JP7213481B2 - Work load estimation method, physical condition evaluation method, heatstroke risk evaluation method, and program - Google Patents

Description

The present application provides a work load estimation method for calculating a work load index of an evaluator by using a measuring device worn by the evaluator to detect heartbeat data of the evaluator and acceleration data associated with movement of the evaluator, and , a physical condition evaluation method for calculating the heartbeat index and physical strength index of the person to be evaluated, and the heartbeat data and acceleration data detected by the measuring device and the temperature inside the clothes are used to calculate the risk of developing heatstroke of the person to be evaluated. The present invention relates to a method for evaluating the risk of developing heatstroke, and a computer program for causing a control means, which is a computer, to execute each of these evaluation methods.

In recent years, the environment for connecting to the Internet such as wireless LAN has been improved, and the means to enable information transmission at short distances such as Bluetooth (registered trademark) has been developed. With the spread of compact sensor devices that can measure physical data such as body temperature, heart rate, and amount of perspiration, the health status of the subject can be managed by transmitting the data acquired by the sensor device to a mobile device connected to the Internet. Management systems that prevent unexpected situations such as accidents caused by poor physical condition have been put into practical use.

As an example of such a physical condition management system, there is known a system for managing and preventing the onset of heatstroke, for which an increase in the risk of developing heat stroke has become a social problem due to global warming. For example, by having the managed person wear a housing having an acceleration sensor, an ultraviolet sensor, a GPS receiver, an operation input unit, a display/audio notification unit, an Internet connection unit, etc., the amount of exercise of the managed person and the Determine the risk of developing heatstroke using the WBGT index based on weather conditions such as temperature and humidity obtained via the Internet, and determine the risk of heatstroke according to the determination results. It has been proposed to prevent the onset of heat stroke by instructing a person to rest (see Patent Document 1).

JP 2012-210233 A

In the above-described conventional heat stroke prevention system, if the person to be managed has a small housing that is information acquisition means attached to their clothes, they can reduce their risk of developing heat stroke without direct contact with their skin. It is possible to grasp in real time and take appropriate countermeasures.

However, for example, in work such as slowly lifting a heavy object, a large physical load is applied even if a large movement of the body does not occur. In addition, depending on the physical condition of the person being managed, the risk of developing heat stroke may change significantly even under the same conditions. Only corrections are made based on data such as time, and if the supervised person does not enter data correctly or if their physical condition suddenly changes during work, the criteria for determining the risk of heat stroke will be changed. It is difficult to perform accurate risk management by

The purpose of the present application is to solve the problems of the above-mentioned prior art, and includes a work load estimation method for calculating a work load index that indicates the work intensity of an evaluator, and a heart rate index and physical strength index of an evaluator. , a heatstroke risk assessment method for calculating a heatstroke risk index of an evaluator, and a computer program for causing a control means, which is a computer, to execute these various evaluation methods. aim.

In order to solve the above problems, the work load index evaluation method disclosed in the present application includes heartbeat detection means for detecting heartbeat data of an evaluator, and acceleration detection means for detecting acceleration data associated with the action of the evaluator. work load indicating the intensity of the work performed by the person to be evaluated based on at least one of the heartbeat data and the acceleration data of the person to be evaluated obtained by the measurement device equipped with the measurement device It is characterized by calculating an index.

In addition, the physical condition evaluation method disclosed in the present application uses a measuring device having heartbeat detection means for detecting heartbeat data of the person to be evaluated, and acceleration detection means for detecting acceleration data associated with the movement of the person to be evaluated, A heartbeat index and a physical fitness index of the person to be evaluated are calculated based on the heartbeat data and the acceleration data of the person to be evaluated for a predetermined period obtained by the measuring device. Furthermore, the method for evaluating the risk of developing heat stroke disclosed in the present application uses the measuring device further comprising temperature detection means for detecting the temperature inside the clothes of the person to be evaluated, and is calculated by the work load estimation method disclosed in the present application. A heat stroke risk index for the person to be evaluated based on the work load index, the temperature inside the clothes measured by the measuring device, and the heat load index for the person to be evaluated calculated based on the environmental temperature. is characterized by calculating

Furthermore, the computer program disclosed in the present application uses a computer as control means, and causes the control means to execute any of the work load estimation method, the physical condition evaluation method, and the heat stroke onset risk evaluation method disclosed in the present application. Characterized by

With the above configuration, the work load estimation method disclosed in the present application can calculate a work load index that takes into consideration the physical condition of the person being evaluated and individual differences.

Further, the physical condition evaluation method disclosed in the present application can calculate the heart rate index and the physical fitness index of the person to be evaluated, and can accurately evaluate the physical condition of the person to be evaluated at the time of evaluation.

Furthermore, the method for evaluating the risk of developing heatstroke disclosed in the present application is based on heart rate data of the person to be evaluated, acceleration data indicating movement, data on the temperature inside the clothes and the temperature of the environment. Based on the burden index and the heat load index that indicates the heat factor, the risk of the person being evaluated developing heat stroke can be evaluated.

Furthermore, the computer program disclosed in the present application can cause the computer, which is the control means, to execute any one of the work load index evaluation method, the physical condition evaluation method, and the heat stroke onset risk evaluation method disclosed in the present application.

FIG. 1 is an image diagram illustrating the overall configuration of a heatstroke risk management system described as an embodiment. FIG. 2 is a block diagram showing the configuration of each part of the heatstroke risk management system described as an embodiment. FIG. 3 is a diagram for explaining the configuration of an undershirt equipped with a biosensor, which is a measuring device for acquiring various types of information on a person to be managed. FIG. 3(a) shows the front side of the undershirt, and FIG. 3(b) shows the back side of the undershirt. FIG. 4 is a flowchart for explaining a heat stroke risk evaluation method in the heat stroke risk management system described in this embodiment. FIG. 5 is a diagram showing a measurement result of a standard heartbeat response showing a heartbeat response to an acceleration deviation. FIG. 5(a) shows a plot of all about 3 million points of data, and FIG. 5(b) shows a standard heartbeat response obtained using the median heartbeat response to the acceleration data. FIG. 6 is a diagram explaining a method of estimating a heartbeat index and a physical strength index in the physical condition evaluation method according to the present embodiment. FIG. 7 is a diagram for explaining the first correction map for obtaining the work burden index in the work burden estimation method explained in this embodiment. FIG. 8 is a diagram for explaining a second correction map for obtaining a work load index in the work load estimation method described in this embodiment. FIG. 9 is a display example of a two-dimensional map showing the risk of developing heatstroke obtained by the evaluation method shown in this embodiment, with the work burden index and the heat load index as axes. FIG. 10 is a diagram for explaining the correlation between the values of the heart rate index and the physical strength index obtained by the physical condition evaluation method according to the present embodiment and the results of questionnaires given to the persons to be evaluated. FIG. 9(a) shows the data before starting the work, and FIG. 9(b) shows the data after the work.

The work load estimation method disclosed in the present application uses a measuring device having a heartbeat detection means for detecting heartbeat data of an evaluator and an acceleration detection means for detecting acceleration data associated with the movement of the evaluator. Based on at least one of the heartbeat data and the acceleration data of the person to be evaluated acquired by the measuring device, a work burden index indicating the intensity of the work performed by the person to be evaluated is calculated.

By doing so, it is possible to calculate the work load index from both the magnitude and speed of the evaluator's movement and the evaluator's heart rate. However, it is possible to accurately estimate the work load of the person being evaluated, even when the person is doing work with a high load.

In the work load estimation method disclosed in the present application, a standard heart rate response model is created based on the heart rate data and the acceleration data obtained by measuring a plurality of persons, and the standard heart rate is used when calculating the work burden index. A response model can be used. By doing so, individual differences can be corrected using a standard heartbeat response model representing a standard heartbeat response of a human.

Moreover, it is preferable that the standard heart rate response model is created with a plurality of persons engaged in the same work as measurement targets. For example, a standard heartbeat response model created based on data obtained by measuring multiple workers engaged in construction work at the same construction site represents a typical heartbeat response of the worker, A work burden index optimized for the work can be calculated.

