Biological information processing method and biological information processing system

Abstract

To provide a biological information processing method capable of processing data obtained from a plurality of forms of biological information acquisition units for acquiring biological information of an evaluated person, and a biological information acquisition system capable of adopting a plurality of forms of biological information acquisition units.SOLUTION: The method comprises steps of: acquiring biological information of an evaluated person by a biological information acquisition unit; and calculating an index showing the state of the evaluated person based on the acquired biological information, and the method converts the biological information acquired by two different types of the biological information acquisition units to a common parameter and calculates the index.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present application relates to a biological information processing method for performing physical condition evaluation of an evaluated person based on biological information obtained from the evaluated person, and a biological information processing system.

Background Art

[0002] In recent years, with the improvement of the connection environment to the Internet such as wireless LAN, the development of means enabling information transmission at short distances such as Bluetooth (registered trademark), and the spread of high-performance mobile devices such as smartphones and small sensor devices capable of measuring physical data such as body temperature, heart rate, and sweating amount, an evaluation system for evaluating the physical condition based on the biological information of the evaluated person obtained by the sensor device, and a physical condition management system for managing the health state of the evaluated person based on the evaluation result and reducing the risk of heat stroke, which has become a problem in recent years, have been put into practical use.

[0003] As an example of a biological information processing system for performing such physical condition evaluation and management, a wearable biological signal detection device including a three-dimensional acceleration sensor for grasping the movement of the body of the evaluated person and a biological information acquisition unit for detecting the heartbeat is used to constantly evaluate the risk of the evaluated person developing heat stroke and take measures to reduce the risk. A heat stroke risk management system has been proposed (see Patent Document 1).

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In the above-described conventional heat stroke risk management system, a biological information acquisition unit having a potentiometer for detecting the wearer's heartbeat, a three-dimensional acceleration sensor capable of detecting the movement of the wearer's body, and a temperature sensor capable of detecting the temperature inside the clothing is arranged at the chest of the undershirt. The biological information acquired by this biological information acquisition unit is transmitted to the information processing unit of a cloud server on the Internet via a communication device such as a smartphone possessed by the person being measured. In the information processing unit, for each individual and further for a group of workers working in the same environment, the risk of developing heat stroke is evaluated, and by instructing the workers with a high risk of developing heat stroke to take a break, the risk of developing heat stroke is reduced.

[0006] As a biological information acquisition unit equipped with sensors for detecting the pulse, body movement, body temperature, etc. of the person being measured, in addition to the wearable clothing type in which a potentiometer or the like is attached to the body of the person being measured using a shirt or the like as used in the above-described conventional heat stroke risk management system, there are also wristwatch types that detect the pulse by vibration or optical methods, which can reduce the discomfort of wearing the biological information acquisition unit and the ease of attachment and detachment, and types that are attached to the earlobe or fingertip and optically detect the pulse of the person being measured. Various types with different methods of acquiring biological information, shapes of devices, and attachment methods have been proposed.

[0007] In these various biological information acquisition units, the types and accuracies of the biological information acquired are different due to their structural constraints. In addition, these biological information acquisition units are each incorporated into a unique biological information processing system, and the settings for the frequency of detecting biological information data and the frequency of data transmission are different. Furthermore, due to differences in the shape, attachment location, data transfer ability within the system employed, and data processing functions incorporated within the biological information acquisition unit, the data processing specifications, i.e., the extent of data processing performed before the acquired data is sent out from the biological information acquisition unit, are also different. For this reason, conventionally, in a biological information processing system using a biological information acquisition unit, the biological information acquisition unit worn by the person being evaluated has been limited to those specific to the system.

[0008] However, in a biological information processing system, as biological information of more subjects is collected, the accuracy of evaluation results such as physical condition evaluation determined based on biological information is improved. To increase the amount of biological information thus obtained to improve the accuracy of evaluation results, to construct a biological information processing system at a lower cost, to obtain biological information in a more appropriate form according to differences in the environment where the subject is placed, the type and intensity of the subject's movements such as exercise and work, and further, to aim to obtain biological information in a state with less burden according to the preferences of individual subjects, it is preferable to use a biological information processing system that can use biological information from various biological information acquisition units of different types.

[0009] The present application aims to solve the problems of the above prior art, and as a biological information acquisition unit for acquiring biological information of an evaluatee, it aims to provide a biological information processing method capable of processing data from a plurality of forms, and a biological information processing system capable of adopting a plurality of forms of biological information acquisition units.

Means for Solving the Problems

[0010] To solve the above problems, the biological information processing method disclosed in the present application includes a step of acquiring biological information of an evaluatee by a biological information acquisition unit, and a step of calculating an index representing the state of the evaluatee based on the acquired biological information, and is characterized in that the biological information acquired by two or more different types of the biological information acquisition units is converted into a common parameter to calculate the index.

[0011] Further, the biological information processing system disclosed in the present application includes two or more different types of biological information acquisition units for acquiring biological information of an evaluatee, and a data processing unit for calculating an index representing the state of the evaluatee based on the acquired biological information, and is characterized in that the data processing unit converts the biological information acquired by the biological information acquisition unit into a common parameter to calculate the index.

