] In the conventional physical condition evaluation system, a biometric information acquisition unit is arranged on the chest of the undershirt, and the biometric information acquired by the biometric information acquisition unit is transmitted to an information processing unit of a cloud server on the Internet via a communication device such as a smartphone carried by the subject. The information processing unit evaluates the subject's physical condition by calculating a heart rate index that is estimated to be the subject's resting heart rate based on a regression line obtained from the correlation between the detected heart rate data and acceleration data. In addition, a heat stroke risk index is calculated based on a work load index calculated based on the heart rate index and the slope of the regression line, and a heat load index obtained from the measurement results of the environmental temperature and the temperature inside the worker's clothes.

[0006] While the inventors were actually using the above-mentioned health evaluation and management system and verifying its accuracy, they discovered the following new problems: (1) In an environment where the outside temperature exceeds body temperature, heat flows from high temperature to low temperature, making it difficult to assess risk using conventional methods that assume an outside temperature below body temperature. (2) In environments with temperatures between 24°C and 35°C, or WBGT (Wet Bulb Globe Temperature) between 22°C and 33°C, temperature and heat stress are correlated, so conventional methods are effective, but outside of this range, there is no correlation. Therefore, heat load cannot be accurately evaluated using temperature and humidity information alone. (3) Heat tolerance varies from person to person and cannot be judged using the same standards.

[0007] Therefore, it was found that in high temperature conditions where the environmental temperature is equal to or higher than body temperature, it is preferable to apply an evaluation method different from that when the environmental temperature is not so high to evaluate the risk of heatstroke in the subject, so as to more reliably protect the subject from heatstroke.

[0008] Conventionally, the heat index WBGT has been widely used as an index to evaluate the heat load of the environment, but it is not possible to accurately determine the risk of developing heat stroke when the environmental temperature is extremely high, such as when it exceeds the upper limit of 33°C recommended as a safety standard, which is the same as body temperature. For this reason, it is considered extremely useful to establish an evaluation method that can accurately determine the risk of developing heat stroke when a person is placed in such a high-temperature environment.

[0009] The present application aims to solve the problems associated with the above-mentioned conventional technologies, and aims to provide a health condition evaluation method and a health condition evaluation system that perform a health condition evaluation based on the biometric information of the person being evaluated, and that are capable of accurately evaluating the health condition of a person being evaluated in a high-temperature environment. [Means for solving the problem]

[0010] In order to solve the above problems, the physical condition evaluation method disclosed in the present application is a physical condition evaluation method performed by a computer, which uses a heart rate detection means for detecting heart rate data of the person being evaluated, an acceleration detection means for detecting acceleration data associated with the movements of the person being evaluated, and a temperature detection means for detecting temperature data indicating the ambient temperature of the person being evaluated, and is characterized in that a heart rate prediction model of the person being evaluated is created in advance based on the obtained heart rate data, acceleration data, and temperature data, and the physical condition of the person being evaluated is evaluated based on the difference between the heart rate data detected at the time of evaluation and the predicted value calculated from the heart rate prediction model.

[0011] In addition, the physical condition evaluation system disclosed in the present application comprises a heart rate detection means for detecting heart rate data as biometric information of the person being evaluated, an acceleration detection means for detecting acceleration data associated with movement, a temperature detection means for detecting temperature data indicating the ambient temperature, and a data processing unit for creating a heart rate prediction model of the person being evaluated in advance based on the acquired biometric information and calculating the difference between the heart rate data measured at the time of evaluation and a predicted value calculated from the heart rate prediction model, and the data processing unit is characterized in that it evaluates the physical condition of the person being evaluated based on the calculated difference. Effect of the Invention

[0012] With the above configuration, the physical condition evaluation method disclosed in the present application can accurately determine the magnitude of heat stress imposed on the subject based on the difference between a predicted value calculated from a heart rate prediction model created based on biometric information obtained from the subject and the heart rate data measured at the time of evaluation, and can correctly evaluate changes in the subject's physical condition due to heat stress.

[0013] Furthermore, with the above configuration, the physical condition evaluation system disclosed in the present application can accurately evaluate the physical condition of the person being evaluated from the increase in the person's heart rate caused by heat stress. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a block diagram illustrating an example of the configuration of a physical condition evaluation system according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating the configuration of a biological information acquisition unit used in the health evaluation system described in this embodiment. [Diagram 3] FIG. 3 is a diagram showing the results of an investigation into the ratio of the change rate of the inner clothing temperature and the heart rate in a high temperature environment. [Figure 4] FIG. 4 shows an evaluation map for grasping the distribution of the past measurement data of the subject, which is used when creating a heart rate prediction model in the physical condition evaluation method according to this embodiment. [Diagram 5]FIG. 5 is an image diagram showing the difference between the heart rate prediction model and the actually measured heart rate in the physical condition evaluation method according to the present embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] The physical condition evaluation method disclosed in the present application uses a heart rate detection means for detecting heart rate data of the person being evaluated, an acceleration detection means for detecting acceleration data associated with the movements of the person being evaluated, and a temperature detection means for detecting temperature data indicating the ambient temperature of the person being evaluated, and creates a heart rate prediction model for the person being evaluated in advance based on the obtained heart rate data, acceleration data, and temperature data, and evaluates the physical condition of the person being evaluated based on the difference between the heart rate data detected during the evaluation and the predicted value calculated from the heart rate prediction model.

[0016] With the above configuration, the physical condition evaluation method disclosed in the present application can grasp the heart rate of the subject under a certain heat load as a heart rate prediction model, and can determine the heat load on the subject from the degree of deviation from the heart rate data measured at the time of evaluation, thereby evaluating the physical condition of the subject. Therefore, the physical condition of the subject, especially in a high-temperature environment, can be accurately evaluated.

[0017] In the above-mentioned physical condition evaluation method, it is preferable to detect the temperature inside the clothes of the subject as the ambient temperature of the subject. The temperature inside the clothes changes less than the ambient temperature, and can more accurately represent the thermal environment in which the subject is placed.

[0018] In addition, it is preferable that an upper limit and a lower limit are set for the ambient temperature used to create the heart rate prediction model of the subject. As a result of the inventors' study, it was confirmed that a linear relationship between the ambient temperature and the heart rate appears only within a certain temperature range, so in order to perform an accurate physical condition evaluation, it is preferable to classify the ambient temperature and perform data processing corresponding to the temperature range.

[0019] In addition, when the ambient temperature is the temperature inside the subject's clothes, it is preferable that the lower limit value is set to a temperature between 29°C and 32°C, and the upper limit value is set to a temperature between 34°C and 38°C.

