Method for estimating collective heat load, and system for estimating collective heat load.

Abstract

This invention provides a method for estimating the heat load of a group of people, including the subjects, by using biometric information obtained from multiple subjects to estimate the heat load of the environment in which the subjects are actually placed. [Solution] The collective heat load estimation method disclosed herein is a collective heat load estimation method that estimates the heat load on a group of multiple subjects using a computer operated by a computer program, wherein a representative value of the heart rate in the group is calculated from the heart rate of each of the multiple subjects, and the WBGT for the group is estimated based on the representative value.

Description

[Technical Field]

[0001] This application relates to a method and system for estimating group heat load, which identifies multiple subjects under common environmental conditions as a single group and estimates the heat load of the environment in which the group is located based on biological information obtained from multiple subjects belonging to this group. [Background technology]

[0002] In recent years, with the development of internet connectivity environments such as wireless LANs, advancements in short-range information transmission methods such as Bluetooth (registered trademark), and the widespread use of high-performance mobile devices such as smartphones and small sensor devices capable of measuring bodily data such as body temperature, heart rate, and sweating, health management systems have been put into practical use. These systems transmit biometric information of individuals being evaluated, acquired by sensor devices, to mobile devices connected to the internet, thereby managing the health status of the individuals being evaluated and preventing unforeseen incidents such as accidents caused by poor health.

[0003] As an example of such a health management system, there is a known system for managing the risk of heat-related disorders such as heatstroke, whose risk of onset has become a social problem due to the increasing risk of developing as a result of global warming. For example, a system has been proposed in which a person to be managed wears a housing equipped with an acceleration sensor, an ultraviolet sensor, a GPS receiver, an operation input unit, a display / voice notification unit, and an internet connection unit, and the system grasps the person's activity level and location information such as whether the person is indoors or outdoors, determines the risk of developing heatstroke based on weather conditions such as temperature and humidity obtained via the internet, and prevents the onset of heatstroke by instructing the person to take an appropriate rest according to the determination result (see Patent Document 1). [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2012-210233 [Overview of the Initiative]

Problems to be Solved by the Invention

[0005] In the above conventional heatstroke prevention system, the value of WBGT (wet bulb globe temperature), which is an index for environmentally grasping the magnitude of the heat load on the human body, is obtained by considering the position information of the person to be managed and the estimated indoor / outdoor information based on the amount of ultraviolet rays. Based on the obtained WBGT value, the heat environment at the time of evaluating the person to be managed is judged, and the risk of developing heatstroke is evaluated.

[0006] However, when the WBGT value cannot be measured by a WBGT meter, even if correction is made based on the surrounding weather information from the position information of the person to be managed, it is impossible to accurately grasp the WBGT value, and there is a risk that the risk of developing heatstroke cannot be evaluated based on the actual heat environment of the person to be managed.

[0007] An object of the present application is to solve the problems of the above conventional technology, and to provide a method for estimating the heat load of a group in which a group including subjects is actually placed, and a system for estimating the heat load of a group, using biological information obtained from a plurality of subjects.

Means for Solving the Problems

[0008] In order to solve the above problems, the method for estimating the heat load of a group disclosed in the present application is a method for estimating the heat load of a group consisting of a plurality of subjects by a computer operated by a computer program, characterized in that a representative value of the heart rate in the group is calculated from the heart rates of each of the plurality of subjects, and the WBGT applied to the group is estimated based on the representative value.

[0009] Furthermore, the collective heat load estimation system disclosed in this application is characterized by comprising a measuring device that acquires biological information from each of several subjects belonging to a group, and a heat load estimation unit that calculates a representative value of heart rate in the group based on the biological information acquired from the several subjects and estimates the WBGT for the group. [Effects of the Invention]

[0010] With the above configuration, the group heat load estimation method disclosed in this application can estimate the magnitude of the heat load applied to the group by estimating the WBGT applied to the group based on a representative value of the heart rate in the group calculated from the heart rate of each subject included in the group.

[0011] Furthermore, the heat stress estimation system disclosed in this application includes a measuring device that acquires biological information of subjects forming a group to be evaluated, and a heat stress estimation unit that calculates a representative value of the heart rate in the group from the heart rate of each subject included in the group acquired by the measuring device and estimates the WBGT, thereby enabling the estimation of the magnitude of the heat stress applied to the group from the heart rate of the subjects. [Brief explanation of the drawing]

[0012] [Figure 1] Figure 1 is a block diagram illustrating an example configuration of a heatstroke risk management system, which will be described as an embodiment. [Figure 2] Figure 2 illustrates the configuration of a wristwatch-type sensing unit that acquires the subject's biometric information in the heatstroke risk management system described in this embodiment. [Figure 3] Figure 3 illustrates a heart rate prediction model that calculates the predicted heart rate of the worker being studied. [Figure 4] Figure 4 is the first diagram illustrating the relationship between the magnitude of the differential heart rate and the estimated WBGT as the heat load in the collective heat load estimation method according to this embodiment. [Figure 5]Figure 5 is a second diagram illustrating the relationship between the magnitude of the differential heart rate and the estimated WBGT as the heat load in the collective heat load estimation method according to this embodiment. [Figure 6] Figure 6 shows the relationship between the arm temperature of a worker belonging to a group and the representative value of the difference in heart rate of a group with the same arm temperature. [Figure 7] Figure 7 shows the relationship between representative values ​​of differential heart rate for a group and the temperature around the worker's arm. [Modes for carrying out the invention]

[0013] The collective heat load estimation method disclosed herein is a collective heat load estimation method that estimates the heat load on a group of multiple subjects using a computer operated by a computer program, wherein a representative value of the heart rate in the group is calculated from the heart rate of each of the multiple subjects, and the WBGT for the group is estimated based on the representative value.

[0014] The group heat load estimation method disclosed herein can estimate the heat load on a group as WBGT based on a representative value of the group's heart rate obtained from the heart rates of the subjects belonging to that group. Therefore, it is possible to accurately estimate the magnitude of the heat load actually being experienced by the subjects. In addition, the subjects for whom the load is estimated in the group heat load estimation method disclosed herein do not have to be all the people present in the target environment, but may be a portion of the group selected as a sample. Therefore, it is also possible to estimate the heat load of persons other than the subjects present in the same environment.

[0015] In the group heat load estimation method disclosed herein, it is preferable that the representative value is a representative value of the differential heart rate in the group, calculated from the difference between the measured heart rate and the predicted heart rate of each of the multiple subjects. In this way, the magnitude of the heat load on the group can be estimated based on the change in heart rate caused by the heat load.

[0016] Furthermore, it is preferable to use the median (50th percentile) and 75th percentile values ​​of the differential heart rate of the subjects included in the group as representative values. By doing so, it is possible to accurately estimate the heat load of the group based on the degree of increase in heart rate due to heat load in the group.

