Biological information processing method and biological information processing system

Abstract

To provide a biological information processing method for processing data from a plurality of forms as a biological information acquisition section to acquire biological information of an evaluated person, and also to provide a biological information acquisition system capable of adopting the biological information acquisition sections in the plurality of forms.SOLUTION: A biological information processing method includes: a process for acquiring biological information of an evaluated person by a biological information acquisition section; and a process for calculating an index which indicates a state of the evaluated person based on the acquired biological information. The biological information acquired by the two or more kinds of different biological information acquisition sections is converted into a common parameter so as to calculate the index.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present application relates to a biometric information processing method and a biometric information processing system for evaluating the physical condition of a person to be evaluated based on biometric information obtained from the person to be evaluated. [Background technology]

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

[0003] As an example of a bio-information processing system that evaluates and manages such physical condition, a heatstroke risk management system has been proposed that uses a wearable detection device for detecting bio-signals, which is equipped with a three-dimensional acceleration sensor that grasps the body movements of the person being evaluated and a bio-information acquisition unit that detects heart rate, to constantly evaluate the risk of the person being evaluated developing heatstroke and enable measures to be taken to reduce that risk (see Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2018-130531 A Summary of the Invention [Problem to be solved by the invention]

[0005] In the above-mentioned conventional heat stroke risk management system, a biometric information acquisition unit having an electrometer for detecting the wearer's heart rate, a three-dimensional acceleration sensor for detecting the wearer's body movement, and a temperature sensor for detecting the temperature inside the clothing is placed on the chest of the undershirt, and the biometric information acquired by this biometric information acquisition unit is transmitted to an information processing unit of a cloud server on the Internet via a communication device such as a smartphone carried by the person being measured. The information processing unit evaluates the risk of developing heat stroke for each person, and for a group of workers working in the same environment, and instructs workers who are at high risk of developing heat stroke to take a break, thereby reducing the risk of developing heat stroke.

[0006] As biometric information acquisition units equipped with sensors that detect the subject's pulse, body movements, body temperature, etc., various types have been proposed that differ in the method of acquiring biometric information, the shape of the device, and the method of wearing, such as wearable clothing types in which an electrometer or the like is attached to the subject's body using a shirt or the like, as used in the conventional heatstroke risk management systems described above, watch-type types that detect the pulse using vibration or optical methods, which are easy to put on and take off and reduce the discomfort caused by wearing a biometric information acquisition unit, and types that are worn on the earlobe or fingertip to optically detect the subject's pulse.

[0007] These various types of biometric information acquisition units have different types of biometric information and their accuracy due to their configuration constraints. In addition, these biometric information acquisition units are incorporated into unique biometric information processing systems, and the settings for the frequency of detecting biometric information data and the frequency of data transmission are different. Furthermore, due to differences in the shape and mounting location of each biometric information acquisition unit, the data transfer capacity within the adopted system, and the data processing functions incorporated in the biometric information acquisition unit, the data processing specifications, such as the extent to which data processing is performed before the acquired data is sent from the biometric information acquisition unit, are also different. For this reason, in the conventional biometric information processing system using a biometric information acquisition unit, the biometric information acquisition unit worn by the subject was limited to one specific to that system.

[0008] However, in a biometric information processing system, the accuracy of the evaluation results such as the physical condition evaluation based on the biometric information is improved by collecting more biometric information of the subjects. In order to increase the amount of acquired biometric information to improve the accuracy of the evaluation results, to build a biometric information processing system at a lower cost, to acquire biometric information in a more appropriate form according to the environment in which the subject is placed and the type and intensity of the subject's movements such as the type of exercise or work, and to acquire biometric information under less stress according to the preferences of each subject, it is preferable to have a biometric information processing system that can use biometric information from various different types of biometric information acquisition units.

[0009] The present application aims to solve the problems associated with the above-mentioned conventional technology, and aims to provide a biometric information processing method capable of processing data from multiple forms as a biometric information acquisition unit that acquires the biometric information of the subject, and a biometric information processing system that can employ multiple forms of biometric information acquisition units. [Means for solving the problem]

[0010] In order to solve the above problems, the biometric information processing method disclosed in this application comprises a step of acquiring biometric information of the person being evaluated using a biometric information acquisition unit, and a step of calculating an index representing the condition of the person being evaluated based on the acquired biometric information, and is characterized in that the biometric information acquired by two or more different types of biometric information acquisition units is converted into a common parameter to calculate the index.

[0011] In addition, the biometric information processing system disclosed in the present application comprises two or more different types of biometric information acquisition units that acquire the biometric information of the person being evaluated, and a data processing unit that calculates an index representing the condition of the person being evaluated based on the acquired biometric information, and is characterized in that the data processing unit converts the biometric information acquired by the biometric information acquisition units into a common parameter and calculates the index. Effect of the Invention

[0012] With the above configuration, the biometric information processing method disclosed in the present application uses the biometric information of the subject acquired by two or more types of biometric information acquisition units to calculate various indices representing the state of the subject, that is, by converting the biometric information acquired from different types of biometric information acquisition units into common parameters, differences in data accuracy and data specifications can be absorbed. Therefore, it is possible to calculate indices representing the state of the subject with high accuracy using the biometric information of many subjects, and it is possible for the subject to select a type of biometric information acquisition unit that is more preferred.

[0013] Furthermore, with the above-mentioned configuration, the biometric information processing system disclosed in the present application has a wider range of options for the biometric information processing units to be adopted in the system, making it possible to realize a biometric information processing system that is lower cost, has higher accuracy, and is more easily accepted by the person being evaluated. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a block diagram showing the configuration of each part of a heat stroke risk management system which will be described as an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating the configuration of a first biological information acquisition unit used in the heat stroke development risk management system described in this embodiment. [Diagram 3] FIG. 3 is a diagram illustrating a second biological information acquisition unit used in the heat stroke development risk management system described in this embodiment. [Figure 4] FIG. 4 is a diagram illustrating a third biological information acquisition unit used in the heat stroke development risk management system described in this embodiment. [Diagram 5] FIG. 5 is a flowchart illustrating a method for assessing the risk of developing heatstroke in the heatstroke risk management system described in this embodiment. [Figure 6]Fig. 6 shows the measurement results of the standard heart rate response, which indicates the heart rate response to the acceleration deviation. Fig. 6(a) shows a plot of all the approximately 3 million data points, and Fig. 6(b) shows the standard heart rate response obtained using the median value of the heart rate response to the acceleration data. [Figure 7] FIG. 7 is a diagram for explaining a method of estimating a heart rate index and a physical fitness index in the biological information processing method according to the present embodiment. [Figure 8] FIG. 8 is a diagram for explaining a first correction map for determining a workload index in the workload estimation method explained in this embodiment. [Figure 9] FIG. 9 is a diagram for explaining a second correction map for determining a workload index in the workload estimation method explained in this embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] The biometric information processing method disclosed in this application comprises the steps of acquiring biometric information of the person being evaluated using a biometric information acquisition unit, and calculating an index representing the condition of the person being evaluated based on the acquired biometric information, and converts the biometric information acquired by two or more different types of biometric information acquisition units into a common parameter to calculate the index.

[0016] By having the above-mentioned configuration, the biometric information processing method disclosed in the present application expands the range of options for the biometric information acquisition unit worn by the person being evaluated, making it possible to acquire more of the subject's biometric information in a manner that places less burden on the person being evaluated, and enabling accurate indicators to be calculated using the acquired biometric information.

[0017] In the above bioinformation processing method, it is preferable that the index is at least one of a work load index indicating the degree of the load that the subject is under, a physical condition evaluation index indicating the degree of change from the normal physical condition of the subject, and a heat stroke risk index indicating the degree of risk of the subject developing heat stroke. In this way, a practical index that indicates the condition of many subjects can be obtained.

[0018] It is also preferable that the biometric information is at least one of heart rate data, acceleration data, and METs (estimated metabolic equivalents). In this way, it is possible to correct differences in characteristics between individuals and differences in the specifications of the biometric information acquisition unit, obtain biometric information necessary to accurately determine the state of the person being evaluated, and accurately calculate an index representing the state of the person being evaluated. The heart rate data includes the heart rate and the heartbeat time interval. The biometric information may also include an estimated value of energy consumption.

[0019] Furthermore, it is preferable that the common parameter used for the heart rate data is the median heart rate. In this way, accurate heart rate data of the subject can be obtained regardless of the method of acquiring heart rate data or the data processing specifications in the bioinformation acquiring unit.

[0020] Furthermore, it is preferable that the common parameter used in the acceleration data or the METs (estimated metabolic equivalent) is the acceleration deviation. In this way, the motion of the subject can be accurately grasped regardless of the method of grasping the body movement of the subject in the bioinformation acquisition unit or the data processing method.