[0020] Furthermore, it is preferable that the heart rate prediction model is created by an index related to the heart rate data, an index related to the acceleration data, and an index related to the temperature data, which are determined based on the past measurement data of the subject. In creating a heart rate prediction model that is a standard for determining the difference with the measurement data at the time of evaluation, a more accurate prediction model can be created by using the past measurement data of the subject.

[0021] Furthermore, it is preferable to determine the risk of developing heat stroke of the person being evaluated as a result of the physical condition evaluation, because it is desirable to accurately determine the risk of developing heat stroke, which has become a social problem as an effect of heat load on the human body.

[0022] The physical condition evaluation system disclosed in the present application comprises a heart rate detection means for detecting heart rate data as biometric information of the person being evaluated, an acceleration detection means for detecting acceleration data associated with movement, a temperature detection means for detecting temperature data indicating the ambient temperature, and a data processing unit for creating a heart rate prediction model of the person being evaluated in advance based on the acquired biometric information and calculating the difference between the heart rate data measured at the time of evaluation and a predicted value calculated from the heart rate prediction model, and the data processing unit performs a physical condition evaluation of the person being evaluated based on the calculated difference.

[0023] With this configuration, the physical condition evaluation system disclosed in the present application can perform an accurate physical condition evaluation of an individual being evaluated in a high temperature environment.

[0024] Hereinafter, embodiments of a physical condition evaluation method and a physical condition evaluation system disclosed in the present application will be described with reference to the drawings.

[0025] (Embodiment) [Overall system configuration] First, the overall configuration of an example of a physical condition evaluation system disclosed in the present application will be described.

[0026] The physical condition evaluation system described as an example in this embodiment is incorporated into a heatstroke risk management system that acquires biometric information of workers working at a construction site and evaluates and manages the risk of heatstroke for each worker, and evaluates the physical condition of each worker as the person being evaluated.

[0027] The physical condition evaluation system disclosed in this application evaluates the physical condition of the person to be evaluated by calculating a heart rate index equivalent to the heart rate of the person to be evaluated at rest based on the heart rate data and acceleration data acquired as the biometric information of the person to be evaluated. Therefore, as exemplified in this embodiment, the system can be easily incorporated into various biometric information processing and evaluation systems that acquire heart rate (pulse) information and acceleration information indicating body movement as the biometric information of the person to be evaluated and perform some kind of evaluation, and can be a system that shares the obtained biometric information to perform the function of evaluating the physical condition of the person to be evaluated.

[0028] In this application, a system is described that accurately grasps changes in physical condition caused by heat stress of an assessee who works in a particularly high-temperature environment, and judges and evaluates the risk of developing heat stroke, etc. In addition to the construction site given as an example, possible sites where workers work in high-temperature environments with high ambient temperatures include steelworks with blast furnaces and machine manufacturing plants with high-temperature furnaces for processing steel plates and steel materials.

[0029] FIG. 1 is a block diagram showing an example of the configuration of each part of a heat stroke development risk management system in which a physical condition evaluation system described in this embodiment is incorporated.

[0030] As shown in Figure 1, the heatstroke risk management system incorporating the physical condition evaluation system is composed of a worker 10 who is to be evaluated, a cloud server 21 on the Internet 20 that evaluates the physical condition of the worker 10 based on his / her biometric information and evaluates the risk of developing heatstroke, a site supervisor 30 who is the manager who oversees a work group that includes a certain number of workers 10, and a business establishment 40 that has multiple site supervisors 30 under its management to grasp the overall picture and operate and maintain the heatstroke risk assessment system.

[0031] It goes without saying that the above is a generalized example assuming a typical construction site, and that different forms can be adopted depending on the actual configuration of the site where the heatstroke risk management system is actually introduced, such as when one site supervisor 30 manages one worker 10, when the site supervisor and the business establishment are inseparable, or when multiple business establishments are included and the entire construction site is managed on a larger scale.

[0032] In the physical condition evaluation system described in this embodiment, a worker 10 wears an undershirt 18 having a biosensor 11, which is a bioinformation acquisition unit that acquires the worker's own bioinformation, placed on the chest.

[0033] Fig. 2 is a diagram showing an example of the configuration of an undershirt with a biosensor arranged thereon, which is worn by a worker in the physical condition evaluation system described in this embodiment. Fig. 2(a) shows the front side of the undershirt, and Fig. 2(b) shows the back side of the undershirt, i.e., the side that faces and comes into contact with the surface of the worker's body.

[0034] 2, a biosensor 11 is disposed on the chest of an undershirt 18 worn by a worker 10. More specifically, the biosensor 11 is composed of a data acquisition and transmission unit 11a disposed in the center of the chest on a front surface 18a of the undershirt 18, and an electrode section 11b of a heartbeat sensor connected to the data acquisition and transmission unit 11a and disposed extending in the left-right direction on a back surface 18b of the undershirt 18, i.e., the side that comes into contact with the skin.

[0035] In the physical condition evaluation system according to this embodiment, the biosensor 11 detects the heartbeat, temperature inside the clothes, and movement of the worker 10, and the heartbeat of the worker 10 can be detected more accurately by contacting the chest with an electrode serving as heartbeat detection means (heartbeat sensor) arranged on the back surface of the undershirt 18. In addition, a temperature sensor (not shown) serving as temperature detection means for detecting the temperature inside the clothes, and an acceleration sensor chip (not shown) serving as acceleration detection means for detecting acceleration in three-dimensional directions are housed in the data acquisition and transmission unit 11a.

[0036] The biosensor 11 and a smartphone 12, which is a portable terminal carried by the worker 11, are constantly connected via short-range communication such as Bluetooth (registered trademark), and various information acquired by the biosensor 11 is sent to the smartphone 12 at any time.

[0037] The smartphone 12 includes a data receiving unit 15 and a data transmitting unit 16, and is constantly connected to the Internet 20 as a network environment via a wireless LAN or a mobile phone information carrier. In the physical condition evaluation system of this embodiment, the smartphone 12 is linked to the identification data of each worker 10 using the functions of the physical condition evaluation system, and the smartphone 12 includes an assessee information transmitting unit 13, and uses the data transmitting function of the smartphone 12 to transmit the biometric information linked to the worker's identification information to a cloud server 21 located on the Internet 20.

[0038] In addition, various methods can be used to link the measuring device worn by the worker with the ID that identifies the work itself, such as inputting the name and management number of the biosensor used by the worker into a smartphone, reading a two-dimensional or three-dimensional identification code attached to the biosensor using the image recognition function of the smartphone, using an identification code in short-range communication between the smartphone and the biosensor, or other methods selected by the worker using an application on the smartphone. In addition, if the smartphone is not owned by the worker but is lent as part of the system use, the identity of the worker who uses the smartphone can be registered by data input using the smartphone, reading an identification code, using a face authentication system, or other methods.