[0017] In this case, it is preferable that the predicted heart rate is calculated based on a pre-created heart rate prediction model. By using a heart rate prediction model to calculate the predicted heart rate, the predicted heart rate can be accurately predicted from the characteristics of the subject's heart rate changes.

[0018] Furthermore, it is preferable that the predicted heart rate is calculated using the heart rate prediction model in accordance with the magnitude of the workload on the subject. In this way, the predicted heart rate under a predetermined workload can be easily and accurately calculated.

[0019] Furthermore, it is preferable that, based on the location information of the subject, the subject who is within a predetermined range is set as the group. In this way, even if the location of the subject changes, a group can be formed of subject who are in similar heat load environments, and the collective heat load in that group can be estimated at any time.

[0020] Furthermore, it is preferable that the workers forming the group are predetermined based on the magnitude of the workload placed on the aforementioned individuals. In this way, it is possible to estimate the collective heat load for groups of individuals who are expected to be placed in similar heat-stress environments.

[0021] Another form of the group heat load estimation method disclosed in this application is a group heat load estimation method that estimates the heat load on a group of multiple subjects using a computer operated by a computer program, characterized in that a representative value of the heart rate in the group is calculated from the heart rate of each of the multiple subjects, and the heat load on a specific subject belonging to the group is estimated based on the representative value.

[0022] By doing so, it is possible to accurately estimate the heat stress actually experienced by the subject using representative heart rate values ​​obtained from the group to which the subject belongs.

[0023] In this case, it is preferable to estimate the heat load on the specific subject by correcting the heat load calculated for the environment in which the specific subject is present with the representative value.

[0024] Furthermore, the collective heat load estimation system disclosed in this application comprises a measuring device that acquires biological information from each of several subjects belonging to a group, and a heat load estimation unit that calculates a representative value of heart rate in the group based on the biological information acquired from the several subjects and estimates the WBGT for the group.

[0025] In this way, the collective heat load estimation system disclosed in this application can estimate the heat load applied to a group as WBGT based on a representative value of the heart rate in the group, which is based on the heart rate of each subject.

[0026] In the collective heat stress estimation system disclosed herein, it is preferable to include an information transmission means for receiving the acquired biological information from the measuring device and transmitting it to the heat stress estimation unit. This makes it possible to make the measuring device that acquires the subject's biological information smaller and lighter, and reduces the burden on the subject when wearing the measuring device.

[0027] Furthermore, it is preferable that the heat load estimation unit is installed on a server on the network. This allows the collective heat load estimation system to be operated as a cloud service, making it easy to estimate the heat load in a group, for example, even when the subjects are moving. In addition, by reflecting information from other groups in similar environments in the heat load estimation results as needed, it is possible to estimate the collective heat load more accurately.

[0028] Furthermore, it is preferable that the measuring device is a wristwatch-type biosensor equipped with a heart rate sensor that detects heart rate data worn by the subject and an acceleration sensor that detects acceleration data. This reduces the subject's discomfort when wearing the measuring device.

[0029] Furthermore, it is preferable to further include location information detection means for detecting the location information of the subject, and to detect multiple subjects whose current location is within a predetermined range based on the location information to form the group. In this way, even when the subject moves, the environment in which the subject is located can be detected in real time and the group can be formed dynamically.

[0030] Furthermore, the group heat load estimation system disclosed in this application is characterized by comprising a measuring device that acquires biological information from each of several subjects belonging to a group, and a heat load estimation unit that calculates a representative value of heart rate in the group based on the biological information acquired from the several subjects and estimates the heat load on a specific subject belonging to the group.

[0031] By doing so, it is possible to accurately estimate the heat stress actually experienced by a specific subject belonging to a group, using a representative value of the heart rate within the group to which the subject belongs.

[0032] The collective heat load estimation method and collective heat load estimation system disclosed in this application will be described below with reference to the drawings.

[0033] (Embodiment) [Overall System Configuration] First, we will explain the overall structure of an example of the collective heat load estimation system disclosed in this application.

[0034] The collective heat load estimation system disclosed herein estimates the heat load acting on a group based on heart rate data and acceleration data acquired as biometric information of the worker, who is the subject of the application, from a representative value of the heart rate of the group in which the subject is included. For this reason, the collective heat load estimation system disclosed herein can be easily incorporated into various biometric information processing systems and physical condition evaluation systems that acquire heart rate (pulse) information and acceleration information indicating body movement as biometric information of the subject to evaluate the subject's physical condition, such as the heatstroke risk management system exemplified in this embodiment, and can become part of a system that uses the obtained biometric information to perform various evaluations of the subject's physical condition.

[0035] Therefore, in this embodiment, we will explain and illustrate a heatstroke risk management system that aims to prevent the onset of heatstroke by accurately understanding the changes in the physical condition of individuals working in high-temperature environments due to heat stress, thereby determining and evaluating the risk of heatstroke, and instructing individuals at high risk to take appropriate breaks. In addition to the construction site exemplified, other workplaces where workers perform tasks in high-temperature environments with high ambient temperatures include steel mills with blast furnaces and machine shops with high-temperature furnaces for processing steel plates and materials.

[0036] Figure 1 is a block diagram showing an example of the configuration of each part of a heatstroke risk management system that incorporates the collective heat load estimation system described in this embodiment.

[0037] As shown in Figure 1, the heatstroke risk management system illustrated in this embodiment consists of multiple workers 10 who are the target group, a cloud server 21 on the internet 20 that estimates the heat load of the group including each worker 10 based on their biometric information and evaluates the risk of heatstroke for each worker, a site supervisor 30 who is a manager who oversees the work group including multiple workers 10, and a business establishment 40 that oversees the multiple site supervisors 30, has an overall view of the situation, and operates and maintains the heatstroke risk assessment system.

[0038] It should be noted that the above is a general example based on a typical construction site, and it goes without saying that the overall configuration of the system may take different forms depending on the actual configuration of the site where the heatstroke risk management system is implemented, such as when the site supervisor and the business office are inseparable, or when multiple business offices are involved and the entire construction site is managed on a larger scale.

[0039] In the heatstroke risk management system described in this embodiment, each worker 10 wears a sensing unit 11, which is a wristwatch-type biosensor, as a measuring device for acquiring their own biological information.

[0040] Figure 2 shows the configuration of a wristwatch-type sensing unit that acquires the worker's biometric information in the heatstroke risk management system according to this embodiment.

[0041] As shown in Figure 2, the sensing unit 11 consists of a device body 11a that is worn in contact with the wrist and a watchband-type fastening belt 11b, and its appearance is similar to that of a wristwatch. In addition, the sensing unit 11 illustrated in Figure 2 has a device body 11a that is detachable from the fastening belt 11b for purposes such as easy maintenance.

[0042] The wristwatch-type sensing unit 11 is worn so that the main body 11a is positioned on the outside of the wrist (back of the hand). On the inner surface (back side) of the main body 11a that contacts the wrist, a pulse detection unit is positioned (not shown) that can be pressed against the outside of the wrist of the worker 10 to detect the pulse. Inside the main body 11a, a three-dimensional acceleration sensor is positioned to detect the movements of the worker 10, and the main body 11a also has a transmission unit that transmits the measured biometric information to a smartphone 12, which is a mobile terminal carried by the worker 10.