[0021] The biometric information processing system disclosed in the present application comprises two or more different types of biometric information acquisition units that acquire the biometric information of the person being evaluated, and a data processing unit that calculates an index representing the condition of the person being evaluated based on the acquired biometric information, and is characterized in that the data processing unit converts the biometric information acquired by the biometric information acquisition units into a common parameter and calculates the index.

[0022] By having the above-mentioned configuration, the biometric information processing system disclosed in the present application provides a wider range of options for the biometric information acquisition unit, allowing the person being evaluated to select a biometric information acquisition unit that corresponds to his or her own preference and the physical condition of the body being measured, and can easily calculate indexes representing the condition using the biometric information of more people being evaluated, thereby realizing a biometric information management system that can obtain accurate evaluation results at low cost.

[0023] Hereinafter, embodiments of a biological information processing method and a biological information processing system disclosed in the present application will be described with reference to the drawings.

[0024] (Embodiment) [Overall system configuration] First, an overall configuration of an example of a biological information processing system disclosed in the present application will be described.

[0025] In this embodiment, a heat stroke risk management system is illustrated, which evaluates the physical condition of a worker based on a work load index indicating the magnitude of the load caused by work and a thermal load index indicating the magnitude of the thermal load on the worker, based on the worker's movements, environmental temperature, heart rate, etc., and evaluates and manages the risk of heat stroke. The heat stroke risk management system according to this embodiment is preferably used, for example, with multiple workers working at a construction site as subjects, and is a system that aims to reduce the risk of heat stroke at a construction site by evaluating the risk of heat stroke based on biological information obtained from each worker and taking measures such as issuing a warning to workers who are at increased risk of heat stroke and having them take appropriate breaks.

[0026] FIG. 1 is a block diagram showing an example of the configuration of each part of a heat stroke development risk management system according to this embodiment.

[0027] As shown in FIG. 1, the heat stroke risk management system according to this embodiment is composed of a worker 10 who is the subject of evaluation, a cloud server 21 on the Internet 20 that evaluates the physical condition of the worker 10 based on the biometric information of the worker 10 and evaluates the risk of developing heat stroke, a site supervisor 30 who is the manager who supervises the worker 10 who is the subject of evaluation and a work group including a certain number of workers 10, and a business establishment 40 that manages the heat stroke risk assessment system by having multiple site supervisors 30 under its management to grasp the whole picture. It goes without saying that the above is a general example assuming a general construction site, and that the heat stroke risk management system of this embodiment can take different forms as appropriate depending on the configuration of the site where it is actually introduced, such as when one site supervisor 30 manages one worker 10, when the site supervisor and the business establishment are inseparable, or when multiple business establishments are included and the entire construction site is managed on a larger scale.

[0028] In the heat stroke risk management system described in this embodiment, each worker 10 wears a measuring device 11, which is a biological information acquisition unit capable of detecting at least one of biological information such as his / her own heart rate data, acceleration data showing body movement, and clothing temperature data as environmental temperature. At least two types of measuring devices 11 are used.

[0029] For example, some workers wear undershirts with a biosensor 11a attached to the chest as a first measuring device, the biosensor 11a being equipped with a temperature sensor for detecting the temperature inside the clothes, an electrometer for detecting heart rate data, and a three-dimensional acceleration sensor for detecting body movements. Another worker wears a wireless pulse meter 11b as a second measuring device, the pulse meter 11b being equipped with a measuring unit attached to the earlobe for optically measuring the pulse, and a main unit having a three-dimensional acceleration sensor attached to a position near the center of the body. Still another worker wears a wristwatch-type sensing unit 11c as a third measuring device, the pulse sensor capable of detecting the pulse as minute vibrations, a three-dimensional acceleration sensor, and sensors for detecting temperature and humidity.

[0030] In addition, the biometric information acquisition unit that can be used in the heatstroke risk management system of this embodiment is not limited to the above examples, and various types of biometric information acquisition units can be used that are equipped with the subject's heart rate data, an acceleration sensor that indicates physical movement, a temperature sensor that measures environmental temperature, etc.

[0031] In addition, as a means for attaching the biosensor 11a, which is the first bioinformation acquisition unit employed in the heat stroke risk management system according to this embodiment, to the body surface of the worker 10 at the chest or center of the body, various means can be adopted, such as a belt method in which the biosensor 11a is fixed using a belt, or a measurement patch method in which the biosensor 11a is placed on an adhesive sheet. The specific configuration of the measurement device 11, which is the bioinformation acquisition unit, and details of data processing of the bioinformation data of the worker 10 acquired by each measurement device 11 will be described in detail later.

[0032] In the heat stroke risk management system according to this embodiment, each worker 10 carries a smartphone 12 as a mobile terminal. The measurement device 11 and the smartphone 12 carried by the worker 10 are constantly connected by short-range communication such as Bluetooth (registered trademark), and biological information acquired by the measurement device 11 is sent to the smartphone 12 at any time.

[0033] The smartphone 12 includes a data receiving unit 15 and a data transmitting unit 16, and is constantly connected to the Internet 20 as a network environment via a wireless LAN or a mobile phone information carrier. In the heat stroke risk management system of this embodiment, the smartphone 12 is linked to the identification data of each worker 10, and the smartphone 12 includes an assessee information transmitting unit 13, and uses the data transmitting function of the smartphone 12 to transmit the biometric information linked to the worker's identification information to a cloud server 21 located on the Internet 20.

[0034] In addition, the measuring device worn by the worker can be linked to an ID that identifies the work itself in a variety of ways, such as the worker inputting the name or management number of the measuring device into his or her smartphone, reading a two-dimensional or three-dimensional identification code attached to the measuring device using the image recognition function of the smartphone, using an identification code in short-range communication between the smartphone and the measuring device, or other methods selected by the worker using an application on the smartphone. In addition, if the smartphone itself is not owned by the worker but is lent as part of the system use, the worker can be recognized by entering data using the smartphone, reading an identification code, using a face recognition system, or other methods.

[0035] In addition, since the smartphone 12 is capable of receiving data, emitting sound, and displaying images, the heatstroke risk management system of this embodiment is equipped with a warning notification unit 14 for performing a warning notification function of informing the worker 10 of the risk of developing heatstroke and urging him or her to take a break, and an image display unit 17 for performing the function of easily displaying the heatstroke risk assessment results for each worker 10 and the entire group to which that worker 10 belongs.

[0036] The cloud server 21 includes a data receiving unit 23 and a data transmitting unit 26, and transmits and receives information via the Internet 20. The cloud server 21 also includes an evaluation and determination unit 22 as a data processing unit, and acquires biometric information data of all the workers 10 who are targets of the heat stroke risk management system, and calculates, for each worker 10, a work burden index indicating the degree of the load caused by the work and a physical condition evaluation index indicating the degree of change in physical condition from the worker's usual normal state, and calculates a heat stroke risk index indicating the degree of risk of developing heat stroke for each worker 10 based on these indices. The evaluation and determination unit 22 can also manage the risk of developing heat stroke for groups of workers 10 formed based on the commonality of work content and working environment.

[0037] In the heat stroke risk management system according to this embodiment, the risk of heat stroke of each worker 10 is managed, and when the risk of heat stroke is judged to be particularly high, the information is transmitted to the worker to encourage the worker to take measures to reduce the risk of heat stroke. For this purpose, the cloud server 21 evaluates and judges the risk of heat stroke, and when the risk of heat stroke is increasing, creates warning information to warn the worker of that fact.

[0038] In addition, the cloud server 21 has a weather information acquisition unit 25, which acquires weather information from an information site that provides weather information via the Internet 20, and can acquire weather conditions at the current time, such as temperature, humidity, and amount of sunlight, in the area where the worker 10 is working, as well as a weather forecast that anticipates changes within the next few hours, thereby enabling weather conditions to be taken into account when assessing the risk of developing heatstroke.

[0039] Furthermore, the cloud server 21 includes a data recording unit 24, which can chronologically record the measurement data and the creation history of warning information from each worker 10 registered in the heatstroke risk management system. This makes it possible to manage the risk of heatstroke based on the results of the current physical condition evaluation of each worker 10 on the day and the physical condition evaluation up to the previous day, and to perform a more accurate risk evaluation of heatstroke based on the results of past evaluations of the risk of heatstroke under similar weather conditions.

[0040] The cloud server 21 is connected via the Internet 20 to a personal computer 31 that serves as a manager information terminal used by a site supervisor 30, who is a manager who supervises the work of the worker 10, who is the person being evaluated, at the construction site. Therefore, the site supervisor 30 at the work site where the worker 10 is working can grasp, by a data receiving unit 33 of the personal computer 31, the biometric information data of the worker 10 transmitted from the cloud server 21 at any time, and whether or not warning information has been generated by the evaluation determination unit 22.