[0039] The smartphone 12 can receive data, emit sound, and display images. Using these functions, the physical condition evaluation system according to the present embodiment uses the image display unit 17 of the smartphone 12 to provide feedback of the physical condition evaluation result to the worker 10. In addition, the various functions of the smartphone 12 are used as an alarm notification unit 14 for performing an alarm notification function of informing the worker 10 of the risk of developing heatstroke and encouraging him / her to take a break in the heatstroke development risk management system, and an image display unit 17 for performing a function of easily displaying the physical condition evaluation result of each worker 10, and also displaying the evaluation result of the heatstroke development risk for the worker 10 and the entire group to which the worker 10 belongs as a function of the heatstroke development risk management system.

[0040] Cloud server 21 includes a data receiving unit 23 and a data transmitting unit 26, and transmits and receives information via Internet 20. Cloud server 21 also includes an evaluation and determination unit 22 as a data processing unit, which acquires biometric information data of all workers 10 who are targets of the health evaluation system, and calculates a health evaluation index for each worker 10, which indicates whether the worker is in good or bad health.

[0041] In addition, the evaluation and judgment unit 22 of the cloud server 21 calculates a work burden index indicating the amount of work load each worker 11 receives from their work and a heat load index in the heat stroke risk management system, and calculates a heat stroke risk index indicating the degree of risk of each worker 10 developing heat stroke based on these indices. Furthermore, the evaluation and judgment unit 22 can manage the risk of developing heat stroke for groups of workers 10 formed based on the commonality of work content and working environment.

[0042] The heat stroke risk management system, which is also a physical condition evaluation system described in this embodiment, manages the heat stroke risk of each worker 10, and when it is determined that the risk of heat stroke is particularly high, transmits the information and encourages the worker to take measures to reduce the risk of heat stroke. For this reason, the cloud server 21 evaluates and determines the risk of heat stroke, and when the risk of heat stroke is increasing, creates warning information to warn the worker of that fact.

[0043] In addition, the cloud server 21 has a weather information acquisition unit 25, which acquires weather information from an information site that provides weather information via the Internet 20, and can acquire weather conditions at the current time, such as temperature, humidity, and amount of sunlight, in the area where the worker 10 is working, as well as a weather forecast that anticipates changes within the next few hours, thereby enabling weather conditions to be taken into account when assessing the risk of developing heatstroke.

[0044] Furthermore, the cloud server 21 is equipped with a data recording unit 24, which can chronologically record the measurement data of biological information, the physical condition evaluation results, the heatstroke development risk index, the history of creating warning information, and the like, from each worker 10 registered in the heatstroke development risk management system. This makes it possible, for example, to perform an on-time physical condition evaluation of the day based on the results of the physical condition evaluation of each worker 10 up to the current time on that day or up to the previous day, and to manage the risk of developing heatstroke. Also, based on the past evaluation results of the risk of developing heatstroke under similar weather conditions, a more accurate risk evaluation of developing heatstroke can be performed.

[0045] The cloud server 21 is connected via the Internet 20 to a personal computer 31 that serves as a manager information terminal used by a site supervisor 30, who is a manager who supervises the work of the worker 10, who is the person being evaluated, at the construction site. Therefore, the site supervisor 30, who is at the work site where the worker 10 is working, can grasp, by a data receiving unit 33 of the personal computer 31, the biological information data of the worker 10 transmitted from the cloud server 21 at any time, the physical condition evaluation result, the evaluation result of the risk of developing heat stroke, and whether or not warning information has been generated by the evaluation determination unit 22.

[0046] The evaluation and determination unit 22 of the cloud server 21 evaluates the physical condition of the worker 10 at any time based on the heart rate data, acceleration data, and temperature inside the clothing obtained from the biosensor 11 worn by the worker 10. Furthermore, it calculates a work burden index and calculates a heat stroke development risk index for the worker 10 by taking into account the temperature inside the clothing information and the environmental temperature information of the work location obtained via the Internet.

[0047] The specific details of the calculation of the heart rate index, which is an index for evaluating the physical condition of the worker 10, the calculation of the work burden index, and the calculation of the heat stroke risk index and the heat stroke risk assessment performed by the evaluation and judgment unit 22 will be described later.

[0048] The cloud server 21 can correct the assessment results of the risk of developing heatstroke for each individual worker 10 based on historical data as past history information of the worker 10 being evaluated recorded in the data recording unit 24, weather information for the work area acquired by the weather information acquisition unit 25, and environmental information such as changes in various information acquired from workers other than the worker being evaluated who work at the same site as the worker being evaluated, thereby managing the risk of developing heatstroke in a more realistic manner.

[0049] In the physical condition evaluation system exemplified in this embodiment, the evaluation / determination unit 22 is not limited to being provided in the cloud server 21. For example, various functions of the cloud server 21 may be implemented on an administrator information terminal or a management computer of a business establishment, and as long as the functions can be realized, the location or device on which the evaluation / determination unit is implemented is not important.

[0050] The site supervisor 30's personal computer 31 is equipped with an information management unit 32 that manages various information obtained by the biosensors 11 for the workers 10, including the workers 10, who belong to the work site supervised by the site supervisor 30, and whether or not warning information has been generated. The information management unit 32 always keeps track of the latest information on the basis of the information obtained from each worker 10 and whether or not warning information has been generated, which is the basis for evaluating the risk of developing heatstroke, based on the information transmitted from the cloud server 21. The information management unit 32 also outputs the acquired evaluation results of the risk of developing heatstroke for each worker 10 and other environmental information to a display image processing unit 35, which adjusts the screen content displayed on a display device 36, such as a liquid crystal monitor.

[0051] In this way, the site supervisor 30 can grasp the information of the workers 10 working at the work site he supervises, the risk of developing heat stroke, etc., in a unified manner as a whole, or as detailed information on each individual worker, on an easy-to-read screen. Note that the specific screen contents displayed on the display device 36 processed by the display image processing unit 35 need only be able to display the information required by the appropriately configured system in an easy-to-read manner, and therefore a detailed description of the specific contents will be omitted in this specification.