[0043] The sensing unit 11 and the smartphone 12 held by the worker 10 are constantly connected via short-range communication such as Bluetooth (registered trademark), and various information such as heart rate data and acceleration data of the worker 10 acquired by the sensing unit 11 is sent to the smartphone 12 as needed.

[0044] Furthermore, connection electrodes (not shown) are provided on the side of the main body 11a of the sensing unit 11 to supply power to a secondary battery located inside the main body 11a and enable data exchange with the internal memory elements. When placed on a dedicated cradle, the operating battery of the sensing unit 11 can be charged and data can be exchanged with a personal computer or the like.

[0045] The wristwatch-type sensing unit 11 used as a measuring device in the heatstroke risk management system described in this embodiment does not have a temperature sensor to detect ambient temperature. Instead, the heat load on the worker is estimated using the collective heat load estimation method disclosed in this application, and the risk of heatstroke is evaluated. While some wristwatch-type measuring devices have a built-in temperature sensor that can measure ambient temperature, these devices are worn on the wrist of the subject and are therefore exposed to the outside. Unlike medical patch-type measuring devices that are attached to the chest of the subject, for example, they cannot measure the body surface temperature, which is the temperature inside the clothing. Thus, even if a measuring device is equipped with a temperature sensor, if the temperature of the subject's body surface cannot be measured due to the location or condition of the device, the heat load on the worker can be estimated using the collective heat load estimation method disclosed in this application, and the risk of heatstroke can be evaluated more accurately.

[0046] The wristwatch-type measuring device 10 shown in Figure 2 includes what is known as a smartwatch, which has an image display device on the surface of the device body that displays the current time and various other information. Some smartwatches are equipped with an optical heart rate sensor that measures heart rate based on infrared absorption, and others have communication functions that utilize mobile phone carriers. By using such a smartwatch, biometric information such as heart rate information and acceleration information acquired by the measuring device 10 can be directly transmitted to a cloud server via the internet, which may eliminate the need for the worker 10 to carry a smartphone 12, as described later. Thus, when the heatstroke risk management system is configured not to include a mobile terminal such as a smartphone 12, it is preferable to have the display device of the smartwatch function as a warning notification unit 14 (described later) that notifies the worker 10 of an alert.

[0047] The smartphone 12 is equipped with a data receiving unit 15 and a data transmitting unit 16, and is always connected to the Internet 20 as a network environment via a wireless LAN or a mobile phone information carrier. In the heatstroke risk management system illustrated in this embodiment, the biometric information of the workers 10 acquired by the sensing unit 11 is linked to the identification data of each worker 10 by the smartphone 12, and the smartphone 12 also has a target information transmitting unit 13, and uses the data transmission function of the smartphone 12 to transmit the biometric information, linked to the identification information of the workers 10, to a cloud server 21 located on the Internet 20.

[0048] Furthermore, various methods can be used to link the measuring device worn by the worker with an ID that identifies the worker. These methods include entering the name and management number of the biosensor used by the worker into a smartphone, reading a 2D or 3D identification code attached to the biosensor using the smartphone's image recognition function, using the identification code in short-range communication between the smartphone and the biosensor, or the worker selecting the method using an application on their smartphone. In addition, if the smartphone is not the worker's personal property but is lent out as part of the system, the worker using the smartphone can be identified using data input via the smartphone, reading an identification code, using a facial recognition system, or other methods.

[0049] The smartphone 12 inherently possesses the functions of sending and receiving data, emitting sound, and displaying images. Utilizing these functions, the heatstroke risk management system according to this embodiment uses the image display unit 17 of the smartphone 12 to provide feedback to the worker 10 regarding the heatstroke risk assessment results. Furthermore, each function of the smartphone 12 is used in the heatstroke risk management system as a warning notification unit 14 to communicate the risk of heatstroke to the worker 10 and encourage them to take a break, as well as as an image display unit 17 to display the results of each worker 10's own physical condition assessment, and, as a function of the heatstroke risk management system, to display the heatstroke risk assessment results for the worker 10 and the entire group to which the worker 10 belongs in an easy-to-understand manner.

[0050] The cloud server 21 is equipped with a data receiving unit 23 and a data transmitting unit 26, and exchanges information via the internet 20. The cloud server 21 is also equipped with an evaluation and judgment unit 22 as a data processing unit, which acquires biometric data of all workers 10 who are subject to the heatstroke risk management system, and performs a risk assessment of the likelihood of each worker 10 developing heatstroke.

[0051] The evaluation and judgment unit 22 of the cloud server 21 calculates a work burden index, which indicates the magnitude of the load each worker 10 is subjected to by their work, and an index of the heat stress experienced by the workers, in the heatstroke risk management system. Based on these indices, it calculates a heatstroke risk index, which indicates the degree of risk that each worker 10 is at risk of developing heatstroke. Furthermore, the evaluation and judgment unit 22 can manage the risk of developing heatstroke for groups of workers 10 formed based on commonalities in work content and work environment.

[0052] In the heatstroke risk management system described in this embodiment, the risk of heatstroke for each individual worker 10 is managed, and if it is determined that the risk of heatstroke is particularly high, the information is transmitted to encourage the worker to take measures to reduce the risk of heatstroke. To this end, the cloud server 21 evaluates and determines the risk of heatstroke, and if the risk of heatstroke is high, it creates warning information to alert the worker accordingly.

[0053] Furthermore, the cloud server 21 has a heat load estimation unit 25, which calculates a differential heart rate, which is the difference between the measured heart rate obtained from heart rate data acquired from workers belonging to a set group and a pre-determined predicted heart rate, in order to calculate a representative value of the heart rate in that group, and estimates the heat load on the group as WBGT based on this representative value of heart rate. In addition, the heat load estimation unit 25 uses the representative value of the heart rate in the group to estimate the heat load on a specific subject belonging to the group.

[0054] The methods for estimating the heat load on a group and the methods for estimating the heat load on specific workers belonging to a group will be described in detail later.

[0055] Furthermore, the cloud server 21 is equipped with a data recording unit 24 that can record, in chronological order, measurement data of biometric information from each worker 10 registered in the heatstroke risk management system, physical condition evaluation results, heatstroke risk index, history of warning information creation, and estimated results of collective heat load. This allows, for example, on-time physical condition evaluations to be performed on the day based on the results of physical condition evaluations up to the present time or up to the previous day for each worker 10, and on the management of the risk of heatstroke onset. In addition, based on past collective heat load estimation results, a more accurate assessment of the risk of heatstroke onset can be performed based on the evaluation results of the risk of heatstroke onset for workers subjected to similar heat loads.