Further, a standardized heart rate obtained by correcting the heart rate data of the person to be evaluated based on the heart rate data and the acceleration data of the person to be evaluated during a predetermined period in the past, and the standard heart rate response model. and calculating the work burden index of the evaluator based on the standardized heart rate. By doing so, it is possible to standardize the personality of each person to be evaluated in terms of changes in heart rate with respect to work load, and to calculate a more accurate work load index.

Further, an estimated standardized heart rate of the person to be evaluated is calculated based on the acceleration data of the person to be evaluated and the standard heart rate response model, and the work burden index of the person to be evaluated is calculated based on the estimated standardized heart rate. is preferably calculated. By doing so, even when the reliability of the heartbeat data obtained from the measuring device is low, the work load index can be calculated using the approximate value of the heartbeat data estimated from the acceleration data.

Furthermore, when calculating the work burden index for the person to be evaluated for the first time, it is preferable to calculate the work burden index based only on the acceleration data. By doing so, it is possible to avoid the influence of the heart rate data, in which individual differences appear greatly, and to estimate the work load in the direction of lower risk even for the person to be evaluated who calculates the work load index for the first time.

A physical condition evaluation method disclosed in the present application uses a measuring device having heartbeat detection means for detecting heartbeat data of an evaluator, and acceleration detection means for detecting acceleration data associated with the movement of the evaluator. A heartbeat index and a physical strength index of the evaluator are calculated based on the heartbeat data and the acceleration data of the evaluator in a predetermined period acquired by the device.

By doing so, the physical condition evaluation method disclosed in the present application can perform more accurate physical condition evaluation in consideration of the condition of the person to be evaluated and the surrounding environment at the time of evaluation.

In the physical condition evaluation method disclosed in the present application, it is preferable that the heartbeat index and the physical strength index are calculated from a regression line with respect to the set of heartbeat data in the predetermined period and the set of acceleration data in the predetermined period.

The method for evaluating the risk of developing heat stroke disclosed in the present application uses the measuring device further comprising temperature detection means for detecting the temperature inside the clothing of the person being evaluated, and calculates by any of the work load estimation methods disclosed in the present application. Onset of heatstroke in the person to be evaluated based on the work load index calculated, the temperature inside the clothes measured by the measuring device, and the heat load index of the person to be evaluated calculated based on the environmental temperature Calculate the risk index.

By doing so, in the heatstroke risk evaluation method disclosed in the present application, the accurate work burden index calculated by the work load estimation method disclosed in the present application and the thermal environment around the evaluated person are taken into consideration. , a more accurate heatstroke risk index can be calculated.

In the heat stroke risk evaluation method disclosed in the present application, it is preferable to correct the heat stroke risk index using the heart rate index and the physical fitness index. By doing so, it is possible to calculate a heatstroke risk index that takes into account the physical condition of the person being evaluated at the time of evaluation.

Further, it is preferable to display the heat stroke risk index of the person to be evaluated on a two-dimensional map having the work burden index and the heat load index as axes. By doing so, for example, the risk of developing heat stroke for multiple evaluators or the risk of developing heat stroke for a specific individual evaluator can be visually divided into work load factors and heat load factors. can be grasped. As a result, it is possible to easily find an evaluator evaluated to have a high risk of developing heat stroke and call attention to it, and to adopt more effective measures to reduce the risk of developing heat stroke. .

The computer program disclosed in the present application uses a computer as control means, and causes the control means to execute any one of the workload estimation method, the physical condition evaluation method, and the heat stroke onset risk evaluation method disclosed in the present application. By doing so, each evaluation method disclosed in the present application can be executed using a computer.

Hereinafter, the workload estimation method, the physical condition evaluation method, the heat stroke onset risk evaluation method, and the computer program disclosed in the present application will be exemplified by a physical condition management system that manages the physical condition using the various evaluation methods disclosed in the present application. , will be described with reference to the drawings.

(Embodiment)
In the present embodiment, the heat stroke risk index of the administrator is calculated using the heat stroke risk evaluation method disclosed in the present application, and the manager develops heat stroke based on the calculated heat stroke risk index. A heat stroke onset risk management system capable of warning a managed person who is at high risk of being in a dangerous state and avoiding the onset of heat stroke is shown and explained.

More specifically, the heatstroke risk management system described in this embodiment regards workers at construction sites who are subjected to a strong physical load and thermal load, such as being forced to work hard under the scorching sun, as managed persons (evaluation This method is introduced by a manager such as a site supervisor as a person to be evaluated for the method to manage the workers to prevent heat stroke.

A physical condition evaluation method for evaluating the physical condition of the person to be evaluated using the measuring device used in the heatstroke risk management system described in this embodiment will also be described.

The heat stroke risk management system described below is merely an example of a system that employs the heat stroke risk evaluation method disclosed in the present application. It does not limit the work load estimation method and the physical condition evaluation method disclosed in the present application, including the disease onset risk evaluation method.

[System overview]
FIG. 1 is an image diagram for explaining the schematic configuration of the heat stroke onset risk management system explained in this embodiment.

FIG. 2 is a block diagram showing a configuration example of each part of the heatstroke risk management system according to this embodiment.

As shown in FIG. 1, a worker 10 working at a construction site uses a measuring device equipped with a three-dimensional acceleration sensor, which is an acceleration detection means for detecting the temperature in clothes, heartbeat data, and body movement. A person wears a certain biosensor 11 on his/her chest and has a smart phone 12 as a portable terminal that functions as an information transmitting unit that transmits various kinds of information obtained by the biosensor 11 to the outside. In addition, in the heat stroke onset risk management system described in this embodiment, the worker 10 corresponds to the managed person. Furthermore, in the work load estimation method, the physical condition evaluation method, and the heat stroke onset risk evaluation method disclosed in the present application, the worker 10 corresponds to the person to be evaluated.

In the heatstroke risk management system described in this embodiment, the biosensor 11 that acquires various information about the worker 10 is attached to the chest of the undershirt worn by the worker 10 .

FIG. 3 is a diagram showing a configuration example of an undershirt worn by a worker in the heatstroke risk management system according to this embodiment. FIG. 3(a) shows the front side of the undershirt to which the biosensor is attached, and FIG. 3(b) shows the back side of the undershirt, that is, the side that faces and contacts the body surface of the worker.

As shown in FIG. 3, a biosensor 11 is arranged on the chest of an undershirt 18 worn by a worker 10 in the heatstroke risk management system according to this embodiment. More specifically, the biosensor 11 is connected to a data acquisition/transmission unit 11a arranged in the chest central portion of the front surface 18a of the undershirt 18, and connected to the data acquisition/transmission unit 11a. In other words, the electrode portion 11b is arranged to extend in the left-right direction at the portion on the side that comes into contact with the skin.

In the heat stroke onset risk management system according to the present embodiment, the biosensor 11 detects the heartbeat, the temperature inside the clothes, and the movement of the worker 10. Electrodes, which are heartbeat detecting means, are arranged on the back surface of the undershirt 18. is in contact with the chest, the heartbeat of the worker 10 can be detected more accurately. A temperature sensor (not shown) for detecting the temperature inside the clothes and an acceleration sensor chip (not shown) for detecting acceleration in three-dimensional directions are accommodated in the data acquisition/transmission unit 11a.

In the heat stroke onset risk management system described in this embodiment, an example of the arrangement of the measurement device that acquires the heart rate, clothing temperature, and movement of the worker 10 is to attach the biosensor 11 to the undershirt 18 shown in FIG. The method is not limited. For example, a method in which the biosensor 11 is placed in a highly adhesive sheet-like attachment cover and this is directly attached to the chest, and a stretchable attachment belt capable of holding the biosensor 11 in close contact with the body is used. For example, a method of arranging it on the chest of a person can be adopted. However, the method using the mounting cover or the mounting belt cannot eliminate the discomfort felt by the worker 10 when wearing the biosensor 11, and when the worker 10 wears the biosensor 11 for a long time, the biosensor 11 may be damaged by sweat or the like. There is a risk that it will come off the body surface of the body or the attachment position will shift.

On the other hand, according to the method of fixing the biosensor 11 to the undershirt 18 worn by the worker 10 as shown in FIG. You can relax and get the information you need. In addition, even if the worker 10 perspires or twists during work, the biosensor 11 fixed to the undershirt 18 will not come off the body surface of the worker 10, and the worker 10 can wear it. The position can also remain substantially unchanged. Therefore, although part of the heartbeat of the worker 10 may not be obtained as heartbeat data, it is possible to avoid a situation in which heartbeat data cannot be obtained at all continuously.