[0052] In the physical condition evaluation system according to the present embodiment, the physical condition evaluation results of the workers 10 are displayed on the display screen of the smartphone 12 carried by the workers 10, but the evaluation results are set so that the site supervisor 30's computer 31 cannot grasp the evaluation results. This is because the site supervisor 30 can take measures to reduce the risk of heat stroke if he obtains the heat stroke risk evaluation results of each worker 10, thereby achieving the purpose of the system, and because the physical condition evaluation results are more likely to be understood by each person as part of self-management, and because the workers 10 tend to dislike having others know their physical condition evaluation results. To whom and how the physical condition evaluation results should be fed back depends on the purpose of the physical condition evaluation system, the position of the evaluator and the manager, etc., and is considered to be appropriately set for each system.

[0053] The site supervisor's computer 31 can check whether the worker 10 has taken measures to prevent the onset of heat stroke by checking changes in the biometric information obtained from the worker 10 after notifying the warning information and receiving confirmation of receipt of the warning information from the worker 10.If the worker 10 has not taken any measures to prevent the onset of heat stroke, the site supervisor can further warn the worker 10 by, for example, repeatedly transmitting the warning information to the worker 10 in question.

[0054] In the above description, an example has been described in which the evaluation and judgment unit 22 of the cloud server 21 generates warning information informing the worker 10 that the risk of developing heatstroke is increasing, but the warning information can also be generated by the information management unit 32 installed in the computer 31 of the site supervisor 30. It is also possible to set both the evaluation and judgment unit 22 and the information management unit 32 to generate warning information. In this way, the computer 31 of the site supervisor 30 who actually supervises the work site generates warning information prior to the judgment result of the evaluation and judgment unit 22 and transmits it to the target worker 10, which may further reduce the risk of developing heatstroke depending on the actual conditions of the work site.

[0055] The warning information generated by the evaluation and judgment unit 22 of the cloud server 21 or the personal computer 31 of the site supervisor 30 is transmitted from the data transmission unit 34 of the personal computer 31 of the site supervisor 30 to the smartphone 12 carried by the worker 10 via a network including a local network such as a wireless LAN and an information carrier of a mobile phone. The warning notification unit 14 of the smartphone 12 that receives the warning information notifies the worker 10 that the risk of developing heat stroke is increasing using various information transmission means such as voice, screen display, lighting or blinking of a lamp, and vibration. The worker 10 who has confirmed the warning information reports that he or she has received the warning information through the touch panel or operation button of the smartphone 12, and takes measures to prevent heat stroke, such as stopping work and taking a rest.

[0056] The smartphone 12 of the worker 10 transmits to the computer 31 of the supervisor 30 a message indicating that the worker 10 has confirmed the warning information and stopped working, and the supervisor 30 can confirm that the worker 10 has taken measures to prevent the onset of heatstroke.

[0057] Furthermore, in the heat stroke risk management system described in this embodiment, the site supervisor 30 transmits the heat stroke risk data at the work site to the smartphone 12 of the worker 10, so that the worker 10 can check the current heat stroke risk at the work site where he or she works. For example, if it is confirmed that the risk of heat stroke of workers other than the worker 10 is high, each worker can take measures to proactively prevent the onset of heat stroke. Also, if it is known that there are other workers who have received warning information about the risk of heat stroke and stopped working, it is expected that the worker will respond more readily to the warning information from the site supervisor 30 addressed to him or her.

[0058] As described above, the smartphone 12 owned by the worker 10 can display the physical condition evaluation result information, such as the current physical condition evaluation result of the worker 10 and the change in physical condition evaluation result over the past few days, in a form that is easy for the worker to understand. An example of the display of the physical condition evaluation result will be described in detail later. The smartphone 12 owned by the worker 11 can display the change in the risk of developing heat stroke up to the present of the worker 10, and related information such as the worker's own heart rate and calories burned acquired by the biosensor 11 on the screen, so that the worker 10 can refer to it. Note that the display form of information other than the physical condition evaluation result is not described in detail in this specification as long as it can display the necessary information in an easy-to-read manner according to the respective display contents and purposes of display.

[0059] The cloud server 21 is also connected to a management computer 41 in the company or business 40 to which the worker 10 belongs via the Internet 20, and transmits the measurement result information of the worker 10 transmitted to the computer 31 of the site supervisor 30 and various information used by the cloud server 21 to judge the risk of developing heat stroke to the management computer 41 of the business 40 in real time. The management computer 41 of the business 40 is equipped with its own data receiving unit 42 and data transmitting unit 43, and is also connected to the computer 31 of the site supervisor 30 via the Internet, and can confirm information such as whether the warning information from the site supervisor 30 to the worker 10 was correctly transmitted, whether the worker 10 took preventive measures against heat stroke, and the like, and can give predetermined instructions as necessary. This makes it possible to effectively back up the avoidance of the risk of developing heat stroke for the worker 10.

[0060] In addition, since the cloud server 21, the personal computer 31 of the site supervisor 30, and the management computer 40 of the business establishment 40 are connected over the Internet 20 environment, it is possible to access the cloud server 21 from the personal computer 31 and the management computer 40, thereby controlling the data processing content in the cloud server 21, updating the judgment program in the evaluation and judgment unit 22, and appropriately retrieving information necessary for heatstroke prevention and management from the cloud server 21.

[0061] In the above explanation, a smartphone is exemplified as a mobile terminal carried by a worker, but the mobile terminal of the worker is not limited to a smartphone, and can be a mobile phone, a tablet device, or even a dedicated small terminal device capable of sending and receiving information specialized for a heat stroke risk management system. Also, as the manager information terminal operated by the site supervisor, various information devices capable of sending and receiving information through a network, displaying data, recording data, etc., such as desktop computers, notebook computers, tablet computers, and small server devices can be used instead of the exemplified personal computers.

[0062] Furthermore, the above explanation describes a form in which warning information is sent from the site supervisor's manager information terminal to the worker's mobile terminal, but if the warning information is generated in the evaluation and judgment section of the cloud server, the system can also be configured to send the warning information directly from the cloud server to the worker's mobile terminal.

[0063] Furthermore, the means of communication between the workers, site supervisors, and the management department within the workplace are not limited to the above examples, and it goes without saying that various types of information communication means for transmitting and receiving data can be used.

[0064] In addition, in the physical condition evaluation system described in this embodiment, the arrangement example of the biosensor 11 that acquires the heart rate, temperature inside the clothes, and movement of the worker 10 is not limited to the method of fixing the biosensor 11 to the undershirt 18 shown in FIG. 2. For example, a method of putting the biosensor 11 in a highly adhesive sheet-like wearing cover and directly attaching it to the chest, or a method of placing the biosensor 11 on the worker's chest using an elastic wearing belt that can hold the biosensor 11 in close contact with the body, etc. can be adopted. However, according to the method of fixing the biosensor 11 to the undershirt 18 worn by the worker 10 as shown in FIG. 2, the worker 10 can acquire necessary information while reducing the special awareness of wearing the sensor compared to the case where the biosensor 11 is worn by other methods. In addition, even if the worker 10 sweats or twists his / her body during work, the biosensor 11 fixed to the undershirt 18 will not come off the body surface of the worker 10, and the wearing position can be maintained substantially unchanged. Therefore, although there may be cases where a part of the heart rate of the worker 10 cannot be acquired as heart rate data, it is possible to avoid a situation in which no heart rate data can be acquired at all for a continuous period of time.

