Biometric 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 that enable 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 the 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 biometric information processing system for evaluating and managing such physical condition, a heatstroke risk management system has been proposed that uses a wearable biometric signal detection device equipped with a three-dimensional acceleration sensor that captures the body movements of the person being evaluated and a biometric information acquisition unit that detects the 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] Japanese Patent Application Publication No. 2018-130531 Summary of the Invention [Problem to be solved by the invention]

[0005] In the conventional heatstroke risk management system, a biometric information acquisition unit is placed on the chest of the undershirt. The biometric information acquired by the biometric information acquisition unit is transmitted to an information processing unit on 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 heatstroke for each individual, or for a group of workers working in the same environment, and instructs workers who are at high risk of heatstroke to take a break, thereby reducing the risk of heatstroke.

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

[0007] These various biometric information acquisition units differ in the type and accuracy of biometric information acquired due to their configuration constraints. Furthermore, each of these biometric information acquisition units is incorporated into a unique biometric information processing system, with different settings for the frequency of biometric data detection and data transmission. Furthermore, differences in the shape and location of each biometric information acquisition unit, the data transfer capabilities within the system, and the data processing functions incorporated within the biometric information acquisition unit result in different data processing specifications, such as the extent to which data is processed before it is sent from the biometric information acquisition unit. For this reason, in conventional biometric information processing systems using biometric information acquisition units, the biometric information acquisition unit worn by the subject was limited to one specific to that system.

[0008] However, in a biological information processing system, the accuracy of evaluation results such as physical condition evaluations based on the biological information improves as the biological information of more subjects is collected. In order to increase the amount of acquired biological information to improve the accuracy of evaluation results, to build a biological information processing system at lower cost, to acquire biological information in a more appropriate form depending on 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 biological information under less stress depending on the preferences of each subject, it is preferable to have a biological information processing system that can use biological information from various different types of biological 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 person being evaluated, as well as 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 problem, 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 state 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 this application comprises two or more different types of biometric information acquisition units that acquire biometric information of the person being evaluated, and a data processing unit that calculates an index representing the state 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 common parameters and calculates the index. [Effects of the Invention]

[0012] With the above configuration, the biometric information processing method disclosed in the present application calculates various indices representing the state of the subject using the biometric information of the subject acquired by two or more types of biometric information acquisition units, i.e., by converting the biometric information acquired from different types of biometric information acquisition units into common parameters, it is possible to absorb differences in data accuracy and data specifications. 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 biometric information acquisition unit of a type that is more preferred.

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

[0014] [Figure 1] FIG. 1 is a block diagram showing the configuration of each part of a heatstroke risk management system described as an embodiment. [Figure 2] FIG. 2 is a diagram illustrating the configuration of a first biological information acquisition unit used in the heatstroke development risk management system described in this embodiment. [Figure 3] FIG. 3 is a diagram illustrating a second biological information acquisition unit used in the heatstroke 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 heatstroke development risk management system described in this embodiment. [Figure 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]Figure 6 shows the measurement results of the standard heart rate response, which indicates the heart rate response to acceleration deviation. Figure 6(a) shows a plot of all approximately 3 million data points, and Figure 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 for estimating a heart rate index and a physical fitness index in the biological information processing method according to this embodiment. [Figure 8] FIG. 8 is a diagram illustrating a first correction map for determining a work strain index in the work strain estimation method described in this embodiment. [Figure 9] FIG. 9 is a diagram illustrating a second correction map for determining a work strain index in the work strain estimation method described in this embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0015] 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 state 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 configuration, the biometric information processing method disclosed in the present application allows for a wider range of choices for the biometric information acquisition unit worn by the person being evaluated, making it possible to acquire more biometric information from the person being evaluated in a manner that places less burden on the person being evaluated, and to calculate accurate indicators using the acquired biometric information.

[0017] In the above-mentioned biometric information processing method, it is preferable that the index is at least one of a work burden index indicating the degree of impact of the workload on the person being evaluated, a physical condition assessment index indicating the degree of change from the normal state of the physical condition of the person being evaluated, and a heatstroke risk index indicating the degree of risk of the person being evaluated developing heatstroke. By doing so, it is possible to obtain practical indexes that represent the condition of many people being evaluated.

[0018] Furthermore, it is preferable that the biometric information is at least one of heart rate data, acceleration data, and METs (estimated metabolic equivalents). This allows for correction of differences in individual characteristics and differences in the specifications of the biometric information acquisition unit, and obtains the biometric information necessary to accurately assess the subject's condition, allowing for accurate calculation of indicators representing the subject's condition. Heart rate data includes heart rate and heartbeat interval. The biometric information may also include an estimated energy expenditure.

[0019] Furthermore, it is preferable that the common parameter used for the heart rate data is the median heart rate, thereby making it possible to obtain accurate heart rate data of the subject regardless of the method of acquiring heart rate data or data processing specifications in the biometric information acquisition unit.

[0020] Furthermore, it is preferable that the common parameter used for the acceleration data or the METs (estimated metabolic equivalent) is acceleration deviation. By doing so, the behavior of the subject can be accurately grasped regardless of the method of grasping the subject's body movement or the data processing method in the bioinformation acquisition unit.