When the biosensor 11 is used as a measuring device for acquiring information about the worker 10, the placement location of the biosensor 11 may be the worker's waist, back, upper arm, or leg, in addition to the operator's chest. etc. can be adopted. In these cases as well, the biosensor 11 can be tightly fixed to the body surface of the worker by arranging the electrode section 11b on the inner surface of the underwear and the data acquisition and transmission unit 11a on the outer surface. A method of fixing the biosensor using a cover or an attachment belt can be employed. However, in the work load estimation method, the physical condition evaluation method, and the heatstroke risk evaluation method according to the present embodiment, the evaluation index is determined based on the change in the heart rate of the person being evaluated. In order to detect 10 heartbeats, it is considered most preferable to place the biosensor 11 on the chest, and in that respect, it is considered practical to use an undershirt 18 as shown in FIG.

In addition, as described in the present embodiment, it is not a heatstroke risk management system that manages workers working at construction sites, but for example, when evaluating the physical condition of athletes who are training, etc. It is conceivable that the evaluator wears sportswear, and in this case as well, it is most rational to place the biosensor on the chest of the clothing worn on the upper body.

The biosensor 11 and the smart phone 12 possessed by the worker 11 are always connected by short-range communication such as Bluetooth (registered trademark), and various types of information acquired by the biosensor 11 are sent to the smart phone 12 at any time. being sent.

The smart phone 12 constantly transmits the information of the worker 10 sent from the biosensor 11 using a wireless LAN or an information carrier such as a mobile phone by means of an evaluator information transmission unit 13, a data reception unit 15, and a data transmission unit 16. It is connected to the Internet 20 as a network environment. Then, the smartphone 12 stores the temperature inside the clothes of the worker 10, the heartbeat data, and the measurement data of the acceleration sensor, which are the evaluated person information, in the cloud server 21, which is a server provided with the risk evaluation determination unit 22 installed on the Internet 20. to transmit.

The cloud server 21 includes a data receiving unit 23 and a data transmitting unit 26 inside, and can transmit and receive information via the Internet 20. Receive measurement data from, evaluate and determine the risk of developing heat stroke for each worker, and create warning information to warn the worker if the risk of developing heat stroke is increasing. do. The cloud server 21 also includes a data recording unit 24, which can record measurement data from each of the plurality of workers 10, generation history of warning information, and the like in chronological order.

Furthermore, the cloud server 21 has a weather information acquisition unit 25, acquires weather information from an information site that provides weather information via the Internet 20, and calculates the temperature in the area where the worker 10 is working. You can get weather conditions at the current time, such as , humidity, and amount of sunshine, as well as weather forecasts that anticipate changes in the next few hours.

In addition, the cloud server 21 is connected via the Internet 20 to a personal computer 31 as a manager information terminal used by a site supervisor 30 who supervises the work of the worker 10 whose risk of developing heat stroke is to be determined at the construction site. is connected with For this reason, the site supervisor 30 at the work site where the worker 10 is working can use the data receiving unit 33 of the personal computer 31 to receive the measurement results obtained from the worker 10, which are transmitted from the cloud server 21 as needed, and the evaluation determination unit 22 It is possible to grasp whether or not warning information has been generated.

The evaluation determination unit 22 of the cloud server 21 evaluates the physical condition of the worker 10 based on the heartbeat data, acceleration data, and clothes temperature data obtained from the biosensor 11, which is a measuring device worn by the worker. A work load index is calculated, and a heatstroke risk index for the worker 10 is calculated by taking into consideration the temperature information inside the clothes and the environmental temperature information of the work area obtained via the Internet.

The specific contents of the work load estimation, physical condition evaluation, and heat stroke onset risk evaluation of the worker 10 performed by the evaluation determination unit 22 will be described in detail later.

In addition, the cloud server 21 stores history data as past history information of the worker 10 to be judged recorded in the data recording unit 24, weather information of the work area acquired by the weather information acquisition unit 25, and further, determination Based on environmental information such as changes in various information acquired from workers other than the determination target worker who work at the same site as the target worker, the heat stroke onset risk evaluation result of the worker 10 is corrected. Therefore, it is possible to evaluate and determine the risk of developing heat stroke in a more realistic manner.

In addition, in the heat stroke onset risk management system exemplified in the present embodiment, the cloud server 21 is not the only one that includes the evaluation determination unit 22 . For example, various functions of the cloud server 21 including the risk determination unit may be implemented on the administrator information terminal or the management computer of the office, and if the function can be realized, the evaluation determination unit is implemented. Any location or equipment is acceptable.

The personal computer 31 of the on-site supervisor 30 manages whether or not various information obtained by the measuring device and warning information regarding the workers belonging to the work site supervised by the on-site supervisor 30 including the worker 10 have been generated. It has an information management unit 32 that Based on the information transmitted from the cloud server 21, the information management unit 32 always provides information that serves as a criterion for evaluating the risk of developing heat stroke whether information obtained from each worker or warning information has been generated. I understand it as the latest information. In addition, the information management unit 32 outputs the evaluation determination result of the heat stroke onset risk of each worker and other environmental information obtained to the display image processing unit 35, and the display image processing unit 35 displays a display device such as a liquid crystal monitor. The screen content displayed on 36 is adjusted.

In this way, the site supervisor 30 grasps the information of the workers 10 working at the work site supervised by him or the risk of developing heatstroke in an integrated manner as a whole or as detailed information of each worker on an easy-to-view screen. can do. It should be noted 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 formed system in an easy-to-see manner. Detailed description is omitted.

As shown in FIG. 1, when the personal computer used by the foreman is a desktop type, the display device 36 is separate from the main body of the personal computer. As described above, the administrator information terminal 31 having the information management unit 32 and the like and the display device 36 for displaying the screen for the administrator are not limited to being physically integrated. Further, for example, when data is checked by a plurality of people, it is conceivable to use a dedicated large-sized image display device, or to use a projector to enlarge and project a displayed image onto a screen or a wall surface.

Furthermore, the personal computer 31 of the site supervisor 30 receives various information obtained from the worker 10 after notification of the warning information and receipt confirmation of the warning information from the worker 10, thereby preventing the worker 10 from developing heat stroke. It is possible to check whether measures have been taken to prevent heat stroke, and if the worker 10 does not take measures to prevent the onset of heat stroke, the warning information is repeatedly sent to the target worker 10 It is possible to call the attention of the worker 10 by, for example, transmitting the

In addition, if it is possible for the on-site supervisor 30 to take measures to improve the working environment of the work site, such as air conditioning and ventilation, the on-site supervisor 30 may take appropriate measures to prevent the occurrence of heat stroke at the work site supervised by him/herself. can be prevented.

In the above description, an example is described in which the evaluation determination unit 22 of the cloud server 21 generates warning information that informs the worker 10 that the risk of developing heat stroke is high. It can be generated by the information management section 32 installed in the personal computer 31 of the site supervisor 30 . Also, both the evaluation determination unit 22 and the information management unit 32 can be set to generate warning information. By doing so, warning information is generated from the personal computer 31 of the site supervisor 30 who actually supervises the work site, prior to the determination result of the evaluation determination unit 22, and is transmitted to the target worker 10. Therefore, it may be possible to further reduce the risk of developing heatstroke depending on the actual conditions of the work site.

The warning information generated by the evaluation determination 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 a local network such as a wireless LAN or an information carrier such as a mobile phone. is transmitted to the smart phone 12 equipped by the worker 10 via a network including the . The warning notification unit 14 of the smartphone 12 that has received the warning information uses various information transmission means such as sound, screen display, lighting or blinking of the lamp, and vibration to notify the worker 10 that he or she has developed heat stroke. to inform you that there is an increased risk of After confirming the warning information, the worker 10 reports that the warning information has been received through the touch panel or operation buttons of the smartphone 12, and takes measures to prevent heat stroke, such as stopping the work and taking a rest. .