[0065] The location of the biosensor 11 for acquiring the heart rate data and acceleration data of the worker 10 may be the waist, back, upper arms, legs, etc. of the worker 10, in addition to the chest of the worker 10 as described above. Also, in cases where the physical condition of an athlete undergoing training is evaluated, for example, rather than as a system for managing the risk of developing heat stroke with the worker 10 working at a construction site as the subject of evaluation as described in this embodiment, the subject of evaluation may be wearing sportswear, and in this case too, it is most reasonable to place the biosensor 11 on the chest of the wear worn on the upper body.

[0066] In addition, in the physical condition evaluation system according to the present embodiment, the biosensor that can be used as the bioinformation acquisition unit that acquires the heartbeat data, which is the bioinformation of the subject, is not limited to the one that acquires the heartbeat data by detecting the change in electric potential at the electrode unit as exemplified above. For example, various sensors that can detect the heartbeat or pulse of the subject wearing the sensor, such as a sensor that detects the pulse by optically detecting the volume change of the subject's blood vessels, or a sensor that is worn on the wrist and detects the pulsation of the blood vessels as vibration, can be used.

[0067] Furthermore, the part that acquires the heart rate data of the subject and the part that detects the acceleration data indicating the body movement of the subject do not need to be arranged in the same member as in the above example, and may be arranged in separate housings that are physically separated. In detecting the overall body movement of the subject, it is preferable that the acceleration sensor that detects the acceleration data is arranged in a part close to the trunk of the upper body of the subject. Therefore, when a wristwatch-type pulse sensor that is worn on the wrist is used as a pulse sensor that acquires the heart rate data of the subject, it is possible to arrange the acceleration sensor in a separate measuring member and wear it on the upper body of the subject using the collar or breast pocket.

[0068] In addition, the temperature sensor for measuring the ambient temperature of the person being evaluated is preferably one that can measure the temperature inside the clothes of the person being evaluated, such as the biosensor 11 described above. This is because the temperature inside the clothes fluctuates less than the surrounding air temperature and is the closest environmental temperature to the person being evaluated. In contrast, when detecting the pulse and environmental temperature of the person being evaluated using a wristwatch-type sensor, the temperature sensor is exposed and mainly measures the temperature of the surrounding air, so it is preferable to take measures such as estimating and correcting the temperature inside the clothes, which is the closest temperature to the person being evaluated, from the environmental temperature measured by the wristwatch-type biosensor, such as grasping in advance the relationship between the air temperature depending on the distance from the furnace, which is the heat source, and the temperature inside the clothes at that time.

[0069] It is not necessary for the subject to wear a temperature sensor. For example, the temperature obtained from an environmental sensor installed in the work environment may be used as the ambient temperature, or the subject's body surface temperature may be extracted from a thermal image obtained by a non-contact thermometer (such as a thermo camera) and used as the ambient temperature.

[0070] [Physical condition evaluation method] Next, a specific description will be given of a health condition evaluation method in the health condition evaluation system according to this embodiment.

[0071] In the physical condition evaluation system according to the present embodiment, first, the heart rate data of the person being evaluated, detected by the heart rate detection means of the measuring device (biosensor) worn by the person being evaluated, and acceleration data, which is an index showing the body movement of the person being evaluated at the time the heart rate data is obtained, are obtained by a three-dimensional acceleration sensor. A heart rate index, which is the heart rate when the acceleration data is 0, that is, when the person being evaluated is in a resting state, is calculated by extrapolating from the slope of the regression line showing the relationship between the obtained heart rate data and the acceleration data, and physical condition evaluation is performed based on this heart rate index.

[0072] In addition, when the physical condition evaluation system according to the present embodiment is made to function as a management system for managing the risk of developing heat stroke, a work strain index indicating the intensity of the worker's work is calculated based on the heart rate index obtained above, and the temperature inside the worker's clothes (T i ) and the environmental temperature (T0) of the site where the worker is working, the heat load index of the worker is calculated. Then, based on the calculated work burden index and heat load index, a heat stroke development risk index that indicates the risk of developing heat stroke is calculated.

[0073] The work load index W is calculated as follows.

[0074] First, a regression line is calculated from data acquired over a period of a specified time or more (for example, two hours) based on the median heart rate, which is the heart rate data, and the acceleration deviation, which is the acceleration data, obtained in each partial interval using a biosensor worn by the worker being evaluated.The slope of the regression line is set to the heart rate response coefficient αr, and the y-axis intercept is set to the intercept heart rate βr.

[0075] Next, the median heart rate value HR obtained in each partial interval is converted into a standardized heart rate HRs using the following (Equation 1).

[0076] HRs=(αs / αr)(HR-βr)+βs (Equation 1) In the above formula (1), αs is the standard response heart rate coefficient, βs is the standard intercept heart rate, and these are the slope (αs) of the regression line and the y-axis intercept value (βs) in the standard heart rate response model that is thought to show the correlation between standard heart rate data and acceleration data.

[0077] Here, the standard heart rate response model is a heart rate response model created based on large-scale data obtained by measuring a large number of people. It is a model that represents a standard human heart rate response to acceleration (body movement) and can be expressed by various parameters and a specified formula. The large-scale data may be data from a number of workers at the site over the past few days, or may be accumulated data sampled in advance at another site. It is preferable to create a standard heart rate response model based on large-scale data obtained by measuring a large number of workers who are engaged in the same work as the worker. This is a heart rate response model optimized for the work, and is considered to represent a typical heart rate response of the worker engaged in that work. There is no particular rule regarding the number of people on which the large-scale data is based, but the heart rate response can be approximated with higher accuracy if the number of samples is larger. It is preferably 5 people or more, more preferably 50 people or more. There is no particular rule regarding the accumulation period, but it is preferable to obtain data for 2 days or more, more preferably 5 days or more at the same site.

[0078] Thus, the standardized heart rate HR SBy calculating this, it is possible to correct individual differences in calculating the work strain index W from the heart rate data based on the characteristics of each worker being evaluated.