[0021] The biometric information processing system disclosed in this application comprises two or more different types of biometric information acquisition units that acquire biometric information of the person being evaluated, and a data processing unit that calculates an index representing the state 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 common parameters and calculates the index.

[0022] By having the above configuration, the biometric information processing system disclosed in this application has 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 their own preferences and the physical condition of their body during measurement, and making it possible to easily calculate an index representing their 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, the overall configuration of an example of a biological information processing system disclosed in the present application will be described.

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

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

[0027] As shown in Figure 1, the heatstroke risk management system of this embodiment is composed of a worker 10 (the person being assessed), a cloud server 21 on the Internet 20 that evaluates the worker's physical condition based on the worker's biometric information and assesses the risk of heatstroke, a site supervisor 30 who oversees the worker 10 and a work group that includes a certain number of other workers 10, and an office 40 that manages multiple site supervisors 30 to provide overall visibility and operate and maintain the heatstroke risk assessment system. The above is a generalized example assuming a typical construction site. Needless to say, the heatstroke risk management system of this embodiment can take various forms depending on the actual configuration of the site where it is implemented, such as when one site supervisor 30 manages one worker 10, when the site supervisor and the office are inseparable, or when multiple offices are involved in managing the entire construction site.

[0028] In the heatstroke risk management system described in this embodiment, workers 10 wear measuring devices 11, which are biological information acquisition units capable of detecting at least one of biological information such as their own heart rate data, acceleration data indicating body movement, and temperature data inside clothing as environmental temperature. At least two types of measuring devices 11 are used.

[0029] For example, some workers wear undershirts with biosensor 11a attached to the chest as a first measuring device, which includes a temperature sensor for detecting the temperature inside the clothing, an electrometer for detecting heart rate data, and a three-dimensional acceleration sensor for detecting body movement. Other workers wear wireless pulse rate monitors 11b as a second measuring device, which include a measuring unit attached to the earlobe for optically measuring pulse and a main unit with a built-in three-dimensional acceleration sensor attached to a position near the center of the body. Still other workers wear wristwatch-type sensing unit 11c as a third measuring device, which includes a pulse sensor capable of detecting pulse as minute vibrations, a three-dimensional acceleration sensor, and sensors for detecting temperature and humidity.

[0030] In addition, the biometric information acquisition units that can be used in the heatstroke risk management system of this embodiment are not limited to those exemplified above, and various types of biometric information acquisition units can be used that include heart rate data of the person being evaluated, an acceleration sensor that indicates body movement, a temperature sensor that measures environmental temperature, etc.

[0031] Note that various means can be used to attach the biosensor 11a, which is the first bioinformation acquisition unit employed in the heatstroke risk management system according to this embodiment, to the body surface of the worker 10 at the chest or center of the body, 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. Note that 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 heatstroke risk management system according to this embodiment, each worker 10 carries a smartphone 12 as a mobile terminal. The measuring device 11 and the smartphone 12 carried by the worker 10 are constantly connected via short-range communication such as Bluetooth (registered trademark), and biological information acquired by the measuring device 11 is sent to the smartphone 12 as needed.

[0033] The smartphone 12 is equipped with 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 heatstroke risk management system of this embodiment, the smartphone 12 is linked to the identification data of each worker 10, and the smartphone 12 also has an assessee information transmitting unit 13, which 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] Note that various methods can be used to link the measuring device worn by the worker with an ID that identifies the work itself, such as the worker inputting the name or control number of the measuring device into their smartphone, using the smartphone's image recognition function to read a two-dimensional or three-dimensional identification code attached to the measuring device, 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 their smartphone.Furthermore, if the smartphone itself is not owned by the worker but is loaned as part of the system, worker recognition can be achieved by data entry using the smartphone, reading an identification code, using a face recognition system, or other methods.

[0035] Furthermore, since the smartphone 12 can receive data, emit sound, and display images, the heatstroke risk management system of this embodiment is equipped with a warning notification unit 14 that performs a warning notification function of informing the worker 10 of the risk of developing heatstroke and urging them to take a break, and an image display unit 17 that performs the function of clearly displaying the heatstroke risk assessment results for each worker 10 himself or herself and the entire group to which that worker 10 belongs.

[0036] Cloud server 21 includes a data receiving unit 23 and a data transmitting unit 26, and transmits and receives information via the Internet 20. Cloud server 21 also includes an evaluation and determination unit 22 as a data processing unit, which acquires biometric information data of all workers 10 who are targets of the heatstroke risk management system, calculates, for each worker 10, a work strain index indicating the degree of impact of the workload caused by the work, and a physical condition evaluation index indicating the degree to which the worker's physical condition has changed from their usual state, and calculates a heatstroke risk index indicating the degree of risk of each worker 10 developing heatstroke based on these indices. Furthermore, evaluation and determination unit 22 can manage the risk of heatstroke for groups of workers 10 formed based on commonalities in the work content and working environment.