Effects of the Invention

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

[0013] Also, with the above configuration, the biological information processing system disclosed in the present application expands the options for the biological information processing unit adopted as the system, and can realize a biological information processing system that is lower in cost, higher in accuracy, and more acceptable to the subject.

Brief Description of the Drawings

[0014]

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[0015] The biological information processing method disclosed in the present application includes a step of acquiring biological information of an evaluated person by a biological information acquisition unit, and a step of calculating an index representing the state of the evaluated person based on the acquired biological information. The biological information acquired by two or more different types of the biological information acquisition units is converted into a common parameter to calculate the index.

[0016] By having the above configuration, the biological information processing method disclosed in the present application can widen the range of selection of the biological information acquisition unit worn by the evaluated person, acquire biological information of more evaluated persons in a method with less burden on the evaluated person, and calculate an accurate index using the acquired biological information.

[0017] In the above biological information processing method, it is preferable that the index is at least one of a work load index indicating the degree of influence of the load received by the evaluated person, a physical condition evaluation index indicating the degree of change from the normal state of the physical condition of the evaluated person, and a heat stroke onset risk index indicating the degree of risk of the evaluated person developing heat stroke. By doing so, a practical index representing the state of many evaluated persons can be obtained.

[0018] Also, it is preferable that the biological information is at least one of heartbeat data, acceleration data, and METs (estimated metabolic equivalent). By doing so, differences in characteristics between individuals and differences in the specifications of the biological information acquisition unit can be corrected, biological information necessary for accurately determining the state of the person to be evaluated can be obtained, and an index representing the state of the person to be evaluated can be accurately calculated. Note that the heartbeat data includes the heart rate and the heartbeat time interval. Also, the biological information may include an estimated value of the energy consumption.

[0019] Furthermore, it is preferable that the common parameter used for the heartbeat data is the central heart rate. By doing so, accurate heartbeat data of the person to be evaluated can be obtained regardless of the method for acquiring the heartbeat data and the data processing specifications in the biological information acquisition unit.

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

[0021] The biological information processing system disclosed in the present application includes two or more different biological information acquisition units that acquire the biological information of the person to be evaluated, and a data processing unit that calculates an index representing the state of the person to be evaluated based on the acquired biological information. The data processing unit is characterized in that it converts the biological information acquired by the biological information acquisition unit into a common parameter and calculates the index.

[0022] By having the above configuration, the biological information processing system disclosed in the present application expands the scope of selection of the biological information acquisition unit, allows the person to be evaluated to select a biological information acquisition unit corresponding to their preferences and the physical state during measurement, and can easily calculate an index representing the state using the biological information of more persons to be evaluated, thereby realizing a biological information management system that can obtain an accurate evaluation result at low cost.

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

[0024] (Embodiment) [Overall Configuration of the System] First, an example of the overall configuration of the heat stroke onset risk management system disclosed in the present application will be described.

[0025] In the present embodiment, based on the actions of the worker, environmental temperature, heart rate, etc., based on a work load index indicating the magnitude of the burden received by the work and a heat load index indicating the magnitude of the thermal load of the worker, an example of a heat stroke onset risk management system that evaluates the physical condition of the worker and evaluates and manages the risk of heat stroke onset will be illustrated. The heat stroke onset risk management system according to the present embodiment is suitably used, for example, with a plurality of workers working at a single construction site as the subjects to be evaluated, evaluates the risk of developing heat stroke based on the biological information obtained from each worker, and gives a warning to a worker with an increased risk of heat stroke onset and takes measures such as allowing appropriate rest, aiming to reduce the risk of heat stroke onset at the construction site.

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

[0027] As shown in Fig. 1, the heat stroke onset risk management system according to this embodiment includes the worker 10 to be evaluated, a cloud server 21 on the Internet 20 that evaluates the physical condition based on the biological information of the worker 10 and evaluates the risk of heat stroke onset, a site supervisor 30 who is a manager that supervises the worker 10 to be evaluated and a work group including a certain number of workers 10, and further, an establishment 40 that places a plurality of site supervisors 30 under its management to grasp the whole and conducts operations and maintenance management of the heat stroke onset risk evaluation system. Note that the above is a general-purpose example assuming a general construction site. When the number of workers 10 managed by one site supervisor 30 is one, or when the site supervisor and the establishment are inseparable, or when a plurality of establishments are included to manage the entire construction site on a larger scale, it goes without saying that different forms can be adopted as appropriate according to the configuration of the site where the heat stroke onset risk management system of this embodiment is actually introduced.

[0028] In the heat stroke onset risk management system described in this embodiment, each worker 10 wears a measuring device 11 which is a biological information acquisition unit capable of detecting at least one or more pieces of biological information such as his or her own heartbeat data, acceleration data indicating body movement, and in-clothing temperature data as environmental temperature. And at least two types of this measuring device 11 are used.

[0029] For example, some workers wear an undershirt with a biological sensor 11a equipped with a temperature sensor for detecting in-clothing temperature, a potentiometer for detecting heartbeat data, and a three-dimensional acceleration sensor for detecting body movement as the first measuring device on the chest. Also, other workers wear a wireless type pulse meter 11b equipped with a measuring unit for optically measuring the pulse when worn on the earlobe and a main body unit with a three-dimensional acceleration sensor built in and worn at a position close to the center of the clothed body as the second measuring device. Further, another worker wears a wristwatch type sensing unit 11c equipped with a pulse sensor capable of detecting a pulse as a fine vibration, a three-dimensional acceleration sensor, and further a sensor for detecting air temperature and humidity as the third measuring device.