[0079] Furthermore, when the detection rate of heart rate data is low, a workload index can be calculated using only the acceleration data by using a predetermined formula, such as multiplying the acceleration deviation, which is acceleration data, by an appropriate coefficient. Also, an estimated standardized heart rate calculated based on the value of the acceleration deviation and a function (correlation curve) showing the correlation between the heart rate data and the acceleration data calculated from the standard heart rate response model can be used.

[0080] Based on the standardized heart rate and the acceleration deviation thus obtained, the evaluation and determination unit calculates the workload index of the worker. At this time, the evaluation and determination unit determines whether to use the standardized heart rate or the estimated standardized heart rate. In addition, the evaluation and determination unit can determine the corrected heart rate HRc used in the calculation of the workload index by using a correction map created based on the standardized heart rate response model.

[0081] The correction map used shows the correlation between the heart rate data and the acceleration data, and for the data above and below the correlation curve showing the standard heart rate response model, depending on the magnitude of the acceleration data, it is shown whether the standardized heart rate HRs obtained based on the measurement data is used as the corrected heart rate HRc as it is, or whether the value of the correlation curve or the corrected heart rate HRc shown as a default value on the map is used. Note that, as the correction map, a map for use when the heart rate detection rate is high is prepared separately, and by increasing the degree to which the standardized heart rate is adopted when the heart rate detection rate is high, it is possible to calculate the workload index more accurately according to the actual situation.

[0082] Corrected heart rate HR obtained using the correction map c Based on this, the workload index W is calculated as follows:

[0083] First, calculate the corrected heart rate HR using the following formula (Equation 2): cis converted to metabolic equivalents (METs).

[0084] METs=a METs ×HR c +b METs (Formula 2) Here, a METs and b METs is a predefined parameter, which can be determined based on respirometry experiments.

[0085] Next, the metabolic equivalents METs are converted into a work load index W using the following formula (Eq.).

[0086] W=a W ×METs+b W (Formula 3) Here, a W and b W is a predetermined parameter.

[0087] For example, W =0,2,b W =-0.2, the workload can be evaluated as follows: if the workload index W is 0.6 or higher, it is high metabolic rate work, i.e., work that places a heavy burden on the worker; if the value of W is 1 or higher, it is work that requires an extremely high metabolic rate, i.e., work that places a very heavy burden on the worker.

[0088] In addition, the heat load index H is calculated using the following formula 4.

[0089] H=(2.73T i +0.05T0-89.2) / 10 (Equation 4) The environmental temperature data T0 of the work site is obtained as temperature data around the work site acquired by the weather information acquisition unit of the cloud server, or temperature information obtained from a temperature sensor placed in the work site when the worker is working indoors. In the above formula 1, if the heat load index H is less than 0, H = 0. It can be evaluated that a heat load index H of 0.6 or more indicates a relatively high heat load, and a heat load index H of 1 or more indicates an extremely high heat load.

[0090] The heat stroke risk index R is calculated using the following formula 5.

[0091]

number

[0092] Here, a is a numerical value defined according to the heat acclimatization of the worker being evaluated, where a = -1.8 in the case of heat acclimatization and a = -1.3 in the case of no heat acclimatization.

[0093] For the heatstroke risk assessment value R obtained as described above, as an example, if R is less than 0.6, the risk of onset can be judged as low, if R is between 0.6 and 1.0, it is a warning level requiring caution, and if R is 1.0 or more, it is a high risk and dangerous level of heatstroke onset. Note that since it is not possible to verify the actual occurrence of heatstroke, when determining the criteria for judging the risk of heatstroke onset, it should be determined so that the risk of heatstroke onset can be judged as strictly as possible, i.e., on the safe side.

[0094] [Evaluation of physical condition in a high-temperature environment] (1. Changes in heart rate in high temperature environments) In the physical condition evaluation method and heat stroke risk management method according to the present embodiment, the heat load H increases approximately in proportion to the increase in the ambient air temperature of the worker, as shown in the above formula 4. This is based on the classic research of Lind [Journal of Applied Physiology 18, 51 (1963)], and is an assumption that is widely adopted in current heat load evaluations (ISO7243:2017, etc.).

[0095] However, as a result of the inventors' investigations, it was confirmed that when the worker is in a high-temperature environment where the environmental temperature around him, particularly the temperature inside his clothing, exceeds his body temperature (approximately 36°C), the degree to which the thermal stress caused by the rise in environmental temperature affects the increase in heart rate is not proportional. It is known that the increase in heart rate that accompanies an increase in environmental temperature correlates with the increase in core body temperature, and the degree of increase in heart rate can be used to evaluate the heat stress (damage the body receives from the environment).

[0096] Figure 3 shows the measurement results of the fluctuation rate of the worker's inner clothing temperature and heart rate in a high-temperature environment.

[0097] The data shown in Figure 3 is based on approximately 200,000 measurements taken over a 34-day period of time for approximately 10 workers working in a high-temperature building. The workers who were measured worked while wearing the biosensor shown in Figure 2. Here, the relationship between temperature inside clothing and heart rate under light work load (2.5 METs or less) was plotted based on the heart rate data, acceleration data, and temperature inside clothing acquired by the biosensor.

[0098] The "x" marks (symbol 31) in Figure 3 plot the heart rate variability calculated by dividing the median heart rate by the average heart rate (average of all measured data) for each worker at each environmental temperature. The dashed line (symbol 32) is an approximation curve using a sigmoid function.

[0099] As shown in Figure 3, in the region where the inside clothing temperature is between about 30.5°C and about 36.5°C (region 33), the rate of change in heart rate increases monotonically with respect to the inside clothing temperature. In contrast, in the region where the inside clothing temperature is low, below 30.5°C (region 34), and in the high region where the inside clothing temperature is high, above 36.5°C (region 35), the change in heart rate is extremely small even when the inside clothing temperature changes. A similar tendency for change depending on the inside clothing temperature was also observed under a moderate workload. However, the increase in the rate of change in heart rate depending on the inside clothing temperature was smaller the higher the workload.

[0100] An increase in heart rate indicates an increase in heat stress, so in environments where the temperature inside clothing exceeds 36.5°C, the environmental temperature and humidity, WBGT, or temperature inside clothing are not appropriate indicators of heat stress.

[0101] Therefore, the inventors devised a new algorithm for evaluating physical condition in a work environment where the heat load is high and the temperature inside clothing can reach or exceed body temperature, which differs from the conventional evaluation based on the heatstroke risk index R, shown in Equation 5 above.

[0102] (2. Algorithm for assessing physical condition in high temperature environments) A. Basic Concept The heart rate HR of the subject obtained by the biosensor shown in Figure 2 is calculated by multiplying the resting heart rate HR0 by the increase in energy metabolism caused by exercise ΔHR M , increase ΔHR due to static movement S , Increase in ΔHR due to heat stress T , Increase ΔHR due to psychological effects and other factors N Using this, it is expressed as the following Equation 6.