[0056] The cloud server 21 is connected via the internet 20 to a personal computer 31, which serves as an administrator information terminal used by the site supervisor 30, who is the manager supervising the work of the worker 10 at the construction site. As a result, the site supervisor 30, who is at the work site where the worker 10 is working, can receive data on the worker 10's biometric information, health condition evaluation results, heatstroke risk evaluation results, estimated heat stress load for a given group, and whether or not warning information has been generated by the evaluation and judgment unit 22, all of which are transmitted from the cloud server 21 in real time via the data receiving unit 33 of the personal computer 31.

[0057] The evaluation and judgment unit 22 of the cloud server 21 continuously evaluates the physical condition of worker 10 based on heart rate data, acceleration data, and estimated results of the collective heat load in the group to which the worker belongs, obtained from the biosensor 11 worn by worker 10. Furthermore, it calculates the heatstroke risk index for worker 10 based on the workload index and the heat load index, which indicates the magnitude of the heat load.

[0058] In the heatstroke risk management system illustrated in this embodiment, the evaluation and judgment unit 22 is not limited to the cloud server 21. For example, the various functions of the cloud server 21 may be implemented on an administrator information terminal or a management computer at a business office, and the location and equipment on which the evaluation and judgment unit is implemented are not limited as long as the functions can be realized.

[0059] The personal computer 31 of the site supervisor 30 is equipped with an information management unit 32 that manages whether various types of information and warning information have been generated for workers 10 belonging to the work site supervised by the site supervisor 30, including the workers 10, obtained from the biosensors 11. Based on information transmitted from the cloud server 21, the information management unit 32 constantly keeps up-to-date information that serves as the basis for evaluating the risk of heatstroke onset, such as whether information and warning information have been generated from each worker 10. The information management unit 32 also outputs the evaluation results of the heatstroke onset risk for each worker 10 and other environmental information to the display image processing unit 35, and the display image processing unit 35 adjusts the screen content displayed on a display device 36 such as an LCD monitor.

[0060] In this way, the site supervisor 30 can grasp information about the workers 10 working at the work site he supervises, including the risk of heatstroke, either as a whole in a unified manner or as detailed information for each individual worker, on an easy-to-view screen. Regarding the specific screen content displayed on the display device 36 processed by the display image processing unit 35, it is sufficient if the information required by the system is displayed in an easy-to-view manner; therefore, a detailed explanation of the specific screen content is omitted in this specification.

[0061] In this embodiment of the heatstroke risk management system, the health assessment results of worker 10 are displayed on the screen of the smartphone 12 held by worker 10, but the site supervisor 30's PC 31 is configured so that the assessment results cannot be viewed. This is because, if the site supervisor 30 obtains the heatstroke risk assessment results for each worker 10, he can take measures to reduce the risk of heatstroke, thus achieving the system's objective. Furthermore, health assessment results are something that each individual should be aware of as part of their self-management, and workers 10 tend to dislike having their health assessment results known to others. It should be noted that who should receive feedback on the health assessment results and how should it be done vary depending on the purpose of the collective heat environment estimation system, the roles of the evaluators and managers, etc., and should be set appropriately for each system.

[0062] On the site supervisor's computer 31, after notifying the worker 10 of the warning information, the supervisor can check whether the worker 10 has taken measures to prevent heatstroke by receiving changes in biometric information from the worker 10 and confirmation from the worker 10 that the warning information has been received. If the worker 10 has not taken measures to prevent heatstroke, the supervisor can take further action to remind the worker 10, such as repeatedly sending the warning information to the worker 10.

[0063] In the above explanation, we described an example in which the evaluation and judgment unit 22 of the cloud server 21 generates warning information informing worker 10 that the risk of developing heatstroke is high. However, the warning information can also be generated by the information management unit 32 installed on the site supervisor's PC 31. It is also possible to configure both the evaluation and judgment unit 22 and the information management unit 32 to generate the warning information. By doing so, the warning information can be generated from the site supervisor's PC 31, who is actually supervising the work site, prior to the judgment result from the evaluation and judgment unit 22, and transmitted to the worker 10 in question. This may allow for a further reduction in the risk of developing heatstroke depending on the actual conditions at the work site.

[0064] Warning information generated by the evaluation and judgment unit 22 of the cloud server 21, or by the site supervisor's PC 31, is transmitted from the data transmission unit 34 of the site supervisor's PC 31 to the smartphone 12 worn by the worker 10 via a local network such as a wireless LAN or a network including a mobile phone information carrier. The warning notification unit 14 of the smartphone 12, upon receiving the warning information, notifies the worker 10 that their risk of developing heatstroke is high, using various means of information transmission such as voice, screen display, lamp illumination or flashing, and vibration. The worker 10, upon confirming the warning information, reports that they have received the warning information via the touch panel or operation buttons of the smartphone 12, and takes measures to prevent heatstroke, such as interrupting work and taking a rest.

[0065] The worker's smartphone 12 sends a message to the supervisor's computer 31 indicating that the worker 10 has checked the warning information and stopped working, allowing the supervisor 30 to confirm that the worker 10 has taken measures to prevent heatstroke.

[0066] Furthermore, in the heatstroke risk management system illustrated in this embodiment, the site supervisor 30 transmits heatstroke risk data from the work site to the worker 10's smartphone 12, allowing the worker 10 to check the current heatstroke risk at the work site where they are working. For example, if they can see that the heatstroke risk of other workers is high, each worker can take proactive measures to prevent heatstroke. Also, if they know that other workers have stopped working after receiving heatstroke risk warnings, they are more likely to respond promptly to warnings sent to them by the site supervisor 30.

[0067] As mentioned above, the smartphone 12 owned by worker 10 can display information on worker 10's current physical condition assessment results and the trend of physical condition assessment results over the past few days in a format that is easy for the worker to understand. An example of how these physical condition assessment results are displayed will be described in detail later. In addition, the smartphone 12 owned by worker 11 can display relevant information on the screen, such as changes in worker 10's risk of developing heatstroke up to the present, and their own heart rate and calories burned, acquired by the biosensor 11, which worker 10 can refer to. As for the display format of information other than the physical condition assessment results, it is sufficient if the necessary information is displayed in an easy-to-read manner according to the content and purpose of each display, so a detailed explanation is omitted in this specification.

[0068] The cloud server 21 is also connected via the internet 20 to the management computer 41 within the company or business office 40 to which the worker 10 belongs. The cloud server 21 transmits in real time to the management computer 41 of the business office 40 the measurement results information of the worker 10 sent to the site supervisor 30's PC 31, as well as various information used by the cloud server 21 to determine the risk of heatstroke. The management computer 41 of the business office 40 is equipped with its own data receiving unit 42 and data transmitting unit 43, and is also connected to the site supervisor 30's PC 31 via the internet. It can check whether warning information was correctly transmitted from the site supervisor 30 to the worker 10, whether the worker 10 took heatstroke prevention measures, and can issue instructions as necessary. This effectively supports the avoidance of the risk of heatstroke for the worker 10.