The smart phone 12 of the worker 10 transmits to the personal computer 31 of the supervisor 30 that the worker 10 confirmed the warning information and interrupted the work, and the supervisor 30 prevents the worker 10 from developing heatstroke. You can confirm that you have taken action.

Furthermore, in the heat stroke onset risk management system described in this embodiment, the heat stroke onset risk data at the work site grasped by the site supervisor 30 is transmitted to the smartphone 12 of the worker 10, and the worker 10 , you can check the current status of the risk of developing heatstroke at the work site where you work. For example, if it can be confirmed that the risk of developing heat stroke in workers other than oneself is high, each worker can take measures to actively prevent the onset of heat stroke. Also, if it is known that there is another worker who received the warning information about the risk of developing heatstroke and interrupted the work, it can be expected that the site supervisor 30 will more obediently respond to the warning information addressed to him/herself.

Furthermore, the smartphone 12 owned by the worker 10 evaluates the health condition calculated from the change in the risk of developing heatstroke up to the present of the worker 10, and the own heart rate and acceleration data acquired by the biosensor 11. The results and related information such as calorie consumption can be displayed on the screen for the operator 10 to refer to. Regarding the display screen of the smart phone owned by the worker 10 as well, it is sufficient if necessary items can be displayed in an easy-to-read manner according to each purpose, so detailed description in this specification is omitted.

The cloud server 21 is also connected to a management computer 41 in the company or office 40 to which the worker 10 belongs via the Internet 20, and receives the measurement result information of the worker 10 transmitted to the personal computer 31 of the site supervisor 30, Various types of information used by the cloud server 21 to determine the risk of developing heatstroke are transmitted to the management computer 41 of the office 40 in real time. Since the management computer 41 of the office 40 has its own data receiving section 42 and data transmitting section 43, it is also connected to the personal computer 31 of the site supervisor 30 via the Internet. It is possible to confirm information such as whether the warning information has been correctly transmitted to the worker 10 and whether the worker 10 has taken preventive measures against heatstroke, and can issue a predetermined instruction as necessary. By doing so, even in the office 40 to which the worker 10 belongs, the situation of the worker 10 and the response at the work site can be confirmed, and the worker 10 is backed up to avoid the risk of developing heat stroke. be able to.

Although not shown in FIG. 1, the cloud server 21, the personal computer 31 of the site supervisor 30, and the management computer 40 of the office 40 are connected via the Internet 20. It is possible to access the cloud server 21 from the cloud server 21, control the data processing content in the cloud server 21, update the determination program in the evaluation determination unit 22, and receive information necessary for heatstroke prevention management from the cloud server 21. It can be taken out as appropriate.

In the above explanation, smartphones are used as examples of mobile terminals equipped by workers, but the mobile terminals of workers are not limited to smartphones. A dedicated small terminal device capable of transmitting and receiving information can be used.

In addition to the personal computers shown in the examples, especially the desktop personal computer shown in FIG. 1, the administrator information terminals operated by the site supervisors include notebook personal computers, tablet personal computers, small server devices, etc., which can be used to send and receive information through networks. Various information devices capable of displaying data, recording data, etc. can be employed.

Furthermore, in the above description, a configuration has been described in which warning information is transmitted from the site supervisor's manager information terminal to the worker's mobile terminal. The system can also be configured to send warning information directly from the device to the worker's mobile terminal.

In addition, as in the heatstroke risk management system exemplified in this embodiment, when managing the risk of heatstroke for workers at a construction site, the position of the worker to be managed is limited to the construction site. be done. Moreover, since the work period is longer than a certain number of days, it is also possible to construct a wireless LAN at the construction site. In this way, if the person to be managed exists only within a certain range, a unique wireless LAN is constructed, and the biometric information of each worker is transmitted to the manager information terminal of the site supervisor via the Internet. It can be done within the LAN without any intervention. In this case, by connecting to the Internet from the administrator information terminal, or from information equipment such as a server unit installed in the LAN or another personal computer, the risk determination part in the cloud server can detect the risk of heatstroke. It is possible to make judgments and use the weather information acquired by the weather information acquisition unit. Also, a risk determination section for determining the risk of developing heat stroke can be provided in a server unit or personal computer connected to a LAN at a construction site.

On the other hand, when the heat stroke onset risk management system targets workers in the transportation industry, for example, it is assumed that the managed person travels long distances. Even in such a case, in the heat stroke risk management system illustrated above, information from the measurement device of the managed person is managed and evaluated by a cloud server located on the Internet. There is an advantage that it can respond as it is even if it does not stay at a specific position.

[Method for evaluating the risk of developing heat stroke]
Next, specific contents of the method for evaluating the risk of developing heat stroke disclosed in the present application will be described.

In the method for evaluating the risk of developing heat stroke disclosed in the present application, the heartbeat data of the person being evaluated detected by the heartbeat detecting means provided in the measuring device worn by the person being evaluated and the acceleration data obtained by the three-dimensional acceleration sensor are combined. Based on this, a work burden index indicating the intensity of the work performed by the person to be evaluated is calculated. Also, the heat load index of the person to be evaluated is calculated based on the temperature inside the clothing of the person to be evaluated obtained from the measuring device and the environmental temperature of the site where the person to be evaluated is working. Then, based on the calculated work load index and heat load index, a heat stroke risk index indicating the risk of developing heat stroke is calculated.

In the following description of the method for evaluating the risk of developing heat stroke, each component of the heat stroke risk management system according to the present embodiment described with reference to FIG. 2 will be exemplified as appropriate.

FIG. 4 is a flow chart showing the flow of heat stroke risk evaluation in the evaluation determination unit in the heat stroke risk management system described in this embodiment.

In the heat stroke onset risk management system according to the present embodiment, the evaluation determination unit 22, which is the control means provided in the cloud server 21 on the Internet, is the biosensor, which is the measurement device worn by the worker 10 who is the person to be evaluated. Based on the data obtained from 11 and the data obtained from each component in the cloud server 21, the work burden index and the heat load index of the worker 10 are calculated to calculate the heat stroke risk index. .

As shown in FIG. 4, when the evaluation by the evaluation determination unit 22 starts (START), the evaluation determination unit 22 starts calculating the work burden index of the person being evaluated.

In addition, the start (START) of the evaluation by the evaluation determination unit 22 is performed by turning the power switch of the biosensor 11, which is a measuring device, to "ON" by the worker 10 himself or the site supervisor 30 who is the manager. The biosensor 11 itself detects that the worker wears the undershirt 18 equipped with the biosensor 11, which is set so that the operation of the biosensor 11 automatically starts when the work start time comes. can be set in various ways, such as starting

In order to calculate the work load index, the evaluation determination unit 22 first determines whether history data, which is heartbeat data and acceleration data, is recorded in the data recording unit 24 as past data of the worker 10 to be evaluated. is confirmed (step S101).

When the worker 10 has been evaluated in the heatstroke risk management system described in the present embodiment in the past, and the history data of the worker 10 is recorded in the data recording unit 24 ("Yes" in step S101 ), a linear section in which the heart rate changes linearly with acceleration is obtained from the set of history data, and a regression line is obtained for the history data included in the linear section. A regression line obtained from this historical data represents the characteristics (individuality) of the heartbeat response of the worker. The slope of this regression line is defined as the heart rate response coefficient αr, and the intercept is defined as the intercept heart rate βr (step S102).

After that, the evaluation determination unit 22 detects heartbeat data of the worker 10 measured by the biosensor 11 (step S103).

On the other hand, if the history data of the worker 10 does not exist, or if the data exists but a certain period of time (one month as an example) has passed since the previous data was recorded, The evaluation determination unit 22 determines that the correct standardized heart rate of the worker 10 cannot be calculated, and calculates the work burden index of the worker 10 only from the acceleration data, not based on the heart rate data. The evaluation determination unit 22 calculates the acceleration deviation, which is a numerical value indicating the motion of the worker 10 (step S110), and calculates the work burden index of the worker 10 based only on the acceleration deviation (step S111). In this case, the acceleration deviation may be converted into a work load index using a predetermined formula, such as by multiplying the acceleration deviation by an appropriate coefficient.