[0103] HR = HR0 + ΔHR M +ΔHR S +ΔHR T +ΔHR N (Formula 6) In addition, ΔHR S and ΔHR N Since the effect of heat stress is small and negligible, the increase in heart rate due to heat stress is ΔHRT ≒HR-(HR0+ΔHR M ) (Formula 7) It can be expressed as follows. Since it is known that changes in core body temperature affect changes in heart rate (considered to be about 20 bpm / ℃ for Japanese people), by grasping the change (increase) in heart rate during heat stress, it is possible to detect changes (rise) in core body temperature, i.e., changes (rise) in heat stress. Furthermore, by applying the heart rate prediction model (Equation 7) to each individual to estimate the predicted heart rate under normal circumstances, and evaluating the difference between the heart rate measured in real time and the predicted heart rate, it is possible to detect abnormal changes in heart rate associated with poor physical condition.

[0104] In the physical condition evaluation method and physical condition evaluation system disclosed in this application, the difference between the heart rate HR of the person being evaluated that is actually measured by a biosensor and the heart rate as a predicted value calculated using the acceleration deviation that indicates the body movement (amount of activity) at the time the heart rate was measured is calculated based on the premise shown in the above formula 7. This difference in heart rate is then determined to be an increase in heart rate caused by heat stress, and the risk of a change in physical condition due to the magnitude of the heat stress imposed on the person being evaluated is evaluated based on the degree of this increase.

[0105] b. Heart rate prediction As described above, in the physical condition evaluation method and physical condition evaluation system shown in this embodiment, a prediction model is created that predicts the heart rate to be measured based on the activity level of the person being evaluated. This prediction model is updated for each physical condition evaluation date that it is used.

[0106] The prediction model for heart rate HR is expressed by the following Equation 8.

[0107]

number

[0108] Acceleration deviation A RMSis the deviation of the acceleration data acquired at the same time as the heart rate data is detected from the biosensor. Since the acceleration data is calculated as the average value ΔA for the past minute, it is calculated as the average value for one minute by the following procedure.

[0109] First, the exponential moving average of the acceleration data in each of the x-axis, y-axis, and z-axis directions {Ax(t)}, {Ay(t)}, and {Az(t)} is calculated using the exponential moving average method, a statistical method, with a time constant of 6 seconds. The time constant is not particularly limited, but may be appropriately determined in the range of, for example, 5 to 10 seconds depending on the characteristics of the body movement and the performance of the acceleration sensor.

[0110] Here, the exponential moving averages in the x-axis, y-axis, and z-axis directions are defined as {Sx(t)}, {Sy(t)}, and {Sz(t)}, respectively.

[0111] Next, the exponential moving average is removed from the acceleration data for each axis to obtain the detrended time series acceleration. For example, for the x-axis, it would be "Ax(t)-Sx(t)".

[0112] Then, for the detrended time series acceleration, calculate the square at each time using the following formula (Formula 9) and calculate the sum.

[0113]

number

[0114] The sum of squares calculated above, "ΔA 2 (t)" average value for each minute "ΔA 2 ave " is calculated. Here, the average value is calculated by dividing by the number of data points. Also, the square mean of the acceleration "ΔA 2 ave "The square root of "ΔA ave " is calculated. This ΔA ave is the speed deviation A RMS In addition, if the data is non-numeric, it will be excluded as an abnormal value.

[0115] Clothes temperature T in The temperature data obtained from the biosensor can be used as is. In addition, the measurement results of the temperature inside the clothes and the fluctuation rate of the heart rate of the worker shown in Figure 3 are reflected in the third term on the right side of the above formula 8, "f(T in Specifically, the temperature inside the clothing T in Upper limit of T max and the lower bound T min and the upper limit T max If it exceeds the upper limit, the lower limit T min If the value falls below this limit, it will be set to a fixed lower limit.

[0116] As an example, based on the measurement results shown in Figure 3, max = 36.5℃, T min = 30.5℃, and the temperature inside the garment T in Between 30.5℃ and 36.5℃, f(T in ) is a monotonically increasing function (e.g., a piecewise linear function), but the temperature inside the clothing T in If the temperature inside the garment is below 30.5℃, or in If the temperature is 36.5°C or higher, they are assumed to be constant for the calculation.

[0117] c. Specific calculation examples First, regarding the first day of application of the physical condition evaluation method according to this embodiment, since there is no past data for the person being evaluated, the value of the following equation 10, which is representative data obtained from past operation examples of the main body evaluation system, is substituted into the above-mentioned equation 8 to create a heart rate model.

[0118]

number

[0119] In the case of the second day or later when the physical condition evaluation according to this embodiment is performed, the parameters used in formula 8 are calculated using the past data of the worker to be evaluated by the following method. Here, the number of past measured data is first confirmed, and the estimation method of the prediction model is changed depending on the number of reliable measured data. This is to avoid obtaining erroneous results when the number of data is small by using the same estimation method as when the number of data is large, and to perform a more appropriate physical condition evaluation even when the number of data is small by creating a prediction model based on the data actually obtained from the worker.

[0120] Figure 4 shows the temperature inside the clothing T in and acceleration deviation A RMS The evaluation map is shown below.

[0121] As shown in FIG. 4, the temperature inside the clothing T in For the data, the 75th percentile (T in (3Q) : The third quartile, ) is the upper and lower boundary 41, and T min = 30.5℃, lower limit 42, T max = 36.5℃ is defined as the upper limit 43. Also, the acceleration deviation A RMS Regarding A, it is considered that the subject is not in a resting state but is performing a static task. RMS = 0.05 is the lower limit 44, and A is considered to be performing moderate or high-level work involving relatively large movements RMS = 0.25 is the middle boundary 45. Note that the acceleration deviation A RMS There is no upper limit on the amount of

[0122] In this way, the temperature inside the clothing in ) and lateral (acceleration deviation A RMS ) into four regions (regions A, B, C, and D in FIG. 4), and the median is calculated for the measurement points included in each of them. Then, the above formula 8 is applied to the four points shown in the following formula 11 using the least squares method.

[0123]

number

[0124] However, if the calculation results in αHR<20 [bpm / G] or βT<2 [bpm / ° C.], the respective parameters are determined using the above-mentioned formula 11.

[0125] d. Heat risk assessment Finally, the worker's risk of heat stress at each measurement point is determined based on the difference between the estimated heart rate at each measurement point calculated based on Equation 8 and the heart rate actually measured by the biosensor.