[0041] The evaluation and judgment unit 22 of the cloud server 21 evaluates the physical condition of the worker 10 based on the heart rate data, acceleration data, and temperature inside the clothing obtained from the measuring device 11 worn by the worker 10, and further calculates a work strain index and calculates a heatstroke development risk index for the worker 10 by taking into account the temperature inside the clothing and environmental temperature information of the work location obtained via the Internet.

[0042] The specific details of the workload estimation of the worker 10 and the heat stroke risk assessment performed by the evaluation and determination unit 22 will be described later.

[0043] The cloud server 21 can correct the assessment results of the risk of developing heatstroke for each individual worker 10 based on historical data as past history information of the worker 10 being evaluated recorded in the data recording unit 24, weather information for the work area acquired by the weather information acquisition unit 25, and environmental information such as changes in various information acquired from workers other than the worker being evaluated who work at the same site as the worker being evaluated, thereby managing the risk of developing heatstroke in a more realistic manner.

[0044] In the heat stroke risk management system exemplified in this embodiment, the evaluation and determination unit 22 is not limited to being provided in the cloud server 21. For example, various functions of the cloud server 21 may be implemented on an administrator information terminal or a management computer of a business establishment, and as long as the functions can be realized, the location or device on which the evaluation and determination unit is implemented is not important.

[0045] The site supervisor's personal computer 31 includes an information management unit 32 that manages various information obtained by the measuring device 11 for the workers 10, including the workers 10, who belong to the work site supervised by the site supervisor 30, and whether or not warning information has been generated. The information management unit 32 always keeps track of the latest information on the basis of the information obtained from each worker 10 and information that serves as the basis for evaluating the risk of developing heatstroke, such as whether or not warning information has been generated, based on the information transmitted from the cloud server 21. The information management unit 32 also outputs the acquired evaluation results of the risk of developing heatstroke for each worker 10 and other environmental information to a display image processing unit 35, which adjusts the screen content displayed on a display device 36, such as a liquid crystal monitor.

[0046] In this way, the site supervisor 30 can grasp the information of the workers 10 working at the work site he supervises, the risk of developing heat stroke, etc., in a unified manner as a whole, or as detailed information on each individual worker, on an easy-to-read screen. Note that the specific screen contents displayed on the display device 36 processed by the display image processing unit 35 need only be able to display the information required by the appropriately configured system in an easy-to-read manner, and therefore a detailed description of the specific contents will be omitted in this specification.

[0047] Furthermore, the site supervisor's computer 31 can check whether the worker 10 has taken measures to prevent the onset of heat stroke by checking for changes in the biometric information obtained from the worker 10 after notifying the warning information and receiving confirmation of receipt of the warning information from the worker 10.If the worker 10 has not taken any measures to prevent the onset of heat stroke, the site supervisor's computer 31 can further warn the worker 10 by, for example, repeatedly transmitting the warning information to the worker 10 in question.

[0048] In the above description, an example has been described in which the evaluation and judgment unit 22 of the cloud server 21 generates warning information informing the worker 10 that the risk of developing heatstroke is increasing, but the warning information can also be generated by the information management unit 32 installed in the computer 31 of the site supervisor 30. It is also possible to set both the evaluation and judgment unit 22 and the information management unit 32 to generate warning information. In this way, the computer 31 of the site supervisor 30 who actually supervises the work site generates warning information prior to the judgment result of the evaluation and judgment unit 22 and transmits it to the target worker 10, which may further reduce the risk of developing heatstroke depending on the actual conditions of the work site.

[0049] The warning information generated by the evaluation and judgment unit 22 of the cloud server 21 or the personal computer 31 of the site supervisor 30 is transmitted from the data transmission unit 34 of the personal computer 31 of the site supervisor 30 to the smartphone 12 carried by the worker 10 via a network including a local network such as a wireless LAN and an information carrier of a mobile phone. The warning notification unit 14 of the smartphone 12 that receives the warning information notifies the worker 10 that the risk of developing heat stroke is increasing using various information transmission means such as voice, screen display, lighting or blinking of a lamp, and vibration. The worker 10 who has confirmed the warning information reports that he or she has received the warning information through the touch panel or operation button of the smartphone 12, and takes measures to prevent heat stroke, such as stopping work and taking a rest.

[0050] The smartphone 12 of the worker 10 transmits to the computer 31 of the supervisor 30 a message indicating that the worker 10 has confirmed the warning information and stopped working, and the supervisor 30 can confirm that the worker 10 has taken measures to prevent the onset of heatstroke.

[0051] Furthermore, in the heat stroke risk management system described in this embodiment, the site supervisor 30 transmits the heat stroke risk data at the work site to the smartphone 12 of the worker 10, so that the worker 10 can check the current heat stroke risk at the work site where he or she works. For example, if it is confirmed that the risk of heat stroke of workers other than the worker 10 is high, each worker can take measures to proactively prevent the onset of heat stroke. Also, if it is known that there are other workers who have received warning information about the risk of heat stroke and stopped working, it is expected that the worker will respond more readily to the warning information from the site supervisor 30 addressed to him or her.

[0052] Furthermore, the smartphone 12 owned by the worker 10 can display on a screen the change in the risk of developing heat stroke of the worker 10 up to now, the heart rate acquired by the biosensor 11, the change in the physical condition assessment index calculated from the acceleration data, calories burned, and other related information, so that the worker 10 can refer to it himself. As for the display screen of the smartphone owned by the worker 10, it is sufficient if it can display the necessary information according to each purpose in an easily viewable manner, and therefore a detailed description thereof will be omitted in this specification.

[0053] The cloud server 21 is also connected to a management computer 41 in the company or business 40 to which the worker 10 belongs via the Internet 20, and transmits the measurement result information of the worker 10 transmitted to the computer 31 of the site supervisor 30 and various information used by the cloud server 21 to judge the risk of developing heat stroke to the management computer 41 of the business 40 in real time. The management computer 41 of the business 40 is equipped with its own data receiving unit 42 and data transmitting unit 43, and is also connected to the computer 31 of the site supervisor 30 via the Internet, and can confirm information such as whether the warning information from the site supervisor 30 to the worker 10 was correctly transmitted, whether the worker 10 took preventive measures against heat stroke, and the like, and can give predetermined instructions as necessary. This makes it possible to effectively back up the avoidance of the risk of developing heat stroke for the worker 10.

[0054] In addition, since the cloud server 21, the personal computer 31 of the site supervisor 30, and the management computer 40 of the business establishment 40 are connected over the Internet 20 environment, it is possible to access the cloud server 21 from the personal computer 31 and the management computer 40, thereby controlling the data processing content in the cloud server 21, updating the judgment program in the evaluation and judgment unit 22, and appropriately retrieving information necessary for heatstroke prevention and management from the cloud server 21.

[0055] In the above explanation, a smartphone is exemplified as a mobile terminal carried by a worker, but the mobile terminal of the worker is not limited to a smartphone, and can be a mobile phone, a tablet device, or even a dedicated small terminal device capable of sending and receiving information specialized for a heat stroke risk management system. Also, as the manager information terminal operated by the site supervisor, various information devices capable of sending and receiving information through a network, displaying data, recording data, etc., such as desktop computers, notebook computers, tablet computers, and small server devices can be used instead of the exemplified personal computers.

[0056] Furthermore, the above explanation describes a form in which warning information is sent from the site supervisor's manager information terminal to the worker's mobile terminal, but if the warning information is generated in the evaluation and judgment section of the cloud server, the system can also be configured to send the warning information directly from the cloud server to the worker's mobile terminal.

[0057] Furthermore, the means of communication between the workers, site supervisors, and the management department within the workplace are not limited to the above examples, and it goes without saying that various types of information communication means for transmitting and receiving data can be used.

[0058] [Biometric information acquisition section] (First measuring device) First, a first embodiment of the measuring device 11 as the bioinformation acquiring section will be described, which is a biosensor of the type that is brought into close contact with the chest of the worker who is to be evaluated and acquires bioinformation.

[0059] Fig. 2 is a diagram showing an example of the configuration of an undershirt to which a biosensor, which is a first bioinformation acquisition unit, is attached, worn by a worker in the heat stroke risk management system according to this embodiment. Fig. 2(a) shows the front side of the undershirt to which the biosensor is attached, and Fig. 2(b) shows the back side of the undershirt, i.e., the side that faces and contacts the body surface of the worker.