[0030] In addition, the biological information acquisition unit that can be adopted in the heat stroke onset risk management system according to this embodiment is not limited to the above-exemplified one, and various biological information acquisition units equipped with, for example, the heartbeat data of the person to be evaluated, an acceleration sensor indicating the movement of the body, a temperature sensor for measuring the environmental temperature, etc. can be adopted.

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

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

[0033] The smartphone 12 is provided with a data reception unit 15 and a data transmission unit 16, and is constantly connected to the Internet 20 as a network environment via a wireless LAN or an information carrier of a mobile phone. In the heat stroke onset risk management system of this embodiment, the smartphone 12 is associated with the identification data of each worker 10, and the smartphone 12 has an evaluated person information transmission unit 13. Using the data transmission function of the smartphone 12, the biological information in a state linked to the identification information of the worker is transmitted to the cloud server 21 arranged on the Internet 20.

[0034] In addition, for linking with an ID that identifies the measuring device worn by the operator and the work itself, various methods can be used, such as a method in which the operator inputs the name or management number of the measuring device used on the smartphone, a method in which a two-dimensional or three-dimensional identification code attached to the measuring device is read using the image recognition function of the smartphone, a method in which an identification code in short-range communication between the smartphone and the measuring device is used, and other methods selected by the operator using an application on the smartphone. Also, when the smartphone itself is lent out as part of system use rather than being the personal property of the operator, methods such as data input using the smartphone, reading of the identification code, use of a face recognition system, and other methods can be adopted for operator recognition.

[0035] In addition, since the smartphone 12 can receive data, emit sound, and display images, in the heat stroke onset risk management system according to the present embodiment, a warning notification unit 14 for fulfilling a warning notification function of communicating the heat stroke onset risk to the operator 10 and prompting the operator to take a break, and an image display unit 17 for fulfilling a function of easily displaying the heat stroke onset risk assessment results for each operator 10 and the entire group to which the operator 10 belongs are provided.

[0036] The cloud server 21 includes a data reception unit 23 and a data transmission unit 26 inside, and exchanges information via the Internet 20. Also, the cloud server 21 includes an evaluation determination unit 22 as a data processing unit, acquires biometric information data of all the operators 10 targeted by the heat stroke onset risk management system, calculates a work burden index indicating the degree of influence of the load received during the work for each operator 10, and a physical condition evaluation index indicating how much the physical condition has changed from the normal state of the operator, and based on these indexes, calculates a heat stroke onset risk index indicating the degree of risk of each operator 10 developing heat stroke. Also, the evaluation determination unit 22 can manage the heat stroke onset risk for groups of operators 10 formed by the commonality of the work content and work environment, etc.

[0037] In the heatstroke onset risk management system according to this embodiment, the heatstroke onset risks of individual workers 10 are managed. When it is determined that the heatstroke onset risk is particularly high, the information is transmitted to prompt the worker to take measures to reduce the heatstroke onset risk. For this purpose, the cloud server 21 evaluates and determines the heatstroke onset risk, and creates warning information to warn the worker when the heatstroke onset risk is increasing.

[0038] In addition, the cloud server 21 has a weather information acquisition unit 25, and acquires weather information from an information site that provides weather information via the Internet 20, such as the current weather conditions of temperature, humidity, sunshine amount, etc. in the area where the worker 10 is working, and the weather forecast expecting changes within the next few hours. The weather conditions can be taken into account in the evaluation of the heatstroke onset risk.

[0039] Furthermore, the cloud server 21 is provided with a data recording unit 24, and can record measurement data from each of the workers 10 registered in the heatstroke onset risk management system, the creation history of warning information, etc. in chronological order. Thereby, for example, the physical condition evaluation of each worker 10 up to the current time of the day, or the heatstroke onset risk is managed based on the results of the physical condition evaluation up to the previous day, or a more accurate heatstroke onset risk evaluation can be performed based on the evaluation results of the heatstroke onset risk under the same past weather conditions.

[0040] The cloud server 21 is connected via the Internet 20 to a personal computer 31 as a manager information terminal used by the on-site supervisor 30 who is a manager supervising the work of the worker 10 being evaluated at the construction site. For this reason, the on-site supervisor 30 at the work site where the worker 10 is working can grasp, by the data receiving unit 33 of the personal computer 31, data on the biometric information of the worker 10 transmitted from the cloud server 21 at any time, and whether warning information has been generated by the evaluation and determination unit 22.

[0041] The evaluation and determination unit 22 of the cloud server 21 evaluates the physical condition of the worker 10 based on the heartbeat data, acceleration data, and in-garment temperature data obtained from the measuring device 11 worn by the worker 10. Furthermore, it calculates a work burden index, and calculates a heat stroke onset risk index for the worker 10, taking into account the in-garment temperature information and the environmental temperature information of the work location obtained via the Internet.

[0042] Note that the specific details of the work burden estimation and heat stroke onset risk assessment of the worker 10 performed by the evaluation and determination unit 22 will be described later.