[0037] The heatstroke risk management system according to this embodiment manages the heatstroke risk of each worker 10, and if it is determined that the risk of heatstroke is particularly high, communicates that information to encourage the worker to take measures to reduce the risk of heatstroke. To this end, the cloud server 21 evaluates and determines the risk of heatstroke, and if the risk of heatstroke is increasing, creates warning information to warn the worker of this fact.

[0038] The cloud server 21 also has a weather information acquisition unit 25, which can acquire weather information from information sites that provide weather information via the Internet 20, and can obtain current weather conditions such as temperature, humidity, and amount of sunlight in the area where the worker 10 is working, as well as a weather forecast that predicts changes over the next few hours, thereby taking weather conditions into account when assessing the risk of developing heatstroke.

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

[0040] Cloud server 21 is connected via Internet 20 to a personal computer 31 that serves as a manager's information terminal used by site supervisor 30, who is the manager who supervises the work of worker 10, who is the person being evaluated, at the construction site. Therefore, site supervisor 30, who is at the work site where worker 10 is working, can use a data receiving unit 33 of personal computer 31 to grasp the biometric information data of worker 10 that is sent from cloud server 21 at any time, and whether warning information has been generated by 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 data 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 risk index for the worker 10 by taking into account the temperature information inside the clothing and environmental temperature information at the work location obtained via the Internet.

[0042] The specific details of the estimation of the workload of the worker 10 and the assessment of the risk of developing heat stroke, which are carried out by the assessment unit 22, will be explained later.

[0043] The cloud server 21 corrects 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 enabling more realistic management of the risk of developing heatstroke.

[0044] In the heatstroke risk management system exemplified in this embodiment, the evaluation and determination unit 22 is not limited to being provided on 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 does not matter.

[0045] The site supervisor's personal computer 31 is equipped with an information management unit 32 that manages various types of information obtained by the measuring device 11 regarding 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 constantly keeps track of the latest information, which is the basis for evaluating the risk of heatstroke, such as the information obtained from each worker 10 and whether or not warning information has been generated, based on information transmitted from the cloud server 21. The information management unit 32 also outputs the acquired evaluation results of the risk of heatstroke for each worker 10 and other environmental information to a display image processing unit 35, which then adjusts the screen content displayed on a display device 36, such as an LCD monitor.

[0046] In this way, the site supervisor 30 can grasp information about 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 about each worker, on an easy-to-read screen. Note that the specific screen content displayed on the display device 36 after processing by the display image processing unit 35 is not described in detail in this specification, as it is sufficient to display the information required by the appropriately configured system in an easy-to-read manner.

[0047] Furthermore, the site supervisor's computer 31 can check whether the worker 10 has taken measures to prevent the onset of heatstroke by checking changes in the biometric information obtained from the worker 10 after notifying the warning information and by receiving confirmation of receipt of the warning information from the worker 10.If the worker 10 has not taken measures to prevent the onset of heatstroke, the site supervisor can further warn the worker 10 by 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 determination unit 22 of the cloud server 21 generates warning information that notifies 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 personal computer 31 of the site supervisor 30. It is also possible to set both the evaluation and determination unit 22 and the information management unit 32 to generate warning information. In this way, the personal computer 31 of the site supervisor 30 who actually supervises the work site generates warning information prior to the determination result by the evaluation and determination 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 / determination unit 22 of the cloud server 21 or the personal computer 31 of the site supervisor 30 is transmitted from the data transmission unit 34 of the personal computer 31 of the site supervisor 30 to the smartphone 12 carried by the worker 10 via a network including a local network such as a wireless LAN and a mobile phone information carrier. The warning notification unit 14 of the smartphone 12 that receives the warning information uses various information transmission means such as voice, screen display, illuminating or flashing lamp, and vibration to notify the worker 10 that he or she is at an increased risk of developing heatstroke. Upon confirming the warning information, the worker 10 reports receipt of the warning information via the touch panel or operation buttons of the smartphone 12 and takes measures to prevent heatstroke, such as stopping work and taking a rest.

[0050] The smartphone 12 of the worker 10 sends a message to the computer 31 of the supervisor 30 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 heatstroke risk management system described in this embodiment, the site supervisor 30 transmits the heatstroke risk data for the work site to the smartphone 12 of the worker 10, allowing the worker 10 to check the current heatstroke risk at the work site where he or she works. For example, if it is confirmed that the risk of heatstroke for other workers is increasing, each worker can take proactive measures to prevent heatstroke from occurring. Furthermore, if a worker learns that other workers have stopped working after receiving a heatstroke risk warning, he or she can be expected to respond more readily to the warning message from the site supervisor 30.

[0052] Furthermore, the smartphone 12 owned by the worker 10 can display on the screen related information such as changes in the worker's 10's risk of developing heatstroke up to the present, changes in the worker's own heart rate acquired by the biosensor 11, changes in the physical condition assessment index calculated from acceleration data, and calories burned, so that the worker 10 can refer to it himself / herself. As for the display screen of the smartphone owned by the worker 10, it is sufficient if it can display necessary information in an easy-to-see manner according to each purpose, and therefore a detailed description thereof will be omitted in this specification.