[0060] 2, a biosensor 11a is disposed on the chest of an undershirt 18 worn by a worker 10. More specifically, the biosensor 11a is composed of a data acquisition and transmission unit 11a1 disposed on a front surface 18a of the undershirt 18 in a central portion of the chest, and an electrode section 11a2 connected to the data acquisition and transmission unit 11a1 and disposed extending in the left-right direction on a back surface 18b of the undershirt 18, that is, on the side that comes into contact with the skin.

[0061] In the heat stroke risk management system according to the present embodiment, the worker 10 wearing the first biometric information acquisition unit detects the heartbeat, temperature inside the clothes, and movement of the worker 10 using the biometric sensor 11a. The electrode, which is a heartbeat detection means arranged on the back side of the undershirt 18, contacts the chest, so that the heartbeat of the worker 10 can be detected from the change in the surface potential. In addition, a temperature sensor (not shown) that detects the temperature inside the clothes and an acceleration sensor chip (not shown) that detects acceleration in three-dimensional directions are housed in the data acquisition and transmission unit 11a1. As described above, in the heat stroke risk management system according to the present embodiment, the smartphone 12 carried by each worker is used as a repeater of the measured biometric information, so that the data acquisition and transmission unit 11a1 only needs to include a minimum data processing circuit, a data transmission unit that performs short-distance communication, and a power source for driving these electronic circuits including each sensor, and the data acquisition and transmission unit 11a1 can be made small and lightweight to reduce the discomfort of the worker 10 wearing the undershirt 18 worn on the chest.

[0062] As described above, in the heat stroke risk management system described in this embodiment, various methods are known for attaching the biosensor 11a, which contacts the chest of the worker 10 to obtain heart rate data, temperature data inside the clothes, and acceleration data generated by movement. However, compared with a method of directly attaching the biosensor 11a to the chest as a measurement patch or a method of using an elastic attachment belt, the method of attaching the biosensor 11a to the undershirt 18 worn by the worker 10 as shown in FIG. 2 allows the worker 10 to obtain necessary information without feeling any special awareness of wearing the biosensor 11a. Furthermore, even if the worker 10 sweats or twists his / her body during work, the biosensor 11a attached to the undershirt 18 will not eventually come off the body surface of the worker 10, and the wearing position can be maintained substantially unchanged. Therefore, although some heart rate data may not be obtained at the moment the biosensor 11a leaves the chest of the worker 10, it is possible to avoid a situation in which no heart rate data can be obtained continuously.

[0063] In addition, the location of the biosensor 11a for acquiring the heart rate data of the worker 10 can be the worker's waist, back, upper arm, or leg, in addition to the chest of the worker described above. However, it goes without saying that it is preferable to limit the range where the acceleration sensor and temperature sensor built into the biosensor 11a can acquire good measurement data. In addition, in the case of evaluating the risk of developing heat stroke as a physical condition evaluation of an athlete who is training, for example, rather than as a system for managing the risk of developing heat stroke with a worker working at a construction site as the subject of evaluation as described in this embodiment, it is considered that the subject of evaluation will wear sportswear, and in this case, it is most reasonable to place the biosensor on the chest of the wear worn on the upper body.

[0064] (Second measuring device) Next, as an example of a second form of the measuring device 11 as a bio-information acquisition unit, we will explain a type of sensor that optically detects blood vessel contraction to detect the worker's pulse as heart rate data, and also detects the worker's body movements using a three-dimensional acceleration sensor.

[0065] FIG. 3 is a diagram showing a state in which a wireless type pulse meter is attached as a second measuring device for acquiring biological information of a worker in the heat stroke development risk management system according to the present embodiment.

[0066] As shown in FIG. 3, the wireless type pulse meter 11b is composed of a pulse detection part that is worn by pinching the earlobe with a pair of measuring pieces 11b1, 11b2 biased by a spring or the like, and a main body part 11b3 that is connected to the pulse detection part by a wire.

[0067] In the pulse detection section, a light source such as an LED is arranged on one of a pair of measuring pieces 11b1, 11b2, and a light receiving element such as a CCD is arranged on the other so as to face each other, and they can be attached and fixed to the earlobe by a biasing means such as a spring. The pulse of the worker 11 is detected from the contraction of the blood vessels in the ear photographed by the CCD. The main body 11b3 contains a control circuit that appropriately removes noise components from the image of the contraction of the blood vessels detected by the pulse detection section and outputs pulse rate data, an operating power supply for the entire pulse meter, a three-dimensional acceleration sensor, a data transmission section, etc.

[0068] In the wireless pulse meter 11b of the type shown in FIG. 3, in order to grasp the physical movements of the worker 11 being evaluated more accurately, it is preferable to attach the main body 11b3 on which the three-dimensional acceleration sensor is located to the upper body close to the torso of the worker 10, and it is considered preferable to attach it to the rear part of the collar, as shown in the figure, where movements different from the physical movements of the worker 10 are unlikely to occur.

[0069] The commercially available wireless pulse meter 11b shown in FIG. 3 does not have a function for measuring the environmental temperature, but if the main body 11b3 is attached to the back side of the collar as shown in the figure, the discomfort felt by the worker 10, who is the subject of evaluation, in wearing the main body 11b3 is smaller than that felt when the biosensor 11a shown in FIG. 2 is attached to the chest. For this reason, it is possible to incorporate various sensors for grasping the environmental conditions, such as a thermometer and a hygrometer, inside the main body 11b3. For the same reason, a transmission unit capable of directly transmitting bioinformation to the Internet environment can be provided inside the main body 11b3, in which case the worker 10 does not need to carry the smartphone 12 that was equipped with the subject information transmission unit 13. If the worker 10 does not carry the smartphone 12, a voice warning function may be incorporated inside the main body 11b3 of the wireless pulse meter 11b in order to perform the function of the warning notification unit 14 that was performed by the smartphone 12.

[0070] In the wireless pulse meter as the second measuring device shown in Fig. 3, the contraction of blood vessels in the earlobe is measured to obtain heart rate data, but the pulse can also be optically detected in places other than the earlobe, such as the fingertip. In addition, in the wireless pulse meter shown in Fig. 3, the pulse detection part and the main body are connected by wire, but if the operating power source for the pulse measurement part can be easily secured, the pulse measurement part and the main body can be connected by a short-distance wireless connection.

[0071] (Third measuring device) Next, we will explain the third form of measuring device 11 as a biometric information acquisition unit, which is a wristwatch-type sensor that is worn on the wrist using a belt and has a device body that includes a vibration sensor that detects pulse, an acceleration sensor that detects movement, and sensors that measure environmental conditions such as temperature and humidity.

[0072] FIG. 4 is a diagram showing a wristwatch-type sensing unit, which is a third measurement device, for acquiring biological information of a worker in the heatstroke development risk management system according to this embodiment.

[0073] As shown in Fig. 4, the sensing unit 11c is composed of a device main body 11c1 that is worn in contact with the wrist and a watchband-type fixed belt 11c2, and has an appearance similar to that of a wristwatch. Note that in the sensing unit 11c illustrated in Fig. 4, the device main body 11c1 is detachable from the fixed belt 11c2 for the purpose of easy maintenance.

[0074] The wristwatch-type sensing unit 11c is worn so that the device body 11c1 is on the outside of the wrist (the back of the hand), and a pulse detection unit (not shown) is arranged on the side that contacts the wrist (the inside of the fixed belt 11c2) to detect the pulse by pressing it against the outside of the wrist of the worker 10 who is to be evaluated. In addition, sensors that measure environmental information such as temperature, humidity, and air pressure, and a three-dimensional acceleration sensor that detects the movement of the worker 10 are arranged inside the device body 11c1, and small openings are formed on the outer surface of the device body 11c1 in parts corresponding to these sensors as shown in the figure. In addition, a connection electrode that supplies power to a secondary battery arranged inside the device body and enables data exchange with an internal memory element is arranged on the side of the device body (not shown), and it is possible to charge the operating battery of the sensing unit 11c and exchange data with a personal computer or the like by placing it on a dedicated cradle.

[0075] In addition, wristwatch-type measuring devices include so-called smart watches, which have a clock function that displays the current time by placing an image display device on the surface of the device body. Some smart watches are equipped with an optical heart rate sensor that measures the heart rate based on the absorption of infrared rays and have a carrier communication function. By using such a smart watch, it is possible to directly transmit bioinformation such as heart rate information obtained by the measuring device to a cloud server via the Internet.