[0069] Furthermore, since the cloud server 21, the site supervisor's PC 31, and the management computer 40 of the business office 40 are connected via the Internet 20, the PCs 31 and the management computer 40 can access the cloud server 21, allowing them to control the data processing on the cloud server 21, update the judgment program in the evaluation and judgment unit 22, and retrieve information necessary for heatstroke prevention and management from the cloud server 21 as needed.

[0070] In the above explanation, a smartphone was used as an example of a mobile device to be equipped by a worker. However, the mobile device used by a worker is not limited to a smartphone. Mobile phones, tablet devices, and even dedicated small terminal devices capable of sending and receiving information specifically for heatstroke risk management systems can be used. Furthermore, as an administrator information terminal operated by the site supervisor, various information devices capable of sending and receiving information, displaying data, and recording data over a network can be adopted, such as desktop computers, laptop computers, tablet computers, and small server devices, in addition to the example of a personal computer.

[0071] Furthermore, while the above description explains a system where warning information is sent from the site supervisor's administrator terminal to the worker's mobile terminal, if the warning information is generated by the evaluation and judgment unit on 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.

[0072] Furthermore, it goes without saying that the means of communication connecting workers, site supervisors, and management departments within the business are not limited to those exemplified above, but can utilize various information and communication methods for sending and receiving data.

[0073] [Method for estimating collective heat load] Next, the group heat load estimation method disclosed in this application will be described. In the group heat load estimation method described in this embodiment, the difference in heart rate is calculated for each of the multiple workers included in the target group, as the difference between the measured heart rate and the predicted heart rate. The heat load is then estimated by determining the magnitude of the heat load in the group as WGBT based on a representative value of the difference in heart rate in the group.

[0074] Here, the individuals whose biometric information is acquired to calculate the differential heart rate do not need to be all members of the target group from which heat stress is estimated; they can be sampled representatives. In this case, it also becomes possible to estimate the heat stress experienced by individuals other than those whose biometric information has been acquired within the target group from which heat stress is estimated.

[0075] First, several workers 10 are selected to form a target group for which the heat load of the workers 10 included will be estimated.

[0076] The formation of a group can be done each time the group heat load is estimated, for example, by using the location information detection function of the smartphone 12 that each worker 10 is carrying to pick out workers 10 located within a certain range.

[0077] Furthermore, groups can be formed in advance based on the expected workload from each worker's tasks and the expected heat load from commonalities in the work environment.

[0078] The heat stress estimation unit 25 in the heatstroke risk management system calculates the actual heart rate from heart rate data acquired by sensing units 11 worn by each worker 10 and received by a cloud server via a smartphone 12.

[0079] In the collective heat stress estimation system incorporated into the heatstroke risk management system described in this embodiment, the heart rate is measured every minute during a predetermined partial period i (= every minute: hh:mm:00) (HR). (min) This is recorded as [i]. If the sensing unit 11 is unable to correctly acquire the worker's heart rate data and the value of HR(min)[i] is clearly abnormal, the data will be deleted.

[0080] The heat stress estimation unit 25 calculates the measured actual heart rate HR as shown in equation (1) below. (min) [i] and the predicted heart rate (HR) for the worker in question pred The difference in heart rate δHR[i], which is the difference from [i], is calculated.

[0081] δHR[i]=HR (min) [i]-HR pred [i] (1) Predicted heart rate (HR)pred [i] is determined using a heart rate prediction model that shows the relationship between heart rate and workload when no heat stress is applied to the worker 10, which has been determined in advance.

[0082] Figure 3 shows an example of a heart rate prediction model.

[0083] As shown in Figure 3, the heart rate prediction model uses acceleration deviation A, which indicates the magnitude of the workload in the worker 10, the subject of the study. RMS (G) shows the relationship between the measured heart rate data and the central heart rate HR (bpm) 51.

[0084] The following explains how to obtain a specific heart rate prediction model.

[0085] First, preprocessing is performed on the heart rate data and acceleration data to calculate the workload index.

[0086] The preprocessing of heart rate data is performed by calculating the median heart rate from the heart rate data detected by the sensing unit 11 worn by the worker 10.

[0087] More specifically, if the heart rate waveform detection rate for a sub-interval is 50% or higher, the central heart rate (HR) is obtained by converting the heart rate data acquisition interval included in the sub-interval to the heart rate per sub-interval (for example, per past minute).

[0088] For acceleration data obtained from the acceleration sensor, the average value ΔA over the past minute is calculated using the following procedure.

[0089] 1) Exponential moving average of data with unequal time intervals For acceleration data {Ax(t)}, {Ay(t)}, and {Az(t)} in the x, y, and z axes, respectively, the exponential moving average method, a statistical technique, is used to calculate the exponential moving average of the acceleration data in each axis direction, with a time constant of 10 seconds. The time constant is not particularly limited, but can be appropriately determined within the range of 5 to 10 seconds, depending on the performance of the acceleration sensor.

[0090] Here, the exponentially weighted moving averages in the directions of the x-axis, y-axis, and z-axis are denoted as {Sx(t)}, {Sy(t)}, and {Sz(t)}, respectively.

[0091] 2) Removal of exponentially weighted moving average Remove the above-mentioned exponentially weighted moving average from the acceleration data of each axis to obtain the detrended time-series acceleration. For example, in the case of the x-axis, it becomes "Ax(t) - Sx(t)".

[0092] 3) Calculation of sum of squares For the detrended time-series acceleration, calculate the square at each time using the following formula and find the sum. ΔA 2 (t)=[Ax(t)-Sx(t)] 2 + [Ay(t)-Sy(t)] 2 +[Az(t)-Sz(t)] 2 4) Average acceleration per minute Calculate the average value "ΔA 2 (t)" per minute of the sum of squares "ΔA 2 ave " obtained above. Here, it is averaged by dividing by the number of data points. Also, calculate the square root "ΔA 2 ave " of the root mean square acceleration "ΔA ave ". Here, ΔA ave is the acceleration deviation A RMS .

[0093] 5) Removal of outliers Regarding the heart rate data obtained from the heartbeat data, remove non-numeric data, data with a heart rate of 40 or less, and data with a heart rate of 180 or more as outliers.

[0094] Also, regarding the acceleration data, exclude non-numeric data and data where "ΔA is 0.05 or less, or 0.55 or more" as outliers.

[0095] The central heart rate (HR) (bpm) and the acceleration deviation A, which represents the workload, were determined in this way. RMS Figure 3 shows the heart rate prediction model 51, which illustrates the relationship with (G). The heart rate prediction model 51 shows the change in the heart rate of worker 10 when a predetermined workload is applied.

[0096] In the diagram showing the relationship between central heart rate and acceleration deviation, the slope of the portion where the central heart rate increases in proportion to the magnitude of the acceleration deviation, i.e., the portion shown as 52 in Figure 3, is the slope angle α in the heart rate prediction model for the worker, and the value of central heart rate 53 when the acceleration deviation is 0, representing a state where no work load is applied, is called the intercept heart rate HP0. The predicted heart rate of the worker is then calculated using the obtained slope α and intercept heart rate HR0 values.