[0053] Cloud server 21 is also connected via the Internet 20 to a management computer 41 in the company or business 40 to which worker 10 belongs, and transmits, in real time, to management computer 41 of business 40, the measurement result information of worker 10 transmitted to site supervisor 30's personal computer 31 and various information used by cloud server 21 to determine the risk of heatstroke. Since management computer 41 of business 40 is equipped with its own data receiving unit 42 and data transmitting unit 43, it is also connected to site supervisor 30's personal computer 31 via the Internet, and can confirm information such as whether warning information from site supervisor 30 was correctly transmitted to worker 10 and whether worker 10 has taken preventive measures against heatstroke, and issue prescribed instructions as necessary. This effectively supports worker 10 in avoiding the risk of heatstroke.

[0054] Furthermore, since the cloud server 21, the site supervisor's 30's personal computer 31, and the business establishment's 40 management computer 40 are connected over the Internet 20 environment, the cloud server 21 can be accessed from the personal computer 31 or the management computer 40, allowing the data processing content in the cloud server 21 to be controlled, the judgment program in the evaluation and judgment unit 22 to be updated, and information necessary for heatstroke prevention and management to be appropriately retrieved from the cloud server 21.

[0055] In the above explanation, a smartphone was used as an example of the mobile terminal carried by the 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 heatstroke risk management system. Furthermore, the manager information terminal operated by the site supervisor can be any of various information devices that can send and receive information over a network, display data, record data, etc., such as a desktop computer, laptop computer, tablet computer, or small server device, in addition to the personal computer given as an example.

[0056] Furthermore, in the above explanation, we have described 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 information transmission means connecting the workers, site supervisors, and management departments within the business are not limited to the above examples, and it goes without saying that various information communication means for sending and receiving data can be used.

[0058] [Biometric information acquisition section] (First measuring device) First, a first embodiment of the measuring device 11 as a biometric information acquiring section will be described, in which the measuring device 11 is a biometric sensor that is placed in close contact with the chest of the worker to be evaluated and acquires biometric information.

[0059] 2A and 2B are diagrams showing an example of the configuration of an undershirt equipped with a biosensor, which is a first biometric information acquisition unit, worn by a worker in a heatstroke risk management system according to this embodiment. Fig. 2A shows the front side of the undershirt equipped with the biosensor, and Fig. 2B shows the back side of the undershirt, i.e., the side that faces and comes into contact with the worker's body surface.

[0060] 2, a biosensor 11a is placed 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 placed in the center of the chest on a front surface 18a of the undershirt 18, and an electrode part 11a2 connected to the data acquisition and transmission unit 11a1 and placed extending in the left-right direction on a back surface 18b of the undershirt 18, i.e., the side that comes into contact with the skin.

[0061] In the heatstroke risk management system according to this embodiment, a worker 10 wearing a first biometric information acquisition unit uses a biometric sensor 11a to detect the worker's 10 heart rate, temperature inside the clothing, and movement. An electrode, which serves as a heart rate detection means, located on the undershirt 18's back surface contacts the worker's chest, enabling the worker's 10 heart rate to be detected from changes in the surface potential. A temperature sensor (not shown) for detecting the temperature inside the clothing and an acceleration sensor chip (not shown) for detecting acceleration in three-dimensional directions are housed in the data acquisition and transmission unit 11a1. As described above, in the heatstroke risk management system according to this embodiment, the smartphone 12 carried by each worker is used as a repeater for the measured biometric information. Therefore, the data acquisition and transmission unit 11a1 only needs to include a minimum amount of data processing circuitry, a data transmission unit for short-range communication, and a power source for driving these electronic circuits, including the sensors. This allows the data acquisition and transmission unit 11a1 to be made smaller and lighter, thereby reducing the discomfort felt by the worker 10 wearing the undershirt 18 attached to his or her chest.

[0062] As described above, in the heatstroke risk management system described in this embodiment, various methods are known for attaching the biosensor 11a, which contacts the chest or other area of ​​the worker 10 to acquire heart rate data, temperature data inside the clothing, and acceleration data generated by movement. However, compared to methods such as attaching a measurement patch directly to the chest or 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 acquire necessary information without having to worry about wearing the biosensor 11a. Furthermore, even if the worker 10 sweats or twists his or her body while working, the biosensor 11a attached to the undershirt 18 will not eventually come off the worker 10's body surface, and its attachment position can be maintained substantially unchanged. Therefore, although some heart rate data may not be acquired at the moment the biosensor 11a leaves the worker 10's chest, a situation in which no heart rate data is acquired for a long period of time can be avoided.

[0063] In addition to the chest of the worker 10, the biosensor 11a for acquiring the heart rate data of the worker 10 can be placed on the worker's waist, back, upper arms, legs, etc. However, it goes without saying that it is preferable to limit the location to a range where the acceleration sensor and temperature sensor built into the biosensor 11a can acquire good measurement data. Furthermore, in cases where the risk of heat stroke is assessed as part of a physical condition assessment of an athlete undergoing training, rather than as a system for managing the risk of heat stroke in which workers working at a construction site are assessed as described in this embodiment, the subject may be wearing sportswear, and in this case too, it is most reasonable to place the biosensor on the chest of the sportswear worn on the upper body.