[0076] In a wristwatch-type measuring device such as the sensing unit 11c illustrated in FIG. 4, which is used in the heat stroke risk management system according to the present embodiment, a transmitter that transmits the measured biometric information to the smartphone 12 carried by the worker 10 is provided inside the device body 11c1. Since the third measuring device is also worn on the outside of the wrist of the subject, the discomfort felt by the worker 10 in wearing the device body 11c1 is smaller than that felt when the biosensor 11a shown in FIG. 2 is worn on the chest, as with the wireless pulse meter, which is the second measuring device. Therefore, the sensing unit 11c shown in FIG. 4 can also be provided with a transmitter that can directly transmit biometric information to the Internet environment inside the device body 11c1. In addition, when the worker 10 does not have the smartphone 12 having the subject information transmission unit 14, the device body 11c1 will perform the function of the warning notification unit 14, but it is easy to adopt a configuration that provides a warning notification function to the worker 10 and displays data on the risk of developing heat stroke in the form of an image, including the case of a smart watch.

[0077] [Heat stroke risk assessment method] Next, the specific contents of the heat stroke risk assessment for an individual worker in the hot environment assessment in the heat stroke risk management system according to this embodiment will be described.

[0078] The heat stroke risk assessment method according to the present embodiment calculates a work burden index indicating the intensity of the work performed by the person being assessed based on the heart rate data of the person being assessed detected by a heart rate detection means provided in a measuring device worn by the person being assessed and acceleration data acquired by a three-dimensional acceleration sensor. Also, a heat load index of the person being assessed is calculated based on the temperature inside the person's clothes obtained from the measuring device and the environmental temperature of the site where the person being assessed is working. Then, a heat stroke risk index indicating the risk of developing heat stroke is calculated based on the calculated work burden index and heat load index.

[0079] In the following description of the heat stroke risk assessment method, each component of the heat stroke risk management system according to the present embodiment described with reference to FIG. 1 will be appropriately illustrated.

[0080] FIG. 5 is a flowchart showing the flow of heat stroke development risk evaluation in the evaluation and determination unit in the heat stroke development risk management system described in this embodiment.

[0081] In the heatstroke risk assessment system of this embodiment, the evaluation and determination unit 22, which is a control means provided in a cloud server 21 on the Internet, calculates a work burden index and a heat load index for the worker 10 based on data obtained from the biosensor 11a, which is a first measuring device serving as a bioinformation acquisition unit worn by the worker 10 to be evaluated, and data obtained from each component within the cloud server 21, and thereby calculates a heatstroke risk index.

[0082] As shown in FIG. 5, when the evaluation in the evaluation judgment unit 22 starts (START), the evaluation judgment unit 22 starts calculating the workload index of the person to be evaluated.

[0083] The start (START) of the evaluation in the evaluation judgment unit 22 can be set in various ways, such as the worker 10 himself or the site supervisor 30, who is the manager, turning on the power switch of the biosensor 11a, which is a measuring device, a timer being set to automatically start operating the biosensor 11 when it is time to start work, or the biosensor 11a itself detecting that the worker has put on an undershirt 18 equipped with the biosensor 11a and starting operation.

[0084] In order to calculate the work strain index, the evaluation and judgment unit 22 first checks whether or not historical data, which is heart rate data and acceleration data, of the worker 10 to be evaluated is recorded in the data recording unit 24 (step S101).

[0085] If the worker 10 has previously been evaluated in the heat stroke risk management system described in this embodiment and the data recording unit 24 has recorded the worker's history data (if "Yes" in step S101), a linear section in which the heart rate changes linearly with respect to acceleration is found from the collection of history data, and a regression line is found for the history data included in that linear section. The regression line found from this history data represents the characteristics (individuality) of the worker's heart rate response. 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, and are calculated (step S102).

[0086] Thereafter, the evaluation and determination unit 22 detects the heartbeat data of the worker 10 measured by the biosensor 11a (step S103).

[0087] On the other hand, if no historical data exists for the worker 10, or if data exists but a certain period of time (for example, one month) has passed since the previous data was recorded, the evaluation and judgment unit 22 determines that it is not possible to calculate a correct standardized heart rate for the worker 10, and calculates a work strain index for the worker 10 only from the acceleration data and not based on the heart rate data. The evaluation and judgment unit 22 calculates an acceleration deviation, which is a numerical value indicating the movement of the worker 10 (step S110), and calculates a work strain index for the worker 10 only based on the acceleration deviation (step S111). In this case, the acceleration deviation may be converted into a work strain index using a predetermined formula, such as by multiplying it by an appropriate coefficient.

[0088] When the data recording unit 24 records the history data of the worker 10 (step S101: "Yes"), the evaluation and judgment unit 22 ensures the reliability of the heartbeat data when detecting the heartbeat data. In the heat stroke risk management system of this embodiment, the electrode unit 11a2 of the biosensor 11a is arranged on the back surface 18b of the undershirt 18 worn by the worker 10 so as to better acquire the heartbeat data of the worker 10, who is the subject of evaluation, as described above. However, the heartbeat may not be detected correctly due to the influence of the worker's body movement and sweating on the body surface. For this reason, in the heat stroke risk management system of this embodiment, when the heartbeat data of the worker 10, who is the subject of evaluation, is not measured correctly, it is confirmed whether the heartbeat data is correctly acquired or not so as to prevent the work load index from being calculated based on incorrect data and the risk of developing heat stroke from being erroneously evaluated.

[0089] First, the evaluation / determination unit 22 calculates a heartbeat waveform detection rate in order to evaluate the reliability of the heartbeat data (step S104).

[0090] The raw data sampled from the biosensor may contain a certain percentage of noise (abnormal heart rate data) due to poor contact between the subject's skin and the electrodes, etc. Therefore, for each beat of data (heart rate interval), for example, data with a heart rate interval of 0.33 seconds or more and 1.33 seconds or less, and a difference from the previous data (heart rate interval difference) of 0.15 seconds or less, is determined to be normal and labeled.

[0091] The threshold for determining whether something is normal or abnormal can be set arbitrarily, but it is sufficient to set an appropriate value so that data with heartbeat intervals that are impossible from a physiological perspective can be removed.The measurement data is then divided into k partial intervals with a specified time width, and the percentage of data labeled as normal for each partial interval is calculated as the heartbeat waveform detection rate Q.

[0092] Next, the evaluation and determination unit 22 determines the heartbeat waveform detection rate for each partial interval (step S105).

[0093] If the heartbeat waveform detection rate is equal to or greater than a reference value (eg, 50%), the section is determined to be reliable (Yes in step S105), and a workload index is calculated using the heartbeat data (step S106).

[0094] On the other hand, if the heartbeat waveform detection rate is less than the reference value (50%), it is determined to be unreliable (No in step S105), and the process proceeds to step S110 in which a work strain index is calculated using the acceleration data for that section.

[0095] Note that the reference values ​​in the above description are merely examples, and may be appropriately adjusted depending on the performance of the biosensor, the subject's occupation, etc. For example, the threshold value may be set low for occupations that require intense movement, and high for occupations that require little movement.

[0096] If the heartbeat waveform detection rate is 50% or more (if "Yes" in step S105), the evaluation unit 22 calculates the median heart rate from the obtained heartbeat data (step S106). Here, the representative value (median value) for each partial interval set in step S104 is taken as the median heart rate data. The representative value may be the average value of the interval, but is preferably the median value of the interval. This is because even if the data obtained from the measurement device contains a small number of irregular values, the influence of these values ​​can be eliminated.

[0097] Furthermore, the evaluation and determination unit 22 simultaneously calculates an acceleration deviation, which is a numerical value indicating the motion status of the worker 10, from the acceleration data obtained from the biosensor 11a (step S107).

[0098] Next, the median heart rate is corrected based on the historical data and a standard heart rate response model created in advance to obtain a standardized heart rate (step S108).

[0099] Specifically, the central heart rate data is converted into a standardized heart rate using the heart rate response coefficient and intercept heart rate, as well as the standard heart rate response coefficient and standard intercept heart rate, which are parameters of the standard heart rate response model, according to the following formula (Formula 1).

[0100] HR S [k]=(αs / αr)(HR[k]-βr)+βs (Equation 1) Where: Central heart rate data: HR[k] Normalized Heart Rate: HR S [k] Heart rate response coefficient: αr Intercept heart rate: βr Standard heart rate response coefficient: αs Standard intercept heart rate: βs Here, k represents the number of the subinterval.

[0101] The standard heart rate response model is a heart rate response model created based on large-scale data obtained by measuring a large number of people. It is a model that represents the standard human heart rate response to acceleration (body movement) and can be expressed by various parameters and a specified formula.

[0102] An example of the measurement results for determining the standard heart rate response model is shown in FIG.

[0103] Figure 6(a) plots all of the large-scale data (approximately 3 million points), with the shading representing the density of the data. Line 51 in Figure 6(a) represents the 5% data line, line 52 represents the 25% data line, line 53 represents the 50% data line, line 54 represents the 75% data line, and line 55 represents the 95% data line.