[0097] Furthermore, it is desirable that the heart rate model measurements be performed when the worker 10 is not subjected to heat stress. Here, an environment without heat stress can be defined as one where the WBGT is 25°C or lower.

[0098] Furthermore, the workload in the heart rate prediction model is the acceleration deviation A shown in Figure 3. RMS In addition to using (G), metabolic equivalents (METs) can also be used.

[0099] In this way, using the pre-created predicted heart rate model, the predicted heart rate for determining the differential heart rate can be calculated.

[0100] Specifically, the resting energy consumption in a minute-by-minute segment of the watch-type sensing unit 11 while the worker 10 is at rest is measured by BsEn (min) [i] The activity energy of each minute-by-minute sub-interval while worker 10 is active is expressed as AcEn (min) [i] is the estimated METs value, which is the value of the workload applied to the worker at time i. (min) W [i] is METs(min) W [i]=AcEn (min) [i] / BsEn (min) [i]+0.2 It can be expressed as follows.

[0101] When applied to the predicted heart rate model shown in Figure 3, the predicted heart rate (HR) pred [i] can be found using the following equation (2).

[0102]

number

[0103] Therefore, the above equation (1) δHR[i]=HR (min) [i]-HR pred [i] (1) From this, the differential heart rate δHR[i], which is the increase in heart rate, can be calculated.

[0104] Furthermore, to exclude abnormal outliers, data where δHR[i] < -5 are excluded.

[0105] And, at a certain time interval T loc (For example, T loc Every 30 minutes, the differential heart rate δHR[i] is calculated for all workers in the group, and the 50th percentile value of the differential heart rate is ΔHR. 50% (Median) and the 75th percentile value ΔHR 75% To find out.

[0106] Figure 4 shows the relationship between the difference in heart rate at the 50th percentile and the WBGT value.

[0107] Figure 5 also shows the relationship between the difference heart rate value at the 75th percentile and the WBGT value.

[0108] Figure 4 shows the difference in heart rate at the 50th percentile, ΔHR. 50% The relationship between WBGT (indicated by 61) and the difference in heart rate at the 75th percentile, ΔHR, as shown in Figure 5. 75%Using the relationship between and WBGT (indicated by 71), the heat load on the group, corresponding to the following conditions, is estimated as a WBGT value.

[0109] (Condition 1) ΔHR 75% For ≤1.5 bpm [Estimated result] The estimated WBGT value was determined to be 28°C or lower. If more detailed values ​​are needed, use the WBGT values ​​for the surrounding environment. (Condition 2) 1.5bpm<ΔHR 75% For ≤4.2 bpm [Estimated result] The estimated WBGT value is set to 29°C. (Condition 3) 4.2bpm<ΔHR 75% ≤5.2 bpm, or, 1.0 bpm < ΔHR 50% If ≤ 1.5 pm [Estimated result] The estimated WBGT value is set to 30°C. (Condition 4) 5.2bpm<ΔHR 75% ≤5.8 bpm, or, 1.5 bpm < ΔHR 50% For ≤2.5 bpm [Estimated result] The estimated WBGT value is set to 31°C. (Condition 5) 5.8 bpm < ΔHR 75% ,or, 2.5 bpm < ΔHR 50% For ≤3.5 bpm [Estimated result] The estimated WBGT value is set to 32°C. (Condition 6) 3.5bpm<ΔHR 50% in the case of [Estimated result] The estimated WBGT value is set to 33°C.

[0110] In the case of (Condition 1), the estimated WBGT value inferred from the differential heart rate summation value ΔHR is 28°C or less. However, since a more detailed value cannot be inferred from heart rate changes alone, it is preferable to use the WBGT of the surrounding environment measured by a WBGT meter placed in or near the work area. In the case of (Condition 6), since 33°C is the upper limit of the WBGT risk assessment, there is no need to estimate values ​​above 33°C, and the estimated WBGT value is set to 33°C when the differential heart rate summation value ΔHR is above a certain level.

[0111] From the estimated collective heat load values ​​as described above, the heat load index H applied to each worker is determined.

[0112] Furthermore, acceleration deviation A, which indicates the body movements of worker 10 as described above. RMS (G), or based on the value of the metabolic equivalent METs, calculate the workload index W for worker 10 and the heatstroke risk index R, R = (aH - W) / a (4) We will seek it as follows.

[0113] In formula (4) above, a is a value determined in accordance with the heat acclimatization of the 10 workers being evaluated, where a = -1.8 if heat acclimatization is present and a = -1.3 if heat acclimatization is not present.

[0114] As an example, the heatstroke risk assessment value R obtained as described above can be used to determine the risk of developing heatstroke. For example, if R is less than 0.6, the risk of developing heatstroke is low; if R is 0.6 or higher but less than 1.0, it is a warning level requiring caution; and if R is 1.0 or higher, it is a high risk level where heatstroke is likely to occur. However, since it is not possible to verify the actual occurrence of heatstroke, when determining the criteria for judging the risk of developing heatstroke, the criteria should be determined to allow for a stricter assessment of the risk of developing heatstroke, that is, to be on the safe side.

[0115] In this way, depending on the magnitude of the risk of heatstroke for each worker 10 as evaluated by the evaluation and judgment unit 22, the worker 10 themselves, or the site supervisor 30 acting as the supervisor, can take measures such as stopping work and taking a break, reducing the workload, or taking measures to lower the temperature inside their clothing to reduce the heat load, thereby effectively preventing the onset of heatstroke.

[0116] Thus, by employing the collective heat load estimation method disclosed in this application, the heatstroke risk management system exemplified in this embodiment can accurately assess the risk of heatstroke in a subject, even if the sensing unit, which is a measuring device that acquires the biological information of the subject (worker), is not equipped with a temperature sensor that measures the heat load environment applied to the subject, and can prevent the onset of heatstroke.

[0117] Furthermore, the collective heat load estimation method disclosed in this application, as described above, can accurately determine the magnitude of the heat load applied to a worker from changes in the subject's heart rate. Therefore, even when a medical pad-type device, which is attached to the subject's chest, is used as a measuring device to acquire the subject's biological information, and the measuring device is equipped with a temperature sensor that allows for the measurement of temperature data of the temperature inside the clothing, which is close to the subject's body temperature, it may be possible to accurately determine the magnitude of the heat load applied to the subject by using the value of the heat load applied to the group estimated by the collective heat load estimation method disclosed in this application in conjunction with the acquired temperature data.

[0118] Furthermore, even when workers are located far apart and no WBGT meters are placed near their work sites, such as in a system for evaluating the risk of heatstroke among workers performing maintenance on high-voltage power lines strung throughout a mountainous area, the magnitude of the heat load on each worker can be estimated as WBGT. Therefore, by using a health management system like the conventional technology described above, which assumes that the heat load in work environments such as factories and construction sites can be grasped as WBGT, it is possible to manage the health of workers working over a wide area or the health of individuals in environments where there are no nearby WBGT meters.