[0064] (Second measuring device) Next, as an example of a second form of the measuring device 11 as a biometric 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 pulse monitor, which is a second measuring device for acquiring biological information of a worker in a heatstroke risk management system according to this embodiment, is worn by the worker.

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

[0067] The pulse detection section has a pair of measuring pieces 11b1, 11b2, one of which has a light source such as an LED and the other a light-receiving element such as a CCD, arranged facing each other, and is secured to the earlobe by a spring or other biasing means. 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 houses a control circuit that outputs pulse rate data after appropriately filtering out noise components from the image of the contracted blood vessels detected by the pulse detection section, as well as a 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 Figure 3, in order to more accurately grasp the physical movements of the worker 11 being evaluated, it is preferable to attach the main body part 11b3 on which the three-dimensional acceleration sensor is located to the upper body close to the trunk of the worker 10, and it is considered preferable to attach it to the back part of the collar as shown in the figure, where movements different from the physical movements of the worker 10 are less likely to occur.

[0069] The commercially available wireless pulse meter 11b shown in Figure 3 does not have a function for measuring environmental temperature. However, if the main body 11b3 is attached to the back of the collar as shown, the discomfort felt by the worker 10 (the person being evaluated) when wearing the main body 11b3 is less than when the biosensor 11a shown in Figure 2 is attached to the chest. Therefore, various sensors for monitoring environmental conditions, such as a thermometer and a hygrometer, can be built into the main body 11b3. For the same reason, the main body 11b3 can be equipped with a transmitter capable of directly transmitting biometric information to an Internet environment. In this case, the worker 10 does not need to carry the smartphone 12 equipped with the subject information transmitter 13. If the worker 10 does not carry the smartphone 12, the main body 11b3 of the wireless pulse meter 11b can be equipped with an audio warning function to fulfill the function of the warning notification unit 14, which was previously performed by the smartphone 12.

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

[0071] (Third measuring device) Next, we will explain the third form of the 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 main body that internally has 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 measuring device, for acquiring biological information of a worker in the heatstroke risk management system according to this embodiment.

[0073] As shown in Fig. 4, sensing unit 11c is composed of device main body 11c1 worn in contact with the wrist and 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, device main body 11c1 is detachable from fixed belt 11c2 for purposes such as easy maintenance.

[0074] The wristwatch-type sensing unit 11c is worn with the device body 11c1 on the outside of the wrist (the back of the hand). The side that contacts the wrist (the inside of the fixed strap 11c2) is equipped with a pulse detector (not shown) that is pressed against the outside of the wrist of the worker 10 being evaluated to detect the pulse. The device body 11c1 also contains sensors for measuring environmental information such as temperature, humidity, and atmospheric pressure, as well as a three-dimensional acceleration sensor for detecting the movements of the worker 10. Small openings are formed on the outer surface of the device body 11c1 in areas corresponding to these sensors, as shown in the figure. The side of the device body also contains connection electrodes (not shown) that supply power to a secondary battery located inside the device body and enable data exchange with the internal memory element. The device can be placed on a dedicated cradle to charge the operating battery of the sensing unit 11c and exchange data with a computer or other device.

[0075] Wristwatch-type measuring devices also include so-called smartwatches, which have a clock function that displays the current time on an image display device located on the surface of the device body. Some smartwatches are equipped with an optical heart rate sensor that measures heart rate based on infrared absorption and have carrier communication capabilities. Using such smartwatches, biometric information such as heart rate information acquired by the measuring device can be directly transmitted to a cloud server via the Internet.

[0076] A wristwatch-type measuring device, such as the sensing unit 11c shown in FIG. 4, used in the heatstroke risk management system of this embodiment includes a transmitter within the device body 11c1 that transmits measured biometric information to the smartphone 12 carried by the worker 10. Because the third measuring device is also worn on the outside of the subject's wrist, similar to the wireless pulse monitor (the second measuring device), the discomfort felt by the worker 10 when wearing the device body 11c1 is less than when the biometric sensor 11a shown in FIG. 2 is worn on the chest. Therefore, the sensing unit 11c shown in FIG. 4 can also include a transmitter within the device body 11c1 that can transmit biometric information directly to an Internet environment. Furthermore, if the worker 10 does not have a smartphone 12 with the subject information transmission unit 14, the device body 11c1 will function as the warning notification unit 14. However, it is easy to adopt a configuration that provides a warning notification function to the worker 10 and displays heatstroke risk data in the form of an image, etc., in the case of a smartwatch or other device.

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

[0078] The heatstroke risk assessment method according to this embodiment calculates a work strain index indicating the intensity of the work performed by the person being assessed based on the person's heart rate data 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. The method also calculates a heat stress index for the person being assessed based on the temperature inside the person's clothes obtained from the measuring device and the environmental temperature of the work site where the person is being assessed. The method then calculates a heatstroke risk index indicating the risk of developing heatstroke based on the calculated work strain index and heat stress index.

[0079] In the following description of the heatstroke risk assessment method, the components of the heatstroke risk management system according to this embodiment described with reference to FIG. 1 will be exemplified as appropriate.

[0080] FIG. 5 is a flowchart showing the flow of heatstroke risk assessment in the assessment unit in the heatstroke risk management system described in this embodiment.