When the history data of the worker 10 is recorded in the data recording unit 24 ("Yes" in step S101), the evaluation determination unit 22 ensures the reliability of the heartbeat data when detecting the heartbeat data. In the heat stroke onset risk management system of this embodiment, the biosensor is attached to the back surface 18b of the undershirt 18 worn by the worker 10 so that the heart rate data of the worker 10 who is the person to be evaluated can be obtained more satisfactorily as described above. 11 electrode portions 11b are arranged. However, the heartbeat may not be detected correctly due to the movement of the worker's body, perspiration on the body surface, and the like. For this reason, in the heatstroke risk management system of the present embodiment, if the heartbeat data of the worker 10 who is the person to be evaluated cannot be measured correctly, the work load index is calculated based on the incorrect data, and the onset of heatstroke is calculated. We check whether the heart rate data is acquired correctly so as not to make an error in the risk assessment.

First, the evaluation determination unit 22 calculates the heartbeat waveform detection rate in order to evaluate the reliability of the heartbeat data (step S104).

Raw data sampled from the biosensor may contain a certain percentage of noise (abnormal heartbeat data) due to poor contact between the subject's skin and electrodes. Therefore, for the data for each beat (heartbeat interval), for example, the heartbeat interval is 0.33 seconds or more and 1.33 seconds or less, and the difference from the previous data (differential heartbeat interval) is Data of 0.15 seconds or less are determined to be normal and labeled.

The threshold for judging normality/abnormality can be arbitrarily set, but an appropriate numerical value may be set so as to eliminate improbable heartbeat interval data based on a physiological point of view. Then, the measured data is divided into k sub-intervals with a predetermined time width, and the cardiac waveform detection rate Q is calculated for each sub-interval as the percentage of the data labeled as normal in the total data for each interval. .

Next, the evaluation determination unit 22 determines the heartbeat waveform detection rate for each partial interval (step S105).

If the heartbeat waveform detection rate is equal to or higher than a reference value (for example, 50%), the section is determined to be reliable ("Yes" in step S105), and the work load index is calculated using the heartbeat data (step S106).

On the other hand, if the heartbeat waveform detection rate is less than the reference value (50%), it is determined that there is no reliability ("No" in step S105), and the work load index is calculated for the section using the acceleration data. Proceed to step S110.

Note that the reference values in the above description are only examples, and may be appropriately adjusted according to the performance of the biosensor, the occupation of the person being evaluated, and the like. For example, a low threshold may be set for occupations that require a lot of movement, and a high threshold may be set for occupations that require less movement.

When the heartbeat waveform detection rate is 50% or more ("Yes" in step S105), the evaluation determination unit 22 calculates the central heartbeat rate from the obtained heartbeat data (step S106). Here, for the partial intervals set in step S104, a representative value (median value) for each partial interval is used as central heart rate data. The representative value may be the interval mean value, but is preferably the interval median value. This is because even if the data obtained from the measuring device contains a small number of irregular values, the influence of such irregular values can be eliminated.

Further, the evaluation determination unit 22 simultaneously calculates an acceleration deviation, which is a numerical value indicating the operation status of the worker 10, from the acceleration data obtained from the biosensor 11 (step S107).

Next, the central heart rate is corrected based on the history data and the standard heart rate response model prepared in advance to obtain the standardized heart rate (step S108).

Specifically, using the heart rate response coefficient and the intercept heart rate, and the standard heart rate response coefficient and the standard intercept heart rate, which are the parameters of the standard heart rate response model, the median heart rate data is standardized by the following formula (equation 1). Convert to heart rate.

HR _S [k]=(αs/αr)(HR[k]−βr)+βs (Formula 1)
here,
・Central heart rate data: HR [k]
・Standardized heart rate: HR _S [k]
・ Heart rate response coefficient: αr
・Intercept heart rate: βr
・Standard heartbeat response coefficient: αs
・Standard intercept heart rate: βs
is. Here, k represents the number of the partial section.

A standard heartbeat response model is a heartbeat response model created based on large-scale data obtained from a large number of people. It is a model that expresses the standard human heartbeat response to acceleration (body movement), and can be expressed by various parameters and predetermined formulas.

An example of measurement results for determining the standard heart rate response model is shown in FIG.

FIG. 5(a) plots all of the large-scale data (approximately 3 million points), and the shading represents the density of the data. Line 51 shown in FIG. 5(a) is the 5% data line, line 52 is the 25% data line, line 53 is the 50% data line, line 54 is the 75% data line, line 55 indicates the 95% data line.

On the other hand, the median value of each interval is indicated by the x mark in FIG. 5(b), and the approximation curve F _HR (reference numeral 76) fitted to each median value represents the standard heart rate response model. .

The approximation curve F _HR can be obtained by various curve fitting techniques, and can be expressed as a function F _HR (A _RMS ) that gives an estimated standardized heart rate F _HR given the acceleration deviation A _RMS . In addition, the slope of the part where _FHR changes linearly (the interval between acceleration of around 0.05 and around 0.45) corresponds to the standard heart rate response coefficient, and the intercept of the approximation curve corresponds to the standard intercept heart rate. do.

Note that the large-scale data may be data of a plurality of workers at the site for the past several days, or may be accumulated data sampled in advance at another site. Preferably, a standard heartbeat response model is created based on large-scale data obtained by measuring a large number of workers engaged in the same work as the worker. This is a heartbeat response model optimized for the work in question, and is considered to represent a typical heartbeat response of a worker engaged in the work. There are no particular restrictions on the number of people on whom the large-scale data is based, but the larger the number of samples, the more accurately the heart rate response can be approximated. It is preferably 5 or more, more preferably 50 or more. There are no particular rules regarding the accumulation period, but it is preferable to acquire data for two days or more, more preferably five days or more, at the same site.

Based on the standardized heart rate and the acceleration deviation thus obtained, the evaluation determination unit 22 calculates the work burden index of the worker 10 (step S109).

At this time, the evaluation determination unit 22 determines whether to use the standardized heart rate or the estimated standardized heart rate using a correction map created based on the standardized heart rate response model. A specific method for determining the central heart rate and the acceleration deviation from the heart rate data obtained from the biosensor 11, which is a measuring device, and the numerical value of the three-dimensional acceleration sensor, and selection criteria for the heart rate data using the correction map, etc. will be described later. I will elaborate.

On the other hand, if the heartbeat waveform detection rate Q is not equal to or higher than the reference value (50% in the above example) ("No" in step S105), the evaluation determination unit 22 determines that the credibility of the obtained heartbeat data is low. Therefore, instead of calculating the central heart rate, the acceleration deviation, which is a numerical value indicating the operation status of the worker 10, is calculated from the acceleration data obtained from the biosensor 11 (step S110). In this case, the evaluation determination unit 22 calculates the work burden index of the worker 10 based only on the acceleration deviation (step S111).

The work load index obtained in step S111 is considered to be less accurate than the work load numerical value obtained in step S109 because the heartbeat data is not reflected. Therefore, it is preferable that the work burden index is obtained continuously rather than not calculating the work burden index during that period on the grounds that the heartbeat data is not obtained.

Furthermore, the evaluation determination unit 22 calculates the heat load index of the worker 10 based on the temperature data inside the clothes of the worker 10 obtained from the biosensor 11 and the environmental temperature data at the site where the worker 10 works. (step S112). The environmental temperature data of the work site is the temperature data around the work site acquired by the weather information acquisition unit 25 of the cloud server 21, or the temperature sensor installed in the work site when the worker is working indoors. It can be obtained based on the obtained temperature information.

A specific procedure for calculating the heat load index based on the temperature data in clothes of the worker 10 and the environmental temperature data will be described in detail later.

Then, the evaluation determination unit 22 calculates the heat stroke risk of the worker 10 as a heat stroke risk index based on the obtained work load index and heat load index (step S113).

The heat stroke risk index in the heat stroke risk determination system shown in this embodiment can be determined as a linear sum of the work load index and the heat load index. Therefore, the greater the value of the heat stroke risk index, the higher the risk of the worker developing heat stroke. It can be ranked whether it is in a high (=dangerous) state, a slightly high (=caution) state, or a low (=safe) state. For this reason, depending on the rank of the heatstroke risk index calculated by the evaluation determination unit 22, the worker 10 himself or the site supervisor 30 who is the supervisor stops the work and takes a rest. It is possible to take countermeasures such as reducing the heat load by reducing the temperature inside the clothes or by lowering the temperature inside the clothes, thereby effectively avoiding the onset of heat stroke.