[0126] FIG. 5 is an image diagram showing the difference between the estimated heart rate and the measured heart rate.

[0127] 5, when a person is under heat stress, the heart rate 52 actually measured by the biosensor is higher than the estimated heart rate 51 obtained by substituting the measurement data at each measurement time point into the above-mentioned formula 8. In the physical condition evaluation according to this embodiment, the risk of heat stress at the measurement time point is evaluated based on the degree of increase in the measured heart rate 52 from the estimated heart rate 51, as indicated by the black arrow in FIG.

[0128] If the evaluation time is t=tk, first, the acceleration deviation A at the previous measurement time t=t[k-1] RMS (60s) [k-1], temperature inside clothes T in [k-1], and the current median heart rate HR (60s) Refer to [k-1].

[0129]

number

[0130] ΔHR calculated using Equation 12 T If [k-1]>10 bpm and ΔHR[k]>15 bpm calculated using Equation 13, it is determined that the user is in a "caution" state due to exposure to high temperature load, and a warning is given to avoid prolonged work.

[0131] Also, the above T' in Substitute the above into Equation 14 and Equation 15 below.

[0132]

number

[0133] ΔHR calculated using Equation 14 T If [k-1]>10 bpm and HR[k-1] is calculated using Equation 13, the median heart rate HR (60s) [k] is HR (60s) If the state of [k]>HR[k-1]+15 bpm continues for a certain period of time, for example for three minutes, it is determined that the heart rate is abnormally high and an "abnormality detected (danger)" state has occurred.

[0134] The judgment result of a "caution" state or an "abnormality detected (danger)" state should be displayed on a mobile device such as a smartphone carried by the worker to alert the worker, and it is preferable to report this to the site supervisor or the department that manages all workers, who will then check whether the worker has taken appropriate action.

[0135] As described above, the physical condition evaluation method and physical condition evaluation system disclosed in the present application can accurately determine the physical condition of each person being evaluated, even if the person being evaluated is placed in a high-temperature environment and is subjected to high heat stress.

[0136] The physical condition evaluation system and physical condition evaluation method shown in this embodiment can be applied as the main physical condition evaluation means for the subject who is assumed to be always in a high temperature environment. In addition, the physical condition evaluation system and physical condition evaluation method disclosed in this embodiment can be combined with a conventional physical condition evaluation system that evaluates the physical condition of the subject based on the central heart rate, and when it can be determined that the subject is in a high temperature environment from the temperature data indicating the ambient temperature of the subject measured by the biosensor, the data processing can be switched as in the above embodiment to perform a more accurate physical condition evaluation.

[0137] The heart rate data of the subject acquired by the biosensor includes the increase in heart rate ΔHR related to each individual poor physical condition. For example, when suffering from an infectious disease such as a cold or influenza, or when there is inflammation in the body, the heart rate increases. This evaluation method can also detect increases in heart rate due to these other factors, making it possible to evaluate changes in physical condition other than those due to thermal factors.

[0138] In the above embodiment, an example was shown in which the physical condition evaluation system disclosed in the present application was installed in a heatstroke risk management system in which the subjects to be evaluated were workers working on construction sites, etc.; however, the system is not limited to the above example and can be installed in a system for only evaluating physical condition, or a biometric information processing system that evaluates the work strain index, physical condition evaluation index, heat stress index, exercise stress index, and other indices of each subject based on acquired biometric information.

[0139] For this reason, it can be used as a means of evaluating physical condition in systems that process a wide range of biometric information with different subjects, measured biometric information, and evaluation purposes, such as managing the physical condition of athletes during training or managing the physical condition of residents in elderly care facilities. [Industrial Applicability]

[0140] The physical condition evaluation method and physical condition evaluation system disclosed in this application can be applied to cases where the subject is in a high-temperature environment and the conventional physical condition evaluation using the heart rate index, which is the median heart rate, cannot provide a correct evaluation, and can perform an accurate physical condition evaluation under heat stress. Therefore, the physical condition evaluation method and system are extremely useful as a physical condition evaluation method and system for evaluating various subjects, including those for evaluating the physical condition of workers at work sites, etc., as a physical condition evaluation method that can be switched to and applied when the ambient temperature of the subject exceeds a certain level, not only when the subject is expected to be under a high heat stress, but also when the subject is expected to be under a high heat stress. [Explanation of symbols]

[0141] 10. Worker (evaluated person) 11 Biometric sensor (biometric information acquisition unit) 11a Data acquisition and transmission unit (acceleration detection means, temperature detection means) 11b Heart rate sensor electrode unit (heart rate detection means) 22 Evaluation and judgment unit (data processing unit)

Claims

1. A heart rate detection means for detecting the heart rate data of the person being evaluated, an acceleration detection means for detecting acceleration data accompanying the movement of the person being evaluated, and a temperature detection means for detecting temperature data indicating the ambient temperature of the person being evaluated are used, Based on the obtained heart rate data, the acceleration data, and the temperature data, a heart rate prediction model is created in advance that indicates the relationship between the subject's body movement, heart rate, and ambient temperature; A computer-implemented physical condition evaluation method characterized in that the physical condition of the person being evaluated is evaluated based on the difference between the heart rate data detected during the evaluation and the predicted heart rate calculated from the heart rate prediction model corresponding to the acceleration data and temperature data acquired during the evaluation.

2. The physical condition evaluation method according to claim 1 , wherein the temperature inside the clothes of the person being evaluated is detected as the ambient temperature of the person being evaluated.

3. The physical condition evaluation method according to claim 1 or 2, wherein an upper limit and a lower limit are set for the ambient temperature used to create the heart rate prediction model of the person being evaluated.

4. A physical condition evaluation method according to any one of claims 1 to 3, wherein the heart rate prediction model is created using an index related to the heart rate data, an index related to the acceleration data, and an index related to the temperature data, which are determined based on past measurement data for the person being evaluated.

5. A health evaluation method according to any one of claims 1 to 4, wherein the risk of the person being evaluated developing heatstroke is determined as a result of the health evaluation.

6. A heartbeat detection means for detecting heartbeat data as biometric information of the subject, an acceleration detection means for detecting acceleration data associated with movement, and a temperature detection means for detecting temperature data indicating the ambient temperature; a data processing unit that creates a heart rate prediction model in advance based on the acquired biological information, which model indicates the relationship between the subject's physical movement, heart rate, and ambient temperature, and calculates the difference between the heart rate data measured during the evaluation and a predicted heart rate calculated from the heart rate prediction model corresponding to the acceleration data and temperature data acquired during the evaluation; A physical condition evaluation system characterized in that the data processing unit evaluates the physical condition of the person being evaluated based on the calculated difference.

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