[0104] The median of each section is shown by the cross in Fig. 6(b), and the approximate curve F HR (Reference number 56) represents the standard heart rate response model.

[0105] Approximate curve F HR can be obtained by various curve fitting techniques, and the acceleration deviation A RMS Given the estimated standardized heart rate F HR A function F that gives HR (A RMS ) can be expressed as FHR The slope of the linearly changing portion (the section where the acceleration is from about 0.05 to about 0.45) corresponds to the standard heart rate response coefficient, and the intercept of the approximation curve corresponds to the standard intercept heart rate.

[0106] The large-scale data may be data from a number of workers at the site over the past few days, or may be accumulated data sampled in advance at another site. Preferably, a standard heart rate response model is created based on large-scale data obtained by measuring a large number of workers who are engaged in the same work as the worker. This is a heart rate response model optimized for the work, and is considered to represent a typical heart rate response of workers engaged in that work. There is no particular rule on the number of people on which the large-scale data is based, but the greater the number of samples, the more accurately the heart rate response can be approximated. It is preferably 5 people or more, more preferably 50 people or more. There is no particular rule on the accumulation period, but it is preferable to obtain data for 2 days or more, more preferably 5 days or more at the same site.

[0107] Based on the standardized heart rate and acceleration deviation thus obtained, the evaluation and determination unit 22 calculates the work strain index of the worker 10 (step S109).

[0108] At this time, the evaluation and determination unit 22 determines whether to use the standardized heart rate or the estimated standardized heart rate by using a correction map created based on the standardized heart rate response model. A specific method for determining the median heart rate and the acceleration deviation from the heart rate data obtained from the biosensor 11, which is a measuring device, and the values ​​of the three-dimensional acceleration sensor, as well as the criteria for selecting heart rate data using the correction map, will be described in detail later.

[0109] On the other hand, if the heartbeat waveform detection rate Q is not equal to or greater than the reference value (50% in the above example) ("No" in step S105), the evaluation and judgment unit 22 determines that the reliability of the obtained heartbeat data is low, does not calculate the median heart rate, and calculates an acceleration deviation, which is a numerical value indicating the motion status of the worker 10, from the acceleration data obtained from the biosensor 11a (step S110). In this case, the evaluation and judgment unit 22 calculates a work strain index of the worker 10 based only on the acceleration deviation (step S111).

[0110] The workload index obtained in step S111 is considered to be less accurate than the workload value obtained in step S109 because it does not reflect the heart rate data. However, since the movements of the worker 10 are continuous, it is preferable to obtain the workload index continuously rather than not calculating the workload index during that time because the heart rate data is not available.

[0111] Furthermore, the evaluation and 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 11a and the environmental temperature data at the work site where the worker 10 works (step S112). The environmental temperature data at the work site can be obtained based on the temperature data around the work site obtained by the weather information acquisition unit 25 of the cloud server 21, or, when the worker is working indoors, based on temperature information obtained from a temperature sensor placed at the work site.

[0112] The specific procedure for calculating the heat load index based on the clothing temperature data of the worker 10 and the environmental temperature data will be described in detail later.

[0113] Then, the evaluation and determination unit 22 calculates the risk of developing heat stroke of the worker 10 as a heat stroke development risk index based on the obtained work burden index and heat load index (step S113).

[0114] In the heat stroke onset risk assessment system according to this embodiment, the heat stroke onset risk index can be determined as the linear sum of the work load index and the heat stress load index. Therefore, the larger the value of the heat stroke onset risk index, the higher the risk of the worker developing heat stroke. By defining the magnitude of the heat stroke onset risk index as a region, it is possible to rank whether the heat stroke onset risk is in a high (= dangerous) state, a moderately high (= caution) state, or a low (= safe) state. Therefore, according to the rank of the heat stroke onset risk index calculated by the evaluation determination unit 22, the worker 10 himself or the on-site supervisor 30 who is the supervisor can take measures such as stopping work and taking a break, reducing the work load, or lowering the in-clothing temperature to reduce the heat stress load, and it becomes possible to effectively avoid the onset of heat stroke.

[0115] [Method for Evaluating Heat Stroke Onset Risk] Here, an algorithm for calculating the heat stroke onset risk index, which is an index for evaluating the heat stroke onset risk for each individual worker, performed in the heat stroke onset risk assessment system according to this embodiment will be described.

[0116] [Estimation of Workload] <a. Pretreatment> First, pretreatment for calculating the work load index is performed on the heart rate data and the acceleration data.

[0117] The pretreatment of the heart rate data is performed by calculating the central heart rate from the heart rate data detected by the biosensor 11a worn by the worker 10, as described with reference to the flowchart of FIG. 5 (step S106 in FIG. 5).

[0118] More specifically, when the heart rate waveform detection rate of the partial section is 50% or more, the central heart rate HR is obtained by converting the acquisition interval of the heart rate data included in the partial section into the heart rate per partial section (for example, per past one minute).

[0119] On the other hand, for the acceleration data obtained by the acceleration sensor, the average value ΔA for the past minute is calculated by the following procedure.

[0120] 1) Exponential moving average of unequal time interval data For the acceleration data {Ax(t)}, {Ay(t)}, and {Az(t)} in the x-axis, y-axis, and z-axis directions, the exponential moving average of the acceleration data in each axial direction is calculated using the exponential moving average method, which is a statistical method, with a time constant of 10 seconds. The time constant is not particularly limited, but may be appropriately determined in the range of, for example, 5 to 10 seconds depending on the performance of the acceleration sensor.

[0121] Here, the exponential moving averages in the x-axis, y-axis, and z-axis directions are defined as {Sx(t)}, {Sy(t)}, and {Sz(t)}, respectively.

[0122] 2) Removal of exponential moving averages From the acceleration data for each axis, remove the exponential moving average described above to obtain the detrended time series acceleration. For example, for the x-axis, it would be "Ax(t)-Sx(t)".

[0123] 3) Calculating the sum of squares For the detrended time series acceleration, calculate the square of each time using the following formula (Formula 2) and calculate the sum.

[0124]

number

[0125] 4) Average acceleration per minute The sum of squares calculated above, "ΔA 2 (t)" average value for each minute "ΔA 2 ave " is calculated. Here, the average value is calculated by dividing by the number of data points. Also, the square mean of the acceleration "ΔA 2 ave "The square root of "ΔA ave " is calculated. Here, ΔA ave is the acceleration deviation ARMS It is as follows.

[0126] <b. Removal of outliers> Regarding the heart rate data obtained from the heartbeat data, non-numerical data, data with a heart rate of 40 or less, and data with a heart rate of 180 or more are removed as outliers.

[0127] Also, regarding the acceleration data, non-numerical data, and data where "ΔA is 0.05 or less, or 0.55 or more are excluded as outliers.

[0128] <c. Calculation of resting heart rate and heart rate response coefficient> Regarding the set of history data, a linear response interval in which the central heart rate changes linearly with respect to the acceleration deviation is set, 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 resting heart rate βr.

[0129] The method of fitting the regression line is not particularly limited. For example, an interval where the acceleration deviation is 0.05 to 0.4 is set as the linear response interval, and this interval is divided into m sub-intervals (m is, for example, 3 to 7). Next, the central value of the central heart rate and the acceleration deviation is obtained for each sub-interval. Then, a regression line is fitted to the obtained central value coordinates of the m points (see Fig. 7).

[0130] <d. Calculation of workload index> In Fig. 5, as shown in step S108, when there is history data of the operator 10 who is the subject to be measured, based on this history data and the standard heart rate response model, the standardized heart rate HR S is calculated. By calculating the standardized heart rate HR S it is possible to correct individual differences in calculating the workload index from the heartbeat data due to the characteristics of each subject to be measured.

[0131] On the other hand, the estimated standardized heart rate is the approximate formula F of the standard heart rate response model HR (A RMS ) is used, and the acceleration deviation A of the subject to be measured RMSIn the actual calculation of the workload index, the key point is whether to trust the standardized heart rate or the estimated standardized heart rate. In this embodiment, a correction map created based on the standard heart rate response model is used to determine which heart rate to select, and the corrected heart rate HR s get.

[0132] FIG. 8 is a first example of a correction map.

[0133] As shown in FIG. 8, the correction map has an approximate curve F with the standard intercept heart rate βs as the y-axis intercept and the slope of the straight line portion being the standard heart rate response coefficient αs. HR 71 is described. Here, the approximation curve F HR The judgment line is indicated by 71. It is noted that once the acceleration deviation exceeds 0.45, the judgment line 71 is no longer a straight line, as shown in FIG. 7, and it has been found that the degree of increase in the median heart rate relative to the acceleration deviation decreases.