[Computer program]
The determination operation in the evaluation determination unit 22 described above using the flowchart of FIG. It can be implemented as a computer program that causes the computer used as the unit 22 to operate.

[Method for evaluating heatstroke risk]
Here, the algorithm for calculating the heat stroke risk index of the heat stroke risk evaluation method used in the heat stroke risk management system according to this embodiment will be described.

(Estimate of work load)
<a. Pretreatment>
First, heartbeat data and acceleration data are preprocessed to calculate a work load index.

As described with reference to the flowchart of FIG. 4, the preprocessing of the heartbeat data is to calculate the central heartbeat rate from the heartbeat data detected by the biosensor 11 worn by the worker 10 (step S106 in FIG. 4). is done in

More specifically, when the heart rate waveform detection rate of the partial section is 50% or more, the heart rate data acquisition interval included in the partial section is converted into the heart rate per partial section (for example, per past minute). to obtain the median heart rate HR.

On the other hand, for the acceleration data obtained by the acceleration sensor, the average value ΔA for the past one minute is obtained by the following procedure.

1) Exponential moving average of unequal time interval data For the acceleration data {Ax(t)}, {Ay(t)}, and {Az(t)} in the respective directions of the x-axis, y-axis, and z-axis, the time constant is The exponential moving average of the acceleration data in each axial direction is obtained using the exponential moving average method, which is a statistical method, at 10 sec. Although the time constant is not particularly limited, it may be appropriately determined within a range of, for example, 5 to 10 seconds according to the performance of the acceleration sensor.

Let {Sx(t)}, {Sy(t)}, and {Sz(t)} denote exponential moving averages in the x-axis, y-axis, and z-axis directions, respectively.

2) Removal of exponential moving average From the acceleration data of each axis, remove the above-mentioned exponential moving average and obtain detrended time-series acceleration. ”.

3) Calculation of the sum of squares Calculate the sum of squares at each time using the following formula (formula 2) for the detrended time-series acceleration

4) Average Acceleration per Minute Calculate the average value "ΔA ² _ave " of the sum of squares "ΔA ² (t)" obtained above per minute. Here, the average value is obtained by dividing by the number of data points. Also, the square root "ΔA _ave " of the mean square of acceleration "ΔA ² _ave " is calculated. where ΔA _ave is the acceleration deviation A _RMS .

<b. Removal of Outliers>
With respect to the heart rate data obtained from the heart rate data, non-numeric data, heart rates of 40 or less and heart rates of 180 or more are removed as abnormal values.

As for acceleration data, non-numerical data and data with ΔA of 0.05 or less or 0.55 or more are excluded as abnormal values.

<c. Calculation of Intercept Heart Rate and Heart Rate Response Coefficient>
A linear response interval in which the central heart rate changes linearly with respect to the acceleration deviation is set for a set of history data, and a regression line is obtained for the data included in the linear response interval. The slope of this regression line is the heart rate response coefficient αr, and the intercept is the intercept heart rate βr.

The method of fitting the regression line is not particularly limited. For example, a section in which the acceleration deviation is 0.05 to 0.4 is set as a linear response section, and is divided into m partial sections (m is 3 to 7, for example). Next, the median heart rate and the median acceleration deviation are determined for each partial interval. Then, a regression line is fitted to the median coordinates of the m points obtained.

<d. Calculation of Work Burden Index>
In FIG. 4, as shown in step S108, if there is history data of the worker 10 who is the subject, the standardized heart rate HR _S is calculated based on this history data and the standard heart rate response model. . By calculating the standardized heart rate HR _S , it is possible to correct individual differences in calculating the work load index from the heart rate data due to individual characteristics of the person being measured.

On the other hand, the estimated standardized heart rate estimates the standard heart rate from the subject's acceleration deviation A _RMS using the approximation formula F _HR (A _RMS ) of the standard heart rate response model. In calculating the actual workload index, the point is which of the standardized heart rate and the estimated standardized heart rate is more reliable. In this example, a correction map created based on a standard heart rate response model is used to determine which heart rate to select and obtain the corrected heart rate HR _s .

FIG. 7 is a first example of a correction map.

As shown in FIG. 7, the correction map describes an approximation curve F _HR 71 in which the y-axis intercept is the standard intercept heart rate βs and the slope of the straight line portion is the standard heart rate response coefficient αs. Here, the approximated curve F _HR 71 is used as the judgment line. It has been found that the determination line 71 ceases to be a straight line as shown in FIG. 7 from the portion where the acceleration deviation exceeds 0.45, and the degree of increase in the central heart rate with respect to the acceleration deviation decreases.

In the correction map shown in FIG. 7, a boundary line 72 is provided in a portion where the acceleration deviation indicating the motion of the worker who is the person to be measured is 0.2. In the area where the acceleration deviation is less than 0.2, the influence of emotion appears more than the change in the heart rate due to movement, and in the area where the acceleration deviation is greater than 0.2, the fluctuation of the heart rate due to body movement is large. This is because it is considered that

In the correction map shown in FIG. 7, the relationship between the heart rate and the acceleration deviation is corrected so as to fall within the hatched areas 73 and 77 in the map. For example, in the range of acceleration deviation up to 0.2, if the standardized heart rate is large and positioned above the judgment line 71, the numerical value of the judgment line 71, that is, the estimated standardized heart rate is used as the corrected heart rate HR _s , and if the standardized heart rate is smaller than the standard intercept heart rate βs, the value of the standard intercept heart rate βs is adopted as the corrected heart rate HR _s . If the standardized heart rate is equal to or less than the decision line 71 and equal to or greater than the standard intercept heart rate βs, the standardized heart rate is directly adopted as the corrected heart rate HR _s . By doing so, in a region where the acceleration deviation is less than 0.2, if a standardized heart rate greater than the estimated standardized heart rate is detected, it can be eliminated as an influence of emotion.

On the other hand, in the region where the acceleration deviation is greater than 0.2, the region where the heart rate value is judged to be too large is defined with the same slope as the above-mentioned standard heart rate response coefficient αs, that is, in parallel with the straight line portion of the approximated curve F _HR . A parallel line 76 is drawn, and it is determined that a correct heart rate has been detected within a region 77 sandwiched between the parallel line 76 and the determination line 71 . If this region 77 is applicable, the workload index is calculated using the standardized heart rate as it is as the corrected heart rate HR _s . If the standardized heart rate is greater than the upper limit parallel line 76, the value on the parallel line 76 is adopted as the corrected heart rate HR _s to eliminate the effect of error, as indicated by arrow 78 in the figure. In addition, the numerical value appearing in the area below the judgment line 71 is obtained by adopting the numerical value on the judgment line 71, that is, the estimated standardized heart rate as the corrected heart rate HR _s as indicated by an arrow 79 in the drawing. It is possible to avoid using a heart rate value that is too low for calculating the work load index even though the measurer is moving more than a certain amount.

The correction map shown in FIG. 8 is a correction map used when it is determined that the detected heartbeat data is more reliable.

A case where the reliability of the heartbeat data is high is a case where the detection rate of the heartbeat data acquired from the biosensor 11 is higher than the criterion (50% as an example), for example, when the state continues to exceed 80%. can be assumed.

The correction map shown in FIG. 8 is basically the same as the correction map shown in FIG. is different. As shown in FIG. 8, when the reliability of the heartbeat data is high, a boundary line 88 that defines the lower limit parallel to the judgment line 81 from the position of the standard intercept heart rate βs at the acceleration deviation of 0.2 is below the judgment line 81. , the standardized heart rate in the area 89 between the boundary line 88 and the judgment line 81 is used as the corrected heart rate HR _s , and when the standardized heart rate is smaller than the boundary line 88, the arrow 91 A value on the boundary line 88 as shown is taken as the corrected heart rate HR _s .

By doing so, it is possible to employ the standardized heart rate over a wide range and calculate a more accurate work load index.

<e. Evaluation of Work Burden>
Based on the corrected heart rate _HRc obtained using the correction map, the work load index W is calculated as follows.

First, the corrected heart rate HR _c is converted to the metabolic equivalent METs using the following formula (Formula 3).