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

[0082] As shown in FIG. 5, when the evaluation by 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 by 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, by a timer being set so that the biosensor 11 automatically starts operating when the time to start work arrives, or by the biosensor 11a itself detecting that the worker has put on an undershirt 18 equipped with the biosensor 11a and starting its operation.

[0084] In order to calculate the work strain index, the evaluation determination unit 22 first checks whether historical data, which is heart rate data and acceleration data, of the worker 10 to be evaluated has been recorded in the data recording unit 24 (step S101).

[0085] If the worker 10 has previously been evaluated by the heatstroke risk management system described in this embodiment and historical data for the worker 10 is recorded in the data recording unit 24 (if "Yes" in step S101), a linear section in which the heart rate changes linearly with acceleration is determined from the collection of historical data, and a regression line is determined for the historical data included in that linear section. The regression line determined from this historical 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 these are calculated (step S102).

[0086] Thereafter, the evaluation / 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 determination 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, not based on the heart rate data. The evaluation and determination 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 can be converted into a work strain index using a predetermined formula, such as by multiplying it by an appropriate coefficient.

[0088] If the data recording unit 24 has recorded the worker 10's history data ("Yes" in step S101), the evaluation and determination unit 22 ensures the reliability of the heart rate data when detecting the heart rate data. In the heatstroke risk management system of this embodiment, as described above, the electrode unit 11a2 of the biosensor 11a is disposed on the back surface 18b of the undershirt 18 worn by the worker 10, so as to more accurately acquire the heart rate data of the worker 10 being evaluated. However, the heart rate may not be detected correctly due to the influence of the worker's body movements and sweating on the body surface. For this reason, in the heatstroke risk management system of this embodiment, if the heart rate data of the worker 10 being evaluated cannot be measured correctly, the system checks whether the heart rate data has been acquired correctly to prevent the calculation of the work strain index based on incorrect data and the resulting erroneous assessment of the risk of heatstroke.

[0089] First, the evaluation / determination unit 22 calculates the 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 (differential heart rate interval) of 0.15 seconds or less, is determined to be normal and labeled.

[0091] The threshold for determining normality / abnormality 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 subintervals with a specified time width, and the heartbeat waveform detection rate Q is calculated by determining the percentage of data labeled as normal for each subinterval out of the total data for that interval.

[0092] Next, the evaluation / 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 (for example, 50%), the section is determined to be reliable (if "Yes" in step S105), and a work strain 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 where a work strain index is calculated using acceleration data for that section.

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

[0096] If the heartbeat waveform detection rate is 50% or higher ("Yes" in step S105), the evaluation and determination unit 22 calculates the median heart rate from the obtained heartbeat data (step S106). Here, the representative value (median) for each partial interval set in step S104 is used 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 can be eliminated.

[0097] Furthermore, the evaluation and determination unit 22 simultaneously calculates an acceleration deviation, which is a numerical value indicating the movement 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, using 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 represents the standard human heart rate response to acceleration (body movement) and can be expressed using various parameters and predetermined formulas.

[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 data density. 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 values ​​of each section are shown by the cross marks in Figure 6(b), and the approximate curve F HR (Reference numeral 56) represents the standard heart rate response model.

[0105] Approximate curve F HR can be found 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 part (the section where acceleration is around 0.05 to 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 multiple 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 perform the same tasks as the worker in question. This heart rate response model is optimized for the task in question and is considered to represent the typical heart rate response of workers performing that task. There is no particular rule regarding the number of people on which the large-scale data is based, but the larger the number of samples, the more accurately the heart rate response can be approximated. Preferably, it is five or more people, more preferably fifty or more people. There is also no particular rule regarding the accumulation period, but it is preferable to obtain data for two or more days, more preferably five or more days, 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, evaluation / 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 acceleration deviation from the heart rate data obtained from biosensor 11, which is a measurement device, and the values ​​of the three-dimensional acceleration sensor, as well as 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 determination 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 operating condition of the worker 10, from the acceleration data obtained from the biosensor 11a (step S110). In this case, the evaluation and determination unit 22 calculates a work strain index of the worker 10 based only on the acceleration deviation (step S111).

[0110] The work strain index obtained in step S111 is considered to be less accurate than the work strain value obtained in step S109 because it does not reflect heart rate data. However, since the movements of worker 10 are continuous, it is preferable to obtain a work strain index continuously rather than not calculating the work strain index during that time because 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 is working (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 temperature data inside the clothes 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 heat stroke of the worker 10 as a heat stroke risk index based on the obtained work strain index and heat load index (step S113).

[0114] In the heatstroke onset risk assessment system shown in this embodiment, the heatstroke onset risk index can be determined as the linear sum of the work load index and the heat stress load index. Therefore, the higher the value of the heatstroke onset risk index, the higher the risk of the worker developing heatstroke. By defining the magnitude of the heatstroke onset risk index as a region, it is possible to rank whether the heatstroke 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 heatstroke 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 stop working and take a break, reduce the work load, or lower the in-clothing temperature to reduce the heat stress load, etc., and it is possible to effectively avoid the onset of heatstroke.