[0119] As described in the above embodiment, the group to be estimated in the collective heat load estimation method disclosed in this application can be formed in advance based on location information, or it can be formed in advance based on certain criteria such as the magnitude of the heat load estimated from the worker's working environment and the magnitude of the work load expected to be applied to the worker, assuming that the heart rate of the worker changes due to the heat load imposed from the surrounding environment such as the weather environment in which the worker is working, and the workload which differs depending on the work content.

[0120] In this case, to more accurately estimate the heat stress load on a group, it is desirable to use a group of at least 5-6 people, and a group of 10 or more people is even preferable. Generally, it is known that a 20 (bpm) increase in heart rate is associated with a 1°C increase in core body temperature. Furthermore, since the individual difference in heart rate under almost identical conditions is about ±10 (bpm), the measurement accuracy based on the number of measurement data (n) is ±10 / n 1 / 2 Therefore, if the number of workers n in the group is 10, the accuracy of the core temperature is approximately ±0.15℃, and if n is 100, the measurement accuracy of the core temperature is approximately ±0.05℃.

[0121] In the above embodiment, the heat load on the group was estimated as WBGT, but various methods can be considered for expressing the heat load estimated by the group heat load estimation method disclosed herein, such as outputting various estimated values ​​required by a physical condition evaluation system that reflects the estimated heat load, or outputting the magnitude of the heat load by rank.

[0122] Furthermore, in the above embodiment, the difference in heart rate, which is the difference between the measured heart rate and the predicted heart rate, was calculated for each subject, and the magnitude of the heat load acting on the group was estimated from a representative value of the difference in heart rate for the group. However, it is also possible to calculate a representative value of the group's heart rate from the actual heart rate of each subject, and estimate the magnitude of the heat load acting on the group based on past data, etc.

[0123] Furthermore, in the above embodiment, the predicted heart rate of the subject was calculated using the heart rate prediction model illustrated in Figure 3 when calculating the differential heart rate. However, the calculation of the predicted heart rate is not limited to using a heart rate prediction model that shows the relationship between the magnitude of the work load and the heart rate when the subject is not subjected to heat stress. For example, the predicted heart rate can be calculated from various methods that calculate the heart rate when the subject performs a predetermined task when not subjected to heat stress, such as creating a data table of the subject's heart rate according to the work conditions and determining the predicted heart rate according to the work conditions at the time of calculation.

[0124] Furthermore, in the above embodiment, the WBGT value as a heat load was estimated by using both the 50th percentile value (median), which is considered a standard representative value, and the 75th percentile value, which is shown as a stricter value considering risk, as representative values ​​for the group of differential heart rates, which is the difference between the measured heart rate and the predicted heart rate. However, the representative value used when determining the group heat load is not limited to using both the 50th percentile value and the 75th percentile value as in the above embodiment. It is also possible to use only the 50th percentile value, only the 75th percentile value, or even the 40th percentile or 60th percentile value, and to verify the results of actually estimating the heat load on the group and adopt a more appropriate representative value that corresponds to the estimation conditions.

[0125] [Estimation of heat stress on specific workers belonging to a group] The inventors used a physical condition evaluation system that acquires workers' heart rate and acceleration information as biometric data, as used in the aforementioned collective heat load estimation system, to verify the heat load on 2405 workers. They compared the environmental WBGT values ​​acquired by WBGT meters placed around the workers with the WBGT values ​​estimated from the collective heat load obtained from the workers' heart rate information described above. As a result, it was confirmed that even when the environmental WBGT increased, the median heart rate of the group obtained from the workers did not increase much, and that the environmental WBGT may not adequately capture the actual magnitude of the heat load on the workers. On the other hand, when the temperature around the arm measured by the sensing unit 11 worn on the worker's arm increased, the median heart rate of the group including this worker tended to increase, and it was confirmed that the temperature around the arm had a higher degree of agreement with the actual magnitude of the heat load on the workers than the environmental WBGT.

[0126] Based on the above, it was confirmed that using the WBGT estimated by the group heat load estimation method described above to understand the heat load on workers belonging to this group, and more specifically, estimating the heat load of a specific worker based on the representative value of the heart rate in the group, is effective in more accurately preventing the onset of heatstroke.

[0127] This section describes a method for more accurately estimating the heat stress on a specific worker belonging to a group, using the magnitude of the heat stress on the group obtained based on the representative value of the differential heart rate of the group to which the workers belong.

[0128] Figure 6 shows the relationship between the arm temperature of a worker belonging to a group and the representative value of the difference in heart rate of a group with the same arm temperature.

[0129] In the following, three values ​​are used as representative values ​​for differential heart rate in the group: the 25th percentile, the median (50th percentile), and the 75th percentile. In Figure 6, the relationship between arm temperature and the 25th percentile of differential heart rate is shown as indicated by symbol 81, the relationship between arm temperature and the median (50th percentile) of differential heart rate is shown as indicated by symbol 82, and the relationship between arm temperature and the 75th percentile of differential heart rate is shown as indicated by symbol 83. In all graphs, it can be seen that when the arm temperature exceeds 29°C, the differential heart rate of the group tends to increase.

[0130] Figure 7 shows the relationship between representative values ​​of differential heart rate for a group and the temperature around the worker's arm.

[0131] In Figure 7, the relationship between the 25th percentile value, which is a representative value of the differential heart rate of the group, and the arm temperature is shown as indicated by symbol 91; the relationship between the median (50th percentile), which is a representative value of the differential heart rate of the group, and the mean arm temperature is shown as indicated by symbol 92; and the relationship between the 75th percentile value, which is a representative value of the differential heart rate of the group, and the arm temperature is shown as indicated by symbol 93.

[0132] As shown in Figure 7, the median (50th percentile) (indicated by 92) and 75th percentile (indicated by 93) of the differential heart rate of the group show a tendency for the temperature around the arm to increase as the magnitude of the differential heart rate, i.e., the degree of increase in the worker's heart rate, increases. Focusing on the 75th percentile of the differential heart rate of the group, which serves as a safety standard, it was found that this increasing trend in differential heart rate can be approximated as "29.0 + 0.13 * differential heart rate," as shown as the straight line indicated by 94 in Figure 7, with the reference point being the point where the temperature around the arm is 29°C when the differential heart rate is 0 (resting heart rate).

[0133] By utilizing the insights gained from the above studies, the heat load on a specific worker within a group can be more accurately estimated by correcting the heat load value in the environment where the worker is placed (estimated from the environmental WBGT value obtained by a WBGT meter, or temperature data obtained from a measuring device worn by the worker) using the heat load value estimated for the group in which the worker is placed.

[0134] The specific method will be explained below.

[0135] First, the heat load H[i] experienced by the worker who is estimating the heat load is...

[0136]

number

[0137] Let's assume that when H > 1.1, H=1.1 (6) Let's assume that.