[0043] The cloud server 21 corrects the evaluation results of the heat stroke onset risk for the individual worker 10 based on the history data as the past history information of the worker 10 to be determined recorded in the data recording unit 24, the weather information of the work area obtained by the weather information acquisition unit 25, and environmental information such as changes in various information obtained from workers other than the worker to be determined who are working at the same site as the worker to be determined, and can perform more realistic heat stroke onset risk management.

[0044] Note that in the heat stroke onset risk management system exemplified in this embodiment, the cloud server 21 is not the only one equipped with the evaluation and determination unit 22. For example, various functions of the cloud server 21 may be implemented on the administrator information terminal or the management computer of the workplace. As long as the function can be realized, the location and device where the evaluation and determination unit is implemented do not matter.

[0045] The personal computer 31 of the on-site supervisor 30 is equipped with an information management unit 32 that manages whether various types of information and warning information obtained by the measuring device 11 for the worker 10 belonging to the work site supervised by the on-site supervisor 30 including the worker 10 have been generated. Based on the information transmitted from the cloud server 21, the information management unit 32 always grasps, as the latest information, the information serving as the criteria for the heat stroke onset risk assessment as to whether the information and warning information obtained from each worker 10 have been generated. Further, the information management unit 32 outputs the evaluation determination results of the heat stroke onset risk of each acquired worker 10 and other environmental information to the display image processing unit 35, and the screen content displayed on the display device 36 such as a liquid crystal monitor is adjusted by the display image processing unit 35.

[0046] In this way, the on-site supervisor 30 can grasp, in an integrated manner as a whole or in a screen that is easy to view as the detailed information of each worker, the information of the worker 10 working at the work site supervised by himself / herself, the heat stroke onset risk, etc. Regarding the specific screen content displayed on the display device 36 processed by the display image processing unit 35, since it is only necessary to be able to display the information required by the appropriately formed system in an easy-to-view manner, the specific detailed description in this specification is omitted.

[0047] Furthermore, in the personal computer 31 of the on-site supervisor 30, by receiving the change in the biological information obtained from the worker 10 after notifying the warning information and the receipt confirmation of the warning information from the worker 10, it is possible to confirm whether the worker 10 has taken measures to prevent the onset of heat stroke. If the worker 10 has not taken measures to prevent the onset of heat stroke, it is possible to further alert the worker 10, such as repeatedly transmitting the warning information to the target worker 10.

[0048] In the above description, an example was given in which warning information for notifying that the risk of the operator 10 developing heat stroke is high is generated by the evaluation determination unit 22 of the cloud server 21. However, the warning information can be generated by the information management unit 32 installed in the personal computer 31 of the on-site supervisor 30. Also, it can be set to generate warning information by both the evaluation determination unit 22 and the information management unit 32. By doing so, warning information can be generated from the personal computer 31 of the on-site supervisor 30 who is actually supervising the work site, prior to the determination result of the evaluation determination unit 22, and transmitted to the target operator 10, which may be able to further reduce the risk of heat stroke according to the actual situation 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 on-site supervisor 30 is transmitted from the data transmission unit 34 of the personal computer 31 of the on-site supervisor 30 to the smartphone 12 equipped by the operator 10 via a network including a local network such as a wireless LAN or an information carrier of a mobile phone. The warning notification unit 14 of the smartphone 12 that has received the warning information uses various information transmission means such as voice, screen display, lighting or blinking of a lamp, and vibration to notify the operator 10 that the risk of the operator himself / herself developing heat stroke has increased. The operator 10 who has confirmed the warning information reports that the warning information has been received through the touch panel or operation buttons of the smartphone 12, and also executes measures for preventing heat stroke such as interrupting the work and taking a break.

[0050] The smartphone 12 of the operator 10 transmits to the personal computer 31 of the supervisor 30 that the operator 10 has confirmed the warning information and interrupted the work, and the supervisor 30 can confirm that the operator 10 has taken measures to prevent the onset of heat stroke.

[0051] Furthermore, in the heatstroke onset risk management system described in this embodiment, the heatstroke onset risk data at the work site grasped by the on-site supervisor 30 is transmitted to the smartphone 12 of the worker 10, enabling the worker 10 to check the current status of the heatstroke onset risk at the work site where they are working. For example, if it is confirmed that the heatstroke onset risk of other workers is increasing, each worker can take proactive measures to prevent heatstroke. Also, if it is found that there is a worker who has received a warning about the heatstroke onset risk and interrupted their work, it is expected that they will respond obediently to the warning information addressed to themselves from the on-site supervisor 30.

[0052] Furthermore, on the smartphone 12 owned by the worker 10, changes in the heatstroke onset risk of the worker 10 up to the present, changes in their heart rate obtained by the biosensor 11, changes in the physical condition evaluation index calculated from the acceleration data, and related information such as calorie consumption can be displayed on the screen for the worker 10 to refer to. Regarding the display screens on the smartphones owned by these workers 10, as long as the necessary information can be clearly displayed according to each purpose, detailed descriptions in this specification are omitted.