METs=a _METs ×HR _c +b _METs (Equation 3)
Here, a _METs and b _METs are predetermined parameters and can be determined based on respirometric experiments.

Next, the metabolic equivalent METs is converted into a work burden index W using the following formula (Formula 4).

W=a _W ×METs+b _W (Formula 4)
where a _W and b _W are predetermined parameters.

For example, when a _W =0, 2 and b _W =−0.2 are set, the work load is evaluated as follows. A large amount of work, i.e., when the numerical value of W is 1 or more, can be regarded as work with an extremely high metabolic rate, that is, work that imposes a very heavy burden on the worker.

(Evaluation of heat load)
A heat load index H is obtained by the following formula (Formula 5) using the temperature Ti obtained by the measuring device 11 and the outside air temperature To obtained as the environmental temperature.

When the heat load index H is smaller than 0, H=0.

When the heat load index H is 0.6 or more, it can be evaluated that the heat load is relatively high, and when the heat load index H is 1 or more, it can be evaluated that the heat load is extremely high.

(Evaluation of heatstroke risk)
Using the work load index W and the heat load index H obtained by the above calculation, a heat stroke risk evaluation index R for the worker 10 to be evaluated is obtained as shown in the following formula (Formula 6).

Here, a is a numerical value defined corresponding to the heat acclimation of the worker to be evaluated. 3.

Regarding the heatstroke risk evaluation value R obtained as described above, if R is less than 0.6, the risk of developing heat stroke is low, and if R is 0.6 or more and less than 1.0, the alert level requires caution. , R is 1.0 or more, it can be determined that the risk is high and the risk level of developing heat stroke.

In addition, since it is not possible to verify the actual occurrence of heat stroke, when determining the criteria for the risk of developing heat stroke, it is necessary to make a more rigorous judgment of the risk of developing heat stroke, that is, to make decisions on the safer side. Should.

(Continuous evaluation of heatstroke risk)
Based on the measurement results obtained from the biosensor 11, which is a measuring device worn by the worker 10, when continuously evaluating the risk of developing heatstroke for the worker, the heat load index H and the work load index W Each exponential moving average value is obtained from the following formulas (Formula 7) and (Formula 8) with a sampling interval of 1 minute.

Here, w ₁ =2/31 and w ₂ =2/11.

Furthermore, the exponential moving average value of the heatstroke risk index R is obtained from the following formula (Formula 9).

For example, if the exponential moving average value of the heatstroke risk index R is 1 or more for 30 minutes or more, it is determined that the risk of developing heatstroke is extremely high, and the worker is asked to take a break. Take countermeasures to prevent heatstroke such as encouraging.

(Display on 2D map)
As can be seen from the above formula (Equation 6), the index R representing the risk of developing heat stroke in the heat stroke risk management system according to the present embodiment is the sum of the heat load index H and the work burden index W for the worker 10. Expressed as a linear sum.

Using this fact, the heat stroke risk index can be displayed as a heat stroke risk index on a two-dimensional map with the heat load index and the work load index as axes. For example, by displaying on a two-dimensional map a symbol corresponding to the current heatstroke risk index for each of the plurality of workers 10 managed by the site supervisor 30 who is the manager, the site supervisor 30 can grasp the overall risk index of the workers to be managed at a glance.

FIG. 9 is a diagram showing a state in which the heat stroke onset risk index obtained for each of a plurality of workers is shown on a two-dimensional map in the heat stroke onset risk management system according to the present embodiment.

In the map shown in FIG. 9, the vertical axis 101 indicates the work load index W, and the horizontal axis 102 indicates the heat load index H, respectively. In the formula (Equation 6) representing R, if the heat stroke risk index is 0.6 or more, the area of "caution", if the heat stroke risk index is 1 or more, the risk of heat stroke is an extremely high "dangerous" area, depending on the position where the work load index W and the heat load index H are 0.6 and 1.0 for the heat stroke risk index respectively, on the map 2 describes two boundary lines (103, 104) of the area.

By doing so, the area between the origin and the first boundary line 103 is divided into a "safe" area 105 with a low risk of developing heat stroke, and the first boundary line 103 and the second boundary line 104. The area between is a "caution" area 106 with a relatively high risk of developing heat stroke, and the area outside the second boundary is a "dangerous" area 107 with a heat stroke risk evaluation value of 1 or more. can do.

If the heatstroke risk index of each worker is displayed as a “▼” mark (symbol 108) based on the work burden index and the heat load index on the map in which the areas are divided in this way, The site supervisor can grasp, at a glance, how many of the workers to be managed by him/herself correspond to each level of the heatstroke risk index.

Furthermore, for example, when the workers indicated by the “▼” mark 108 are displayed biased toward the vertical axis 101 indicating the work load index, the heat load on the workers to be managed tends to be not so large. It is possible to understand whether the factor that increases the risk of developing heat stroke is the work burden or the heat load, and by taking measures to reduce each burden, it is possible to reduce the risk of heat stroke for all workers. The onset risk can be reduced.

As described above, in the heat stroke onset risk management system according to the present embodiment, the heat stroke onset risk index of an individual evaluator is displayed on a two-dimensional map with the work burden index and the heat load index as their respective axes. can be expressed as For this reason, for example, a two-dimensional map is displayed on the display screen of the manager information terminal possessed by the site supervisor who supervises multiple workers, and the heatstroke risk index of each worker to be managed is collectively displayed on the map. By displaying, it is possible to understand at a glance the risk of developing heat stroke for each worker and the overall tendency of the risk of developing heat stroke for workers to be managed.

In addition, by displaying it on a two-dimensional map, it is possible to visually understand which of the work load and the heat load increases the risk of developing heat stroke. By displaying one's own heat stroke risk index on a two-dimensional map, the worker himself/herself can take more effective measures to prevent the risk of heat stroke from increasing.

(physical condition evaluation)
In the above embodiment, the algorithm for evaluating the risk of developing heat stroke in the risk management system for developing heat stroke has been described. This algorithm detects the work load of the person being evaluated as a factor of the risk of developing heatstroke, and detects the heartbeat of the person being evaluated and the details of the movement by a three-dimensional acceleration sensor using a measuring device worn by the worker. It was evaluated by Such a physical condition of the person being evaluated can be used not only for evaluating the risk of developing heat stroke, but also as means for evaluating the physical condition of the person being evaluated under a wider range of circumstances.

A specific method for adjusting the physical condition of the person to be evaluated based on the heart rate and motion of the person to be evaluated will be described below.

a. Wearing of Measuring Device The heartbeat of the person to be evaluated and the movement of the three-dimensional sensor can be detected by the measuring device 11 worn by the worker described in the above embodiment. In particular, by using an undershirt having the electrodes 11b for detecting the heartbeat on the back side (the side facing the body surface) as shown in FIG. can touch the chest of the person being evaluated, and can stably detect the heartbeat with high accuracy.

In addition, the three-dimensional acceleration data indicating the heartbeat and movement of the evaluator measured by the measuring device placed on the undershirt is transmitted to the mobile terminal possessed by the evaluator, and the information is further transmitted to the Internet from the mobile terminal. By adopting a system configuration for transmission, it is possible to reduce the sense of incongruity experienced by the person to be evaluated when wearing the measurement device.

Of course, in various evaluation scenarios, such as evaluating the health condition of an athlete during training, the measurement device is placed on the chest of the undershirt worn by the person being evaluated. It is not essential for the evaluator to have a separate mobile terminal, and the measuring device that detects the heartbeat and movement of the evaluator can be worn more comfortably, and the data detected by the measuring device can be used to evaluate the health condition. There is no limitation on specific means as long as it is configured to be able to transmit to the evaluation determination unit.

b. Evaluation Algorithm The algorithm for evaluating the health condition is basically the same as the algorithm for calculating the workload index in the heat stroke risk evaluation described above.

c. Preprocessing First, the heartbeat data and the acceleration data are preprocessed before calculating evaluation values.

The heartbeat data is preprocessed in the same manner as the preprocessing for heatstroke risk assessment described above to obtain the central heart rate HR for one minute.