[0138] Furthermore, when using temperature Ta and relative humidity RH, the WBGT conversion value is calculated using the following equation (7).

[0139]

number

[0140] Next, to correct for the heat stress H[i] mentioned above, we need to calculate the differential heart rate ΔHR for the population. (med) (Tloc) This is calculated using the following procedure.

[0141] (1) Identify the workers belonging to the group. Using the workers' location information, 5 to 20 workers are selected in order of proximity to a specific target whose heat load is estimated. When determining the distance between a specific worker and other workers, it is preferable to use an appropriate method available, such as automatically selecting workers using location information when available, or selecting workers belonging to the same work group using pre-registered work groups when location information is difficult to use.

[0142] (2) Calculation of the worker's differential heart rate Based on equation (1), calculate the differential heart rate δHR[i] for each worker belonging to the extracted group. δHR[i] = HR(min)[i] - HR pred [i] (1) Furthermore, to exclude abnormal outliers, data with δHR[i] < -5 will be deleted.

[0143] (3) Calculation of differential heart rate for the group At predetermined time intervals Tloc (for example, Tloc = 30 min), the group's differential heart rate ΔHR is calculated from each worker's differential heart rate δHR[i] to obtain a representative value (median, 50th percentile) of the group's differential heart rate. (med) (Tloc) Calculate.

[0144] Next, we calculate the heat load correction value H(grp) used when calculating the heat load for the specific worker in question.

[0145] The difference in heart rate (ΔHR) for the group calculated above (med) (Tloc) Regarding Group difference in heart rate ΔHR (med) (Tloc) When >4 bpm, the candidate value h(grp) for correcting the heat load is calculated using the following formula (8).

[0146]

number

[0147] Also, the differential heart rate (ΔHR) of the group. (med) (Tloc) When ≤4 bpm, the heat load correction value H(grp) is given by the following equation (10).

[0148]

number

[0149] The corrected heat load H(grp) obtained as described above is used as the heat load for the specific worker in question. However, if H(grp) > 1.1, H(grp)=1.1 (11) Let's assume that.

[0150] In this way, the heat load on workers can be accurately determined.

[0151] Furthermore, when determining the heat stress risk for a worker, the worker's work intensity W[i] and its average (simple arithmetic mean) are calculated as follows.

[0152]

number

[0153] Here, t = t[i], and ♯(·) represents the number of data points that satisfy the condition.

[0154] Using the corrected heat load H(grp) and work intensity W[i] obtained above, the evaluation index R(t) for the heat work risk of a specific worker is defined as shown in equation (14) below.

[0155]

number

[0156] Risk assessment should be performed as follows: When 0 ≤ R(t) < 0.8, it is within the acceptable range. When 0.8 ≤ R(t) < 1.0, there is a possibility of exceeding the tolerance standard. When 1.0 ≤ R(t), the condition exceeds the acceptable limit.

[0157] In the above embodiment, when estimating the heat load of a specific worker, a value obtained by correcting the WBGT (Wet Bulb Globe Temperature) acquired from the arm circumference temperature obtained by a sensing unit, which is a measuring device worn by the worker, with a heat load correction value H(grp) estimated from the representative value of the differential heart rate of the group was used. However, the base heat load (WBGT) to be corrected by the heat load correction value H(grp) obtained from the group in which the worker is included is not limited to the arm circumference temperature obtained by the worker's sensing unit. Depending on the circumstances surrounding the worker, it may be possible to accurately determine the heat load on a specific worker by correcting the environmental WBGT of the workplace where the worker is working with the heat load correction value H(grp). [Industrial applicability]

[0158] The collective heat load estimation method and collective heat load estimation system disclosed herein can accurately and quickly perform a collective evaluation of the heat load experienced by workers in environments where physical and thermal loads are significant, such as construction sites and transportation businesses, and can also accurately estimate the heat load on specific workers belonging to a group. [Explanation of symbols]

[0159] 10 Workers (Target Persons) 11. Sensing Unit (Measuring Device) 12. Smartphone (mobile device) 21 Cloud Servers 25 Heat load estimation section

Claims

1. A method for estimating the heat load on a group of multiple subjects using a computer operated by a computer program, A representative value of the heart rate in the group is calculated from the heart rates of each of the aforementioned multiple subjects. A method for estimating the heat load on a group, characterized by estimating the WBGT for the group based on the aforementioned representative value.

2. The method for estimating group heat stress according to claim 1, wherein the representative value is a representative value of the differential heart rate in the group, calculated from the difference between the measured heart rate and the predicted heart rate of each of the multiple subjects.

3. The method for estimating group heat stress according to claim 2, wherein the 50th percentile (median) and 75th percentile values ​​of the differential heart rate of the subjects included in the group are used as representative values.

4. The method for estimating collective heat stress according to claim 2, wherein the predicted heart rate is calculated based on a pre-created heart rate prediction model.

5. The method for estimating group heat stress according to claim 4, wherein the predicted heart rate is calculated using the heart rate prediction model in accordance with the magnitude of the workload on the subject.

6. The method for estimating group heat load according to claim 1, wherein, based on the location information of the subject persons, the subject persons within a predetermined range are set as the group.

7. The method for estimating group heat load according to claim 1, wherein the workers forming the group are predetermined based on the magnitude of the workload imposed on the aforementioned target persons.

8. A method for estimating the heat load on a group of multiple subjects using a computer operated by a computer program, A representative value of the heart rate in the group is calculated from the heart rates of each of the aforementioned multiple subjects. A method for estimating group heat load, characterized by estimating the heat load on a specific subject belonging to the group based on the aforementioned representative value.

9. The method for estimating a group heat load according to claim 8, wherein the heat load applied to a specific subject is estimated by correcting the heat load calculated for the environment in which the specific subject is present with the representative value.

10. A measuring device that acquires biometric information from each of several subjects belonging to a group, A group heat load estimation system characterized by comprising a heat load estimation unit that calculates a representative value of the heart rate in the group based on the biometric information obtained from a plurality of subjects and estimates the WBGT for the group.

11. The collective heat load estimation system according to claim 10, further comprising information transmission means for receiving the acquired biological information from the measuring device and transmitting it to the heat load estimation unit.

12. The collective heat load estimation system according to claim 10, wherein the heat load estimation unit is installed on a server on a network.

13. The collective heat stress estimation system according to claim 10, wherein the measuring device is a wristwatch-type biosensor comprising a heart rate sensor for detecting heart rate data worn by the subject and an acceleration sensor for detecting acceleration data.

14. The system further comprises location information detection means for detecting the location information of the subject, The group heat load estimation system according to claim 10, which detects a plurality of subjects whose current location is within a predetermined range based on the location information and forms the group.

15. A measuring device that acquires biometric information from each of several subjects belonging to a group, A group heat load estimation system characterized by comprising a heat load estimation unit that calculates a representative value of heart rate in the group based on the biometric information obtained from multiple subjects and estimates the heat load on a specific subject belonging to the group.

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