[0053] The cloud server 21 is also connected to the management computer 41 within the company or workplace 40 to which the worker 10 belongs through the Internet 20, and transmits the measurement result information of the worker 10 sent to the personal computer 31 of the on-site supervisor 30 and various information used by the cloud server 21 to judge the heatstroke onset risk to the management computer 41 of the workplace 40 in real time. Since the management computer 41 of the workplace 40 is equipped with its own data receiving unit 42 and data transmitting unit 43, it is also connected to the personal computer 31 of the on-site supervisor 30 via the Internet, and can check information such as whether the warning information from the on-site supervisor 30 has been correctly transmitted to the worker 10 and whether the worker 10 has taken heatstroke prevention measures, and issue a predetermined instruction if necessary. Therefore, the avoidance of the heatstroke onset risk of the worker 10 can be effectively backed up.

[0054] In addition, since the cloud server 21, the personal computer 31 of the on-site supervisor 30, and the management computer 40 of the office 40 are connected in the Internet 20 environment, it is possible to access the cloud server 21 from the side of the personal computer 31 or the management computer 40, control the data processing content in the cloud server 21, update the determination program in the evaluation determination unit 22, or appropriately extract information necessary for heat stroke prevention management from the cloud server 21.

[0055] In the above description, a smartphone is exemplified as the mobile terminal equipped by the worker. However, the worker's mobile terminal is not limited to a smartphone, and a mobile phone, a tablet device, or even a dedicated small terminal device capable of transmitting and receiving information specialized for the heat stroke onset risk management system can be used. Further, as the administrator information terminal operated by the on-site supervisor, as the exemplified personal computer, various information devices capable of transmitting and receiving information through a network, such as a desktop personal computer, a notebook personal computer, a tablet-type personal computer, and a small server device, and data display and data recording can be adopted.

[0056] Furthermore, in the above description, a form of transmitting warning information from the administrator information terminal of the on-site supervisor to the worker's mobile terminal has been described. However, when the warning information is generated by the evaluation determination unit of the cloud server, the system can also be configured to directly transmit the warning information from the cloud server to the worker's mobile terminal.

[0057] Furthermore, the information transmission means connecting the worker, the on-site supervisor, and the management department within the office is not limited to those exemplified above, and it goes without saying that various information communication means for transmitting and receiving data can be used.

[0058] [Biological information acquisition unit] (First measuring device) First, regarding the first form of the measuring device 11 as the biological information acquisition unit, a biosensor of the type that acquires biological information by being closely attached to the chest of the worker who is the person to be evaluated will be described.

[0059] FIG. 2 is a diagram showing a configuration example of an undershirt with a biosensor, which is a first biological information acquisition unit, worn by an operator in the heat stroke onset risk management system according to the present embodiment. FIG. 2(a) shows the front surface of the undershirt with the biosensor attached, and FIG. 2(b) shows the back surface of the undershirt, that is, the side that contacts and faces the body surface of the operator.

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

[0061] The operator 10 wearing the first biological information acquisition unit in the heat stroke onset risk management system according to the present embodiment detects the heartbeat, in-clothing temperature, and movement of the operator 10 by the biosensor 11a. The electrode, which is a heartbeat detection means disposed on the back surface of the undershirt 18, contacts the chest, so that the heartbeat of the operator 10 can be detected from the change in the surface potential. Further, a temperature sensor (not shown) for detecting the in-clothing temperature and an acceleration sensor chip (not shown) for detecting the acceleration in three-dimensional directions are housed in the data acquisition and transmission unit 11a1. As described above, in the heat stroke onset risk management system according to the present embodiment, since the smartphone 12 possessed by each operator is used as a relay for the measured biological information, the data acquisition and transmission unit 11a1 only needs to include a minimum data processing circuit, a data transmission unit for performing short-distance communication, and a power source for driving these electronic circuits including each sensor. The data acquisition and transmission unit 11a1 can be made smaller and lighter, and the discomfort of the operator 10 wearing the undershirt 18 attached to the chest can be reduced.

[0062] As described above, in the heat stroke onset risk management system described in this embodiment, various methods are known for attaching the biosensor 11a that contacts the chest or the like of the worker 10 to acquire heartbeat data, in-body temperature data, and acceleration data generated by movement. However, compared to methods such as directly attaching to the chest as a measurement patch or using an elastic wearing belt, according to the method of fixing the biosensor 11a to the undershirt 18 worn by the worker 10 as shown in FIG. 2, the worker 10 can relax the special awareness of wearing the biosensor 11a and acquire the necessary information. Further, even if sweating or body twisting during work occurs to the worker 10, the biosensor 11a fixed to the undershirt 18 will not finally come off from the body surface of the worker 10, and the wearing position can be maintained in a substantially unchanged state. For this reason, although there may be a situation where a part of the heartbeat cannot be acquired as heartbeat data at the moment when the biosensor 11a is separated from the chest of the worker 10, a situation where no heartbeat data can be acquired continuously can be avoided.

[0063] In addition, as the placement location of the biosensor 11a for acquiring the heartbeat data of the worker 10, in addition to the chest of the worker described above, the waist, back, upper arm, or leg of the worker can be adopted. However, it goes without saying that it is preferable to limit it to the range where the acceleration sensor and temperature sensor built in the biosensor 11a can acquire good measurement data. Further, not as a system for managing the onset risk of heat stroke with a worker working at a construction site as the evaluated person, for example, when evaluating the onset risk of heat stroke as a physical condition evaluation of a sports player who conducts training, etc., it is conceivable that the evaluated person wears sports wear. Also in this case, it is most reasonable to place the biosensor on the chest of the wear worn on the upper body.