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

[0116] (Estimation of Work Load) <a. Pretreatment> First, pretreatment for calculating the work load index is performed on the heartbeat data and the acceleration data.

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

[0118] More specifically, when the detection rate of the heartbeat waveform in the partial section is 50% or more, the central heart rate HR is obtained by converting the acquisition interval of the heartbeat 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 using 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-, y-, and z-axis directions, the exponential moving average of the acceleration data in each axis direction is calculated using the exponential moving average method, a statistical technique, with a time constant of 10 seconds. The time constant is not particularly limited, but may be set appropriately in the range of 5 to 10 seconds, for example, 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 denoted as {Sx(t)}, {Sy(t)}, and {Sz(t)}, respectively.

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

[0123] 3) Calculating the sum of squares For the detrended time series acceleration, calculate the square at 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 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 Baseline 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 baseline heart rate βr. <x00005p1> The method of fitting the regression line is not particularly limited. For example, an interval where the acceleration deviation is 0.05 to RMS is set as the linear response interval, and this interval is divided into m subintervals (m is, for example, 3 to 7). Next, the central values of the central heart rate and the acceleration deviation are obtained for each subinterval. Then, a regression line is fitted to the obtained central value coordinates of the m points (see Figure 7).

[0130] <d. Calculation of Workload Index> In Figure 5, as shown in step S108, when there is historical data of the operator 10 who is the subject to be measured, based on this historical data and the standard heart rate response model, the standardized heart rate HR S is calculated. By calculating the standardized heart rate HR<00x000019>it is possible to correct individual differences in calculating the workload index from the heart rate 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 to calculate the acceleration deviation A of the subject to be measured RMSIn the actual calculation of the workload index, the key point is to decide 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 Figure 8, the correction map shows 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 71. It has been found that once the acceleration deviation exceeds 0.45, the judgment line 71 is no longer a straight line, as shown in Figure 7, and the degree of increase in the median heart rate relative to the acceleration deviation decreases.

[0134] In the correction map shown in Fig. 8, a boundary line 72 is provided in the area where the acceleration deviation, which indicates the movement of the worker being measured, is 0.2. This is because it is thought that in areas 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 that in areas 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 Figure 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 acceleration deviation up to 0.2, if 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, if a standardized heart rate greater than the estimated standardized heart rate is detected in a region 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 larger than 0.2, the gradient 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 graph 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 sandwiched between this parallel line 76 and the judgment line 71. If the heart rate falls within this area 77, the normalized heart rate is used as it is as the corrected heart rate HR s If 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 value that appears in the area below the decision line 71 as shown by the arrow 79 in the figure, that is, the value on the decision line 71, i.e., the estimated standardized heart rate, as the corrected heart rate HR s By adopting this, it is possible to avoid using a heart rate value that is too low in the calculation of the workload index even when the subject is moving at a certain level or more.

[0137] The correction map shown in FIG. 9 is a correction map that is used when it is determined that the detected heartbeat data is more reliable.

[0138] A case where the reliability of heartbeat data is high can be assumed to be a case where the detection rate of heartbeat data acquired by the biosensor 11 is higher than the judgment standard (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 when the acceleration deviation is 0.2 or more and the normalized heart rate is in a 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 cut-off heart rate βs at an acceleration deviation of 0.2, and 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, a 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, the workload index W is calculated as follows. c First, the corrected heart rate HR is converted into metabolic equivalents METs (Metabolic equivalents) using the following formula (Formula 3).

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

[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<00000​​​​​W is a predetermined parameter.

[0146] For example, a 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 a high metabolic rate job, i.e., a heavy workload; if the value of W is 1 or higher, it is an extremely high metabolic rate job, i.e., a very heavy workload for 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, it can be evaluated that the heat load is relatively high, and when the heat load index H is 1 or more, it can be evaluated that the heat load is extremely high.

[0151] (Assessment of the risk of heat stroke) Using the work strain index W and 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, with a = -1.8 in the case of heat acclimatization and a = -1.3 in the case of no heat acclimatization.

[0154] The heatstroke risk assessment value R calculated in the above manner can be judged as low risk if R is less than 0.6, low risk if R is 0.6 or greater but less than 1.0, requiring caution and a warning level, and high risk if R is 1.0 or greater, indicating a danger level of heatstroke.

[0155] Since it is not possible to verify the actual occurrence of heatstroke, when determining the criteria for judging the risk of developing heatstroke, the criteria should be determined so that the risk of developing heatstroke can be judged as strictly as possible, i.e., on the safe side.

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

[0157]

number

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

[0159] Furthermore, the exponential moving average value of the heatstroke risk index R can be calculated from the following equation (Equation 9).

[0160]

number

[0161] For example, if the exponential moving average value 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 are taken to prevent the onset of heatstroke, such as encouraging workers to take breaks.

[0162] (Displayed on a 2D map) As can be seen from the above equation (Equation 6), in the heatstroke risk management system according to this embodiment, the index R representing 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 heatstroke risk index can be displayed as a heatstroke risk index on a two-dimensional map with the heat load index and the work strain index as axes. For example, by displaying a symbol corresponding to the current heatstroke risk index for each of multiple workers 10 managed by a 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 / her management at a glance. Note that a detailed description of the display image that displays the degree of heatstroke risk for each worker 10 will be omitted.