Regarding the preprocessing of the acceleration data obtained by the acceleration sensor, in the same way as in the evaluation of the risk of developing heat stroke, the detrended time series acceleration is squared at each time, and the sum of squares obtained Calculate the average value "ΔA ² _ave " of "ΔA ² (t)" every minute, and calculate the square root "ΔA _ave " of the root mean square of acceleration "ΔA ² _ave ".

d. Removal of Abnormal Values Similarly, removal of abnormal values is performed by removing non-numerical data for acceleration, and excluding data with ΔA of 0.05 or less or 0.55 or more. As for the heartbeat data, non-numerical data and those with a heartbeat of 40 or less or 180 or more are excluded.

e. Calculation of Heart Rate Index and Physical Fitness Index A regression line is obtained from the graph showing the correlation between the central heart rate and the acceleration deviation in the same manner as in the calculation of the work load index described above, and the heart rate index HR ₀ , which is the intercept of the regression line, A physical strength index PI calculated based on the slope α of the regression line is obtained.

A method of calculating the heart rate index HR ₀ will be described with reference to FIG.

FIG. 6 plots the acceleration deviation and the center heart rate obtained from the measurement data, with the horizontal axis representing the acceleration deviation and the vertical axis representing the center heart rate. A regression line 61 is applied to data whose acceleration deviation falls within the range of 0.05 to 0.4. An intercept 62 of this regression line 61 is the heart rate index HR ₀ . Also, using the slope α of the regression line 61, the physical strength index PI is obtained by the following formula (formula 10).

PI=α/αs (Equation 10)
where αs is the standard heartbeat response coefficient, which is a parameter of the standard heartbeat response model.

However, the method of calculating the exercise index PI is not limited to this, and may be obtained by linearly transforming the slope α of the regression line using a predetermined formula. For example, in order to make the index easy to understand, it may be obtained by multiplying it by a constant and summing it by a constant so that 1 is a normal value.

The health condition of the person to be evaluated can be evaluated from the heartbeat index and physical strength index obtained as described above.

By comparing the heart rate index and physical fitness index obtained in this way with large-scale data obtained from a large number of people and past measurement results of the person being evaluated, changes in the person's physical condition at the time of evaluation can be estimated. can be detected.

FIG. 10 is a diagram showing the degree of correlation between the values of the heartbeat index and the physical strength index, which are the physical condition evaluation indices described in the present embodiment, and the results of a questionnaire about the health condition of workers.

In FIG. 10, FIG. 10(a) shows the correlation between evaluation values before starting work and questionnaire results, and FIG. 10(b) shows the correlation between evaluation values after work and questionnaire results.

As shown in FIG. 10( a ), 1689 workers who answered “no problem” to the questionnaire about their health condition conducted before starting work, and 1,689 workers who answered “feeling tired/malaise” and “had a headache”. Among the 126 workers who answered "I am sick" who answered either "Yes" or "I have abdominal pain", there was clearly a significant difference between the values of the heartbeat index and the physical fitness index. In addition, the difference in the standard deviation in each worker group is quantified and shown on the upper side of each graph.

In addition, as shown in FIG. 10(b), 1641 workers answered "no symptoms" to a questionnaire about their health condition after work, and 1,641 workers answered "dizziness or lightheadedness" and "hot face." Even among the 137 workers with symptoms who answered one of the following: "I have cramps in my limbs," "I have cramps in my legs," and "I have headaches and nausea." There was a significant difference in

An increase in the heart rate index reflects activation of the sympathetic nervous system, and the value increases due to psychological stress and fatigue. It is also expected to increase due to heat load, which is used as an index for the above-mentioned heatstroke risk evaluation value. On the other hand, the physical fitness index reflects the response performance of the heart rate to exercise and work load. This physical fitness index is thought to decrease due to aging and cardiovascular disease, so if the value is less than 1, it is recommended to refrain from heavy-duty work.

As described above, it was confirmed that the physical condition of the person being evaluated can be evaluated from the measurement data of the person's heartbeat and movement by the algorithm described in this embodiment.

In the above algorithm for physical condition evaluation, a heart rate index, which is an estimated resting heart rate, and a fitness index are calculated from the relationship between the actual heart rate and body movement of the subject. More than a certain amount of data is required for fluctuations in heart rate data associated with body movements of the evaluator. Therefore, in order to accurately evaluate the health condition, it is preferable to obtain measurement data in the morning, half a day in the afternoon, or a whole day before calculating the evaluation result.

In addition, by adding the physical condition evaluation results obtained in this way to the heat stroke onset risk evaluation, there is a possibility that heat stroke can be prevented more reliably. In this case, for example, the physical condition of the person being evaluated at the time of evaluation is indexed, or a plurality of areas are divided to determine which area is included, and the heatstroke risk index is calculated according to the index or the corresponding area. It is conceivable to change the criteria for

Furthermore, the above-described work load index can be converted into numerical values such as calories and _METs , which are general indices for determining work load. For this reason, for example, in the heatstroke risk management system described in the present embodiment, by displaying the work load as a calorie display or a MET _S value on the mobile terminal carried by the worker, the work load of the worker can be calculated. can arouse interest in By doing so, workers will more actively participate in the heatstroke risk management system, reducing the risk of heatstroke and accumulating large-scale data necessary for more accurate risk assessment. will contribute to

The work load estimation method, physical condition evaluation method, and heatstroke risk evaluation method disclosed in the present application are, for example, physical condition evaluation of workers who work in a state where physical load and thermal load are large, such as construction sites and transportation industry. It is useful as a method for managing the risk of developing heat stroke. Further, the computer program disclosed in the present application is useful for operating a computer used as control means for executing the workload estimation method, the physical condition evaluation method, and the heat stroke onset risk evaluation method disclosed in the present application. In particular, by adopting the risk of developing heat stroke, which has become a social problem, in a management system for administrators to manage, a physical condition management system that allows administrators to easily grasp the physical condition of the person being managed under various circumstances. can be realized.

10 Workers (Persons to be Managed, Persons to be Evaluated)
11 biosensor (measuring device)
22 Risk assessment department 30 On-site supervisor (manager)
31 PC (administrator information terminal)
35 display image processing unit 36 display device

Claims (8)

A work burden estimation method for calculating a work burden index of an evaluator by a computer operated by a computer program,
using a measuring device comprising heartbeat detection means for detecting heartbeat data of an evaluator and acceleration detection means for detecting acceleration data associated with the action of the evaluator,
A task of creating a standard heartbeat response model based on the heartbeat data and the acceleration data obtained by measuring a plurality of persons, and using the standard heartbeat response model to indicate the intensity of the work performed by the person being evaluated. A work burden estimation method, characterized by calculating a burden index. 2. The work load estimation method according to claim 1, wherein said standard heartbeat response model is created with a plurality of persons engaged in the same work as measurement targets. A standardized heart rate is calculated by correcting the heart rate data of the person to be evaluated based on historical data, which is the heart rate data and the acceleration data of the person to be evaluated during a predetermined past period, and the standard heart rate response model. 3. The work load estimation method according to claim 1 or 2, wherein said work load index of said person to be evaluated is calculated based on said standardized heart rate. calculating an estimated standardized heart rate of the person to be evaluated based on the acceleration data of the person to be evaluated and the standard heart rate response model, and calculating the workload index of the person to be evaluated based on the estimated standardized heart rate The work load estimation method according to claim 1 or 2, wherein 2. The work load estimation method according to claim 1, wherein when the work load index is calculated for the person to be evaluated for the first time, the work load index is calculated based only on the acceleration data. using the measuring device further comprising temperature detection means for detecting the temperature inside the clothes of the person being evaluated,
the work load index calculated by the work load estimation method according to any one of claims 1 to 5;
Based on the temperature inside the clothes measured by the measuring device and the heat load index of the person to be evaluated calculated based on the environmental temperature,
A method for evaluating the risk of developing heat stroke, which comprises calculating a heat stroke risk index for the person to be evaluated. 7. The heat stroke risk evaluation method according to claim 6 , wherein the heat stroke risk index of the person to be evaluated is displayed on a two-dimensional map having the work load index and the heat load index as axes. A computer is used as control means, and the control means is caused to execute either the workload estimation method according to any one of claims 1 to 5 or the heat stroke onset risk evaluation method according to claim 6 or 7 . A computer program characterized by:

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