[0134] 8, a boundary line 72 is provided in the portion where the acceleration deviation, which indicates the movement of the worker being measured, is 0.2. This is because it is considered that in the region where the acceleration deviation is smaller than 0.2, the influence of emotions is greater than the change in heart rate due to movement, and in the region where the acceleration deviation is larger than 0.2, the fluctuation in heart rate due to body movement is greater.

[0135] In the correction map shown in Fig. 8, the relationship between the heart rate and the acceleration deviation is corrected to fall within the hatched ranges 73 and 77 in the map. For example, in the range of the acceleration deviation up to 0.2, when the standardized heart rate is large and located above the judgment line 71, the value of the judgment line 71, i.e., the estimated standardized heart rate, becomes the corrected heart rate HR s If the standardized heart rate is smaller than the standard intercept heart rate βs, the value of the standard intercept heart rate βs is used as the corrected heart rate HR s In addition, if the standardized heart rate is below the judgment line 71 and above the standard intercept heart rate βs, the standardized heart rate is used as the corrected heart rate HR sIn this way, when a standardized heart rate greater than the estimated standardized heart rate is detected in an area where the acceleration deviation is smaller than 0.2, this can be eliminated as an effect of emotion.

[0136] On the other hand, in the region where the acceleration deviation is greater than 0.2, the slope is the same as the standard heart rate response coefficient αs mentioned above, that is, the approximation curve F HR A parallel line 76 is drawn parallel to the straight line portion of the line 71 to define the area where the heart rate value is judged to be too large, and it is judged that the correct heart rate has been detected within the area 77 between the parallel line 76 and the judgment line 71. If the heart rate falls within this area 77, the standardized heart rate is used as it is as the corrected heart rate HR s When the standardized heart rate is greater than the upper limit of the parallel line 76, the value on the parallel line 76 is used as the corrected heart rate HR s In addition, the influence of errors is eliminated by adopting the estimated standardized heart rate as the corrected heart rate HR as the numerical value that appears in the area below the judgment line 71 as shown by the arrow 79 in the figure. s By adopting this as the heart rate measurement, it is possible to prevent a heart rate value that is too low from being used to calculate the workload index even if the person being measured is moving at a certain level or more.

[0137] The correction map shown in FIG. 9 is a correction map that is used when the detected heart rate data is determined to be more reliable.

[0138] A case where the reliability of the heartbeat data is high can be assumed to be a case where the detection rate of the heartbeat data acquired by the biosensor 11 is higher than a judgment criterion (for example, 50%) and continues to exceed, for example, 80%.

[0139] The correction map shown in Fig. 9 is basically the same as the correction map shown in Fig. 7, except that it is different when the acceleration deviation is 0.2 or more and the normalized heart rate is in the region lower than the determination line 81. When the reliability of the heart rate data shown in Fig. 9 is high, a boundary line 88 is drawn below the determination line 81, parallel to the determination line 81, from the position of the standard intercept heart rate βs at the acceleration deviation of 0.2. The normalized heart rate in the region 89 between the boundary line 88 and the determination line 81 is used as the corrected heart rate HR s as it is, and when the normalized heart rate is smaller than the boundary line 88, the value on the boundary line 88 is adopted as the corrected heart rate HR s as shown by the arrow 91 in the figure.

[0140] By doing so, the normalized heart rate can be adopted in a wide range, and a more accurate workload index can be calculated.

[0141] <e. Evaluation of workload> Based on the corrected heart rate HR obtained using the correction map c the workload index W is calculated as follows.

[0142] First, the corrected heart rate HR c is converted into metabolic equivalents METs (Metabolic equivalents) using the following formula (Formula 3).

[0143] METs = a METs ×HR c + b METs (Formula 3) Here, a METs and b METs are predetermined parameters and can be determined based on a respiratory measurement experiment.

[0144] Next, the metabolic equivalent METs is converted into the workload index W using the following formula (Formula 4).

[0145] W = a W ×METs + b W (Formula 4) Here, a W and bW is a predetermined parameter.

[0146] For example, W =0,2,b W =-0.2, the workload can be evaluated as follows: if the workload index W is 0.6 or higher, it is high metabolic rate work, i.e., work that places a heavy burden on the worker; if the value of W is 1 or higher, it is work that places an extremely high metabolic rate, i.e., work that places a very heavy burden on the worker.

[0147] (Evaluation of heat load) Using the clothing temperature Ti obtained by the measuring device 11 and the outside air temperature To obtained as the environmental temperature, the heat load index H is calculated by the following formula (Formula 5).

[0148]

number

[0149] If the heat load index H is less than 0, H=0.

[0150] When the heat load index H is 0.6 or more, the heat load is evaluated as being relatively high, and when the heat load index H is 1 or more, the heat load is evaluated as being extremely high.

[0151] (Assessment of the risk of heat stroke) Using the work strain index W and the heat load index H obtained by the above calculation, a heat stroke development risk assessment index R for the worker 10 to be evaluated is calculated as shown in the following formula (Formula 6).

[0152]

number

[0153] Here, a is a numerical value defined according to the heat acclimatization of the worker being evaluated, where a = -1.8 in the case of heat acclimatization and a = -1.3 in the case of no heat acclimatization.

[0154] Regarding the heatstroke risk assessment value R calculated in the above manner, if R is less than 0.6, the risk of developing heatstroke is judged to be low; if R is 0.6 or more but less than 1.0, it is judged to be at a warning level requiring caution; and if R is 1.0 or more, it is judged to be at a high risk and at a dangerous level of developing heatstroke.

[0155] Since it is not possible to actually verify the occurrence of heatstroke, when determining the criteria for judging the risk of developing heatstroke, they should be determined so as to enable a stricter assessment of the risk of developing heatstroke, that is, so as to be on the safe side.

[0156] (Continuous assessment of heat stroke risk) When continuously evaluating the risk of developing heat stroke of a worker 10 based on the measurement results obtained from a biosensor 11, which is a measuring device worn by the worker, the exponential moving average values ​​of the heat load index H and the work burden index W are calculated using the following equations (Equation 7) and (Equation 8) with a sampling interval of 1 minute.

[0157]

number

[0158] Here, w1 = 2 / 31 and w2 = 2 / 11.

[0159] Furthermore, the exponential moving average value of the heat stroke risk index R is calculated from the following equation (Equation 9).

[0160]

number

[0161] For example, if the exponential moving average of the heatstroke risk index R remains at or above 1 for 30 minutes or more, it is determined that the risk of developing heatstroke is extremely high, and measures to prevent the onset of heatstroke, such as encouraging workers to take breaks, are taken.

[0162] (Displayed on a 2D map) As can be seen from the above formula (Formula 6), in the heatstroke risk management system according to this embodiment, the index R indicating the risk of heatstroke is expressed as a linear sum of the heat load index H and the work burden index W for the worker 10.

[0163] Using this, 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 a symbol corresponding to the current heat stroke risk index for each of the workers 10 managed by the site supervisor 30, who is a manager, on a two-dimensional map, the site supervisor 30 can grasp the overall risk index of the workers under his management at a glance. Note that a detailed explanation of the display image that displays the degree of heat stroke risk for the workers 10 will be omitted.

[0164] [Handling of biological information obtained by devices other than the first measuring device] As described above, in the heat stroke risk management system according to this embodiment, the physical condition of each worker is evaluated and the risk of developing heat stroke is evaluated based on the biological information acquired by the biological sensor 11a, which is the first measuring device, attached to the chest part of the undershirt shown in Fig. 2. Therefore, by converting the biological information acquired by the wireless type pulse meter 11b as the second measuring device and the sensing unit 11c as the third measuring device described above into biological information acquired by the first measuring device, workers wearing different types of biological information acquisition units can be commonly managed by the heat stroke risk management system.

[0165] In the heat stroke risk management system of this embodiment, the median heart rate is used as a common parameter for the heart rate data, and the acceleration deviation is used as a common parameter for the acceleration data, thereby integrating biological information obtained by different measuring devices.

[0166] (Method for processing biological information acquired by second measurement device) The wireless type pulse meter 11b outputs the pulse wave interval measured as heartbeat data every second, which is different in timing of data output from the biosensor 11a, which outputs the heartbeat interval for each heartbeat.

[0167] In the biosensor 11a, the reliability of the heart rate data was judged by estimating the heart rate waveform detection rate as described above, and only the heart rate data judged to be normal was used to calculate the central heart rate HR. Correspondingly, in the pulse data outputted every second from the wireless type pulse meter 11b, the less reliable data was discarded and only the values ​​considered to be more reliable were used.