[0064] (Second measuring device) Next, as an example of a second form of the measuring device 11 as a biological information acquisition unit, a type of sensor that optically detects blood vessel constriction to detect the operator's pulse as heartbeat data and detects the movement of the operator's body with a three-dimensional acceleration sensor will be described.

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

[0066] As shown in FIG. 3, the wireless type pulse meter 11b includes a pulse detection portion that is attached by sandwiching the earlobe with a pair of measurement pieces 11b1 and 11b2 biased by a spring or the like, and a main body portion 11b3 that is wired-connected to the pulse detection portion.

[0067] In the pulse detection portion, a light source such as an LED is arranged on one of the pair of measurement pieces 11b1 and 11b2, and a light receiving element such as a CCD is arranged so as to face each other on the other side, and can be brought into close contact with and fixed to the earlobe by a biasing means such as a spring. The pulse of the operator 11 is detected from the constriction of the blood vessels in the ear photographed by the CCD. The main body portion 11b3 incorporates a control circuit that outputs data on the pulse rate by appropriately excluding noise components from the contraction image of the blood vessels detected by the pulse detection portion, an operating power source for the entire pulse meter, a three-dimensional acceleration sensor, a data transmission portion, and the like.

[0068] In the wireless type pulse meter 11b of the type shown in FIG. 3, in order to more accurately grasp the movement of the body of the operator 11 who is the person to be evaluated, it is preferable to fix the main body portion 11b3 in which the three-dimensional acceleration sensor is arranged to the upper body close to the trunk of the operator 10, and as shown in the drawing, it is preferably attached to the rear portion of the collar where a movement different from the movement of the body of the operator 10 is unlikely to occur.

[0069] The commercially available wireless type pulse meter 11b shown in FIG. 3 does not have a function of measuring the environmental temperature. However, if the main body 11b3 is attached to the rear side of the collar as shown in the figure, the discomfort felt by the operator 10, who is the subject to be evaluated, when wearing the main body 11b3 is smaller than when wearing the biosensor 11a shown in FIG. 2 on the chest. For this reason, it is possible to incorporate various sensors for grasping environmental conditions such as a thermometer and a hygrometer inside the main body 11b3. Also, for the same reason, a transmission unit capable of directly transmitting biological information to the Internet environment can be provided inside the main body 11b3. In this case, it is not necessary for the operator 10 to carry the smartphone 12 equipped with the subject information transmission unit 13. When the operator 10 is not made to carry the smartphone 12, in order to exhibit the function of the warning notification unit 14 that the smartphone 12 has been performing, a voice warning notification function may be incorporated inside the main body 11b3 of the wireless type pulse meter 11b.

[0070] Note that, in the wireless type pulse meter as the second measuring device shown in FIG. 3, the contraction of the blood vessels in the earlobe was measured to acquire heartbeat data. However, it is also possible to optically detect the pulse at locations other than the earlobe, for example, at the fingertip portion. Also, in the wireless type pulse meter shown in FIG. 3, an example in which the pulse detection portion and the main body are wired-connected was illustrated. However, if the operating power supply of the pulse measurement portion can be easily secured, it is also possible to connect the pulse measurement portion and the main body by a short-range wireless connection.

[0071] (Third Measuring Device) Subsequently, regarding the third form of the measuring device 11 as the biological information acquisition unit, a wristwatch type sensor in which a device main body having a vibration sensor for detecting a pulse, an acceleration sensor for detecting an operation, and sensors for measuring environmental conditions such as air temperature and humidity is attached to the wrist using a belt will be described.

[0072] FIG. 4 is a diagram showing a wristwatch type sensing unit which is a third measuring device for acquiring the biological information of an operator in the heat stroke onset risk management system according to the present embodiment.

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

[0074] The wristwatch-type sensing unit 11c is worn such that the device main body 11c1 is on the outside of the wrist (the back of the hand side), and on the side in contact with the wrist (the inside of the fixing belt 11c2), a pulse detection unit that can be pressed against the outside of the wrist of the operator 10, who is the person to be evaluated, to detect the pulse is arranged (not shown). Further, inside the device main body 11c1, sensors for measuring environmental information such as temperature, humidity, and atmospheric pressure, and a three-dimensional acceleration sensor for detecting the movements of the operator 10 are arranged. On the outer surface of the device main body 11c1, as shown in the figure, small openings are formed in parts corresponding to these sensors. Also, on the side surface of the device main body, connection electrodes are arranged that supply power to the secondary battery arranged inside the device main body and enable data exchange with the internal memory element (not shown). By placing it on a dedicated cradle, it is possible to charge the operating battery of the sensing unit 11c and exchange data with a personal computer or the like.

[0075] Further, as a wristwatch-type measuring device, there is included what is called a smartwatch that has a clock function of displaying the current time with an image display device arranged on the surface of the device main body part. Some smartwatches are equipped with an optical heart rate sensor that measures the heart rate based on the absorption of infrared rays and have a carrier communication function. By using such a smartwatch, it is possible to directly transmit biological information such as heart rate information acquired by the measuring device to a cloud server via the Internet.