[0164] [Handling of biological information obtained by devices other than the first measuring device] As described above, in the heatstroke risk management system according to this embodiment, the physical condition of each worker is evaluated and the risk of developing heatstroke is assessed 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 pulse meter 11b, which is the second measuring device, or the sensing unit 11c, which is the third measuring device, into biological information acquired by the first measuring device, workers wearing different types of biological information acquisition units can be managed in common by the heatstroke risk management system.

[0165] In the heatstroke risk management system of this embodiment, biometric information obtained by different measurement devices is integrated by using the median heart rate as a common parameter for heart rate data and the acceleration deviation as a common parameter for acceleration data.

[0166] (Method for processing biological information acquired by second measurement device) The wireless pulse rate monitor 11b outputs the pulse wave interval measured as heart rate data every second, which differs from the biosensor 11a in that the heart rate interval is output for each heart rate.

[0167] In the biosensor 11a, the reliability of the heart rate data was determined by estimating the heart rate waveform detection rate as described above, and only heart rate data that was determined to be normal was used to calculate the central heart rate HR. Correspondingly, in the pulse data output every second by the wireless pulse meter 11b, less reliable data was discarded and only values ​​that were 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 was discarded as being unreliable. Also, because it is physiologically unnatural for the same pulse to occur consecutively, if the same pulse data occurred five or more times in a row, the data from the fifth beat onwards was discarded as bad data.

[0169] In this way, using only reliable data, the one-minute median of the central heart rates in 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 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 low frequency components of 0.1 Hz or less.

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

[0172] The values ​​of the central heart rate and acceleration deviation obtained in this way are considered to be the same as the central heart rate and acceleration deviation obtained by the first measuring device, the biosensor 11a, so the work strain 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. Therefore, 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, research materials comparing data from clothing temperature sensors with WBGT (e.g., experimental data from Shinshu University) are publicly available, and appropriate reference to such materials is expected to lead to more accurate calculations of the heat load index.

[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), and also outputs METs estimates for the subject's activity status 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 for the difference between the median and average heartbeat intervals over one minute that occurs due to the asymmetry of the statistical distribution of heartbeat intervals and the characteristics of the pulse wave measuring device.

[0176] Furthermore, taking advantage of the fact that METs data and acceleration data correspond linearly during walking, light jogging, etc., it is possible to obtain a correlation coefficient by actually simultaneously acquiring data from two measuring devices, and convert the output METs data into acceleration data. From the resulting converted acceleration data, acceleration displacement is calculated in the same way as when it is calculated by the biosensor 11a.

[0177] Since the sensing unit 11c is worn on the arm of the worker 10 being evaluated, it detects not only the movements of the worker 10's entire body but also the fine movements of the arm on which it is worn as acceleration data. For this reason, even at a work site, for example, the acceleration data measured will differ between tasks such as carrying and assembling large components and tasks requiring the use of hands to handle small parts. In such cases, the reliability of the measurement data can be improved by taking measures such as appropriately grouping workers 10 performing similar tasks and setting different evaluation 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, the work strain index can be calculated in the same way as with the data obtained by the biosensor 11a.

[0179] The sensing unit 11c has a sensor that measures environmental conditions such as temperature and humidity. However, the measurement location is the wrist, which is naturally different from the temperature inside the clothing measured by the biosensor 11a. For this reason, when calculating the heat load index, as in the case of the wireless pulse meter 11b described above, 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, or to separately calculate the heat load index by correlating the temperature inside the clothing with the temperature of exposed body surfaces, and correcting the temperature data accordingly.

[0180] As described above, in the biometric information processing method and biometric information processing system disclosed in this application, the biometric information of the person being evaluated acquired by different biometric information acquisition units is converted into each other using a common index, thereby 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 build a biometric information processing system that can evaluate more subjects at a lower cost, in accordance with customer needs, for example, by allowing the subject to select and use the type of biometric information that they prefer, or by using a biometric information acquisition unit that they already own. As the number of subjects to be evaluated increases, the number of data processing examples in the biometric information processing system increases, making it possible to make corrections using past data and further 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 the 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, but other parameters such as the average heart rate can also be used.

[0184] Furthermore, in the above embodiment, the acceleration deviation is used as a common parameter for the acceleration data, but other parameters such as the square root of the resultant acceleration deviation can also be used.

[0185] Furthermore, in the above embodiment, the biometric information processing system disclosed in the present application is exemplified as a heatstroke risk management system in which workers working at construction sites, etc., are evaluated as subjects, but the system is not limited to the above example and can acquire biometric information from multiple subjects and 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 managing the physical condition of athletes during training or managing the physical condition of residents in 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 person being evaluated, and therefore can realize a method for processing biometric information for a larger number of people being evaluated.It is also possible to construct a highly versatile biometric information processing system at low cost, making it extremely useful. [Explanation of symbols]

[0188] 10. Worker (evaluee) 11a biosensor (first measuring device, bioinformation acquisition unit) 11b Wireless 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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