[0168] More specifically, data with a value of 0, or data less than 45 bpm or greater than 180 bpm were discarded as being unreliable. In addition, because it is physiologically unnatural for the same pulse to continue, if the same pulse data occurred five or more times in a row, the data from the fifth beat onwards was discarded as defective data.

[0169] In this way, using only reliable data, the median of the central heart rates for a 60-second window every 30 seconds was taken as the central heart rate (HR).

[0170] Regarding the acceleration data, only high-frequency components were extracted from the acceleration data because the wireless type pulse meter 11b measures the area around the head or neck, such as the back of the collar, or the upper body, as shown in Figure 3. As an example, it is believed that noise components that are not the movements of the worker 10 being evaluated can be removed by passing the data through a high-pass filter with a cutoff frequency of 0.1 Hz or less.

[0171] Then, for the acceleration data that has passed through the high-pass filter, the square deviation of the data in the x-direction, y-direction, and z-direction over one minute is calculated.

[0172] The values ​​of the median heart rate and acceleration deviation obtained in this manner are considered to be the same as the median heart rate and acceleration deviation obtained by the first measuring device, the biosensor 11a, so the workload index can be calculated as described above.

[0173] As mentioned above, some commercially available wireless pulse rate monitors 11b do not have sensors for measuring environmental conditions such as temperature and humidity. For this reason, when calculating the heat load index, it is preferable to estimate the environmental conditions in which the worker 10 is placed using data acquired by the weather information acquisition unit 25 of the cloud server 21. In this case, since research materials (e.g., experimental data from Shinshu University, for example) comparing data from clothing temperature sensors with WBGT are publicly available, it is expected that a more accurate calculation of the heat load index can be achieved by appropriately taking into account the contents of such materials.

[0174] (Method for processing biological information acquired by a third measuring device) The sensing unit 11c outputs pulse wave intervals measured as heart rate data at regular intervals (e.g., every minute). In addition, a METs estimate is output for the motion status of the subject at regular intervals (e.g., every minute).

[0175] In this case, the one-minute pulse interval data is multiplied by a predetermined coefficient, for example 0.984, to obtain a correspondence with the heartbeat interval output by the biosensor 11a and obtain the median heartbeat value. This coefficient is used to correct the difference between the median and average heartbeat intervals for one minute that occurs due to the asymmetry of the statistical distribution of the heartbeat intervals and the characteristics of the pulse wave measuring device.

[0176] In addition, by utilizing the fact that the METs data and the acceleration data correspond linearly during walking, light jogging, etc., it is possible to obtain a correlation coefficient by actually obtaining data from two measuring devices at the same time, and convert the output METs data into acceleration data. From the converted acceleration data obtained as a result, the acceleration displacement is calculated in the same manner as that calculated by the biosensor 11a.

[0177] In addition, since the sensing unit 11c is attached to the arm of the worker 10 to be evaluated, in addition to the movement of the entire body of the worker 10, the sensing unit 11c detects the fine movements of the attached arm as acceleration data. For this reason, for example, even at a work site, the acceleration data as a measurement result differs between work such as carrying and assembling large parts and fine work using the hands to handle small parts. In such a case, the reliability of the measurement data can be improved by taking measures such as appropriately grouping the workers 10 performing similar work and setting different judgment criteria for each group.

[0178] In this way, by using the heart rate data and METs data acquired by the sensing unit 11c to determine the median heart rate and acceleration deviation, a workload index can be calculated in the same manner as with the data obtained by the biosensor 11a.

[0179] The sensing unit 11c has a sensor for measuring environmental conditions such as temperature and humidity. However, the measurement location is the wrist, which is naturally different from the temperature inside the clothes measured by the biosensor 11a. For this reason, in calculating the heat load index, it is preferable to estimate the environmental conditions in which the worker 10 is placed using data acquired by the meteorological information acquisition unit 25 of the cloud server 21, as in the case of the wireless pulse meter 11b described above, or to calculate the heat load index by appropriately correcting the temperature data, for example, by separately taking a correlation between the temperature inside the clothes and the temperature of the exposed body surface.

[0180] As described above, in the biometric information processing method and biometric information processing system disclosed in the present application, the biometric information of the person being evaluated acquired by different biometric information acquisition units is converted into each other using a common indicator, making it possible to evaluate and manage the person being evaluated within a common system without being restricted by the form of the biometric information acquisition unit.

[0181] As a result, it is possible to construct a biometric information processing system that can evaluate more subjects at a lower cost in accordance with the needs of the customer, for example, by allowing the subject to select and use the type of biometric information that the subject finds more preferable, or by using a biometric information acquisition unit that the subject already owns. By increasing the number of subjects to be evaluated, the number of data processing examples in the biometric information processing system increases, making it possible to make corrections using past data, and thus improving the accuracy of the evaluation results obtained from the biometric information.

[0182] In addition, by referring to a large amount of data, it is possible to grasp the trend of differences in accuracy of acquired data due to differences in measuring devices, thereby improving the accuracy of conversion of data acquired by different biometric information acquisition units.

[0183] In the above embodiment, the median heart rate is used as a common parameter of the heart rate data. However, other parameters such as the average heart rate can be used as well as the median heart rate.

[0184] In the above embodiment, the acceleration deviation is used as a common parameter of the acceleration data. However, other than the acceleration deviation, the square root of the resultant acceleration deviation or the like can be used as a parameter.

[0185] In addition, in the above embodiment, the biometric information processing system disclosed in the present application is exemplified as a heatstroke risk management system in which the subjects to be evaluated are workers working on construction sites, etc. However, the system is not limited to the above example and can acquire biometric information of multiple subjects, and can be used as a biometric information processing system that evaluates the work burden index, physical condition assessment index, heat stress index, exercise stress index, and other indices of each subject based on the acquired biometric information.

[0186] For example, it can be used in systems that process a wide range of biometric information with different subjects, biometric information measured, and evaluation purposes, such as health management for athletes during training or health management systems for residents of elderly care facilities. [Industrial Applicability]

[0187] The biometric information processing method and biometric information processing system disclosed in this application are not limited to the biometric information acquisition unit worn by the subject, and therefore can realize a method of processing biometric information for a larger number of subjects, and can construct a highly versatile biometric information processing system at low cost, making them extremely useful. [Explanation of symbols]

[0188] 10. Worker (evaluated person) 11a biosensor (first measuring device, bioinformation acquisition unit) 11b Wireless type pulse meter (second measuring device, biological information acquisition unit) 11c sensing unit (third measuring device, biological information acquisition unit) 22 Evaluation and judgment unit (data processing unit)

Claims

1. a step of acquiring biometric information of the person to be evaluated by a biometric information acquisition unit; and calculating an index representing the state of the subject based on the acquired biometric information, The assessees are multiple, The biometric information of each of the plurality of subjects is acquired by one of a plurality of types of biometric information acquisition units, each of which has a different frequency of detecting biometric information data or a different frequency of data transmission; In a data processing unit located away from the subject, if the biological information is heart rate data, it is converted into a central heart rate as a common parameter, and if the biological information is acceleration data or METs, it is converted into an acceleration deviation or a square root of the squared deviation of a composite acceleration as a common parameter, A biometric information processing method, characterized in that the biometric information acquired by the biometric information acquisition units in different forms is integrated into a common parameter to calculate the index.

2. The biometric information processing method described in claim 1, wherein the multiple forms include at least three types of forms: a clothing type, a wristwatch type, and a type worn on the earlobe or fingertip.

3. A biometric information processing method as described in claim 1 or 2, wherein the index is at least one of a workload index indicating the degree of impact of the workload on the person being evaluated, a physical condition assessment index indicating the degree of change in the person's physical condition from its normal state, and a heatstroke risk index indicating the degree of risk of the person being evaluated developing heatstroke.

4. 4. The biological information processing method according to claim 1, wherein the biological information is at least one of heart rate data, acceleration data, and METs.

5. The biological information processing method according to claim 4 , wherein the common parameter used for the heart rate data is a median heart rate.

6. Two or more different types of biometric information acquisition units, each with a different frequency of detecting biometric information data and a different frequency of data transmission, for acquiring biometric information for each of a plurality of subjects; a data processing unit located away from the subject and calculating an index representing the subject's condition based on the acquired biometric information; The data processing unit converts the biometric information acquired by the biometric information acquisition unit into a central heart rate as a common parameter if the biometric information is heart rate data, or into acceleration data or METs if the biometric information is acceleration deviation or the square root of the squared deviation of resultant acceleration as a common parameter, and integrates the biometric information acquired by the biometric information acquisition unit in different forms into these common parameters to calculate the index.

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