[0076] In the wristwatch-type measuring device such as the sensing unit 11c illustrated in FIG. 4, which is used in the heat stroke onset risk management system according to the present embodiment, inside the device main body 11c1, there is a transmission unit that transmits biometric information measured by the smartphone 12 held by the worker 10. Note that since the third measuring device is also worn on the outer side of the wrist portion of the person to be evaluated, similar to the wireless pulse meter which is the second measuring device, the discomfort felt by the worker 10 when wearing the device main body 11c1 is smaller compared to the case where the biosensor 11a shown in FIG. 2 is worn on the chest. For this reason, also in the sensing unit 11c shown in FIG. 4, a transmission unit capable of directly transmitting biometric information to the Internet environment can be provided inside the device main body 11c1. Further, when the worker 10 does not have the smartphone 12 having the person-to-be-evaluated information transmission unit 14, the device main body 11c1 will perform the function of the warning notification unit 14. However, including the case of a smartwatch, a warning notification function for the worker 10 and a configuration for showing data on the risk of heat stroke onset in an image or the like are easy to adopt.

[0077] [Heat Stroke Onset Risk Evaluation Method] Next, regarding the evaluation of the hot environment in the heat stroke onset risk management system according to the present embodiment, the specific content of the heat stroke onset risk evaluation for an individual worker will be described.

[0078] The heat stroke onset risk evaluation method according to the present embodiment calculates a work load index indicating the intensity of the work performed by the person to be evaluated based on the heart rate data of the person to be evaluated detected by the heart rate detection means provided in the measuring device worn by the person to be evaluated and the acceleration data acquired by a three-dimensional acceleration sensor. Further, based on the temperature inside the clothing of the person to be evaluated obtained from the measuring device and the environmental temperature at the site where the person to be evaluated is working, a heat stress load index of the person to be evaluated is calculated. Then, based on these calculated work load index and heat stress load index, a heat stroke onset risk index indicating the risk of developing heat stroke is calculated.

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

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

[0081] In the heat stroke onset risk assessment system according to the present embodiment, an evaluation determination unit 22 which is a control means provided in a cloud server 21 on the Internet calculates a work load index and a heat stress load index of a worker 10 based on data obtained from a biosensor 11a which is a first measuring device serving as a biological information acquisition unit worn by the worker 10 who is the person to be evaluated, and data acquired from each component in the cloud server 21, and calculates a heat stroke onset risk index.

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

[0083] Note that the start (START) of the evaluation in the evaluation determination unit 22 can be set by various methods such as the worker 10 himself / herself or a site supervisor 30 who is an administrator turning on the power switch of the biosensor 11a which is a measuring device, being set so that the operation of the biosensor 11 automatically starts when the work start time is reached by a timer, and the biosensor 11a itself detecting that the worker has worn an undershirt 18 equipped with the biosensor 11a and starting the operation.

[0084] In order to calculate the work load index, the evaluation determination unit 22 first checks whether history data which is heartbeat data and acceleration data is recorded in the data recording unit 24 as past data of the worker 10 to be evaluated (step S101).

[0085] When the worker 10 has been an evaluation target in the heat stroke onset risk management system described in the present embodiment in the past and the history data of the worker 10 is recorded in the data recording unit 24 (when "Yes" in step S101), a linear section in which the heart rate linearly changes with respect to the acceleration is obtained from the set of the history data, and a regression line is obtained for the history data included in the linear section. The regression line obtained from this history data represents the characteristics (personality) of the heart rate response of the worker. The slope of this regression line is defined and calculated as the heart rate response coefficient αr, and the intercept is defined and calculated as the intercept heart rate βr (step S102).

[0086] Thereafter, the evaluation determination unit 22 detects the heart rate data of the worker 10 measured by the biological sensor 11a (step S103).

[0087] On the other hand, when there is no history data of the worker 10, or when a certain period (for example, one month) has elapsed since the previous data was recorded although the data exists, the evaluation determination unit 22 determines that it is impossible to calculate the correct standardized heart rate of the worker 10, and calculates the work load index of the worker 10 from only the acceleration data without relying on the heart rate data. The evaluation determination unit 22 calculates an acceleration deviation, which is a numerical value indicating the movement of the worker 10 (step S110), and calculates the work load index of the worker 10 based only on the acceleration deviation (step S111). In this case, it may be converted into a work load index using a predetermined mathematical formula such as multiplying the acceleration deviation by an appropriate coefficient.

[0088] When the history data of the worker 10 is recorded in the data recording unit 24 (when "Yes" in step S101), the evaluation determination unit 22 ensures the reliability of the heartbeat data when detecting the heartbeat data. In the heat stroke onset risk management system of the present embodiment, as described above, in order to better acquire the heartbeat data of the worker 10 who is the person to be evaluated, the electrode portion 11a2 of the biosensor 11a is disposed on the back surface 18b of the undershirt 18 worn by the worker 10. However, due to the influence of the movement of the worker's body and sweating on the body surface, it may not be possible to correctly detect the heartbeat. Therefore, in the heat stroke onset risk management system of the present embodiment, when the heartbeat data of the worker 10 who is the person to be evaluated cannot be correctly measured, it is confirmed whether the heartbeat data can be correctly acquired so that the work load index is not calculated with incorrect data and the onset risk evaluation of heat stroke is not mistaken.

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

[0090] The raw data sampled from the biosensor may include a certain proportion of noise (abnormal heartbeat data) due to the influence of poor contact between the skin of the person to be evaluated and the electrode. Therefore, for the data (heartbeat interval) for each beat, for example, the data with a heartbeat interval of 0.33 seconds or more and 1.33 seconds or less, and the difference (differential heartbeat interval) from the previous data of 0.15 seconds or less is determined to be normal and labeled.

[0091] The threshold for determining normal / abnormal can be arbitrarily set, but appropriate values may be set so as to remove data of heartbeat intervals that cannot exist based on physiological viewpoints. Then, the measurement data is divided into k partial intervals with a predetermined time width, and the heartbeat waveform detection rate Q is calculated as to what percentage of the data labeled as normal is included in the total interval data for each partial interval.

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

[0093] If the heart rate waveform detection rate is equal to or higher than a reference value (for example, 50%), it is determined that the reliability of the relevant section is high (when "Yes" in step S105), and the workload index is calculated using the heart rate data (step S106).

[0094] On the other hand, if the heart rate waveform detection rate is less than the reference value (50%), it is determined that the reliability is low (when "No" in step S105), and the process proceeds to step S110 of calculating the workload index using the acceleration data for the relevant section.

[0095] Note that the reference value in the above description is just an example and can be adjusted as appropriate according to the performance of the biosensor, the occupation of the subject, etc. For example, for occupations with intense movements, the threshold value may be set low, and for occupations with few movements, the threshold value may be set high.

[0096] When the heart rate waveform detection rate is 50% or more (when "Yes" in step S105), the evaluation determination unit 22 calculates the central heart rate from the obtained heart rate data (step S106). Here, for the partial sections set in step S104, the representative value (median) for each partial section is used as the central heart rate data. The representative value may be the interval average value, but preferably, it is the interval median. This is because even if the data acquired from the measuring device contains a small number of irregular values, the influence can be eliminated.

[0097] Furthermore, the evaluation determination unit 22 calculates an acceleration deviation, which is a numerical value indicating the operation status of the operator 10, from the acceleration data obtained from the biosensor 11a at the same time (step S107).

[0098] Next, based on the history data and the pre-created standard heart rate response model, the central heart rate is corrected to obtain the normalized heart rate (step S108).

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

[0100] HR S [k] = (αs / αr)(HR[k] - βr) + βs (Equation 1) Herein · 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 is. Here, k represents the number of the sub - interval.

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

[0102] An example of the measurement results for obtaining the standard heart rate response model is shown in FIG. 6.

[0103] FIG. 6(a) plots all of the large - scale data (about 3 million points), and the shading represents the data density. In FIG. 6(a), line 51 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] On the other hand, the median of each section is the × mark in FIG. 6(b), and the approximate curve F HR (reference numeral 56) represents the standard heart rate response model.

[0105] The approximate curve F HR can be obtained by various curve fitting methods, and when the acceleration deviation A RMS is given, it can be expressed as a function F HR that gives the estimated normalized heart rate F HR (A RMS ). Also, FHR The slope of the linearly changing part (the section where the acceleration is from about 0.05 to about 0.45) corresponds to the standard heart rate response coefficient, and the intercept of the approximate curve corresponds to the standard intercept heart rate.

[0106] Note that the large-scale data may be the data of a plurality of workers in the past few days at the site, or the accumulated data sampled in advance at another site. Preferably, the standard heart rate response model is created based on the large-scale data obtained by measuring a large number of workers engaged in the same work as the worker. This is a heart rate response model optimized for the work and is considered to represent the typical heart rate response of the workers engaged in the work. There is no particular determination for the number of people on which the large-scale data is based, but the larger the sampling number, the more accurately the heart rate response can be approximated. Preferably, it is 5 or more, more preferably 50 or more. There is no particular determination for the accumulation period either, but preferably, data of 2 days or more, more preferably 5 days or more, is acquired at the same site.

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

[0108] At this time, the 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. Specific methods for obtaining the central heart rate and the acceleration deviation from the heart rate data obtained from the biological sensor 11 which is the measuring device and the numerical values of the three-dimensional acceleration sensor, selection criteria for the heart rate data using the correction map, etc. will be described in detail later.

[0109] On the other hand, when the heart rate waveform detection rate Q is not equal to or higher than the reference value (50% in the above example) (when "No" in step S105), the evaluation determination unit 22 determines that the reliability of the obtained heart rate data is low and does not calculate the central heart rate. Instead, it calculates the acceleration deviation, which is a numerical value indicating the operating status of the operator 10, from the acceleration data obtained from the biosensor 11a (step S110). In this case, based only on the acceleration deviation, the evaluation determination unit 22 calculates the work load index of the operator 10 (step S111).

[0110] The work load index obtained in step S111 is compared with the numerical value of the work load obtained in step S109. Although it is considered that the accuracy is inferior by the amount not reflected in the heart rate data, since the operations of the operator 10 are continuously performed, it is preferable that the work load index is continuously obtained rather than not calculating the work load index during that period on the grounds that no heart rate data is obtained.

[0111] Furthermore, the evaluation determination unit 22 calculates the heat stress load index of the operator 10 based on the in - clothing temperature data of the operator 10 obtained from the biosensor 11a and the environmental temperature data at the work site where the operator 10 works (step S112). The environmental temperature data at the work site can be obtained based on the ambient temperature data around the work site acquired by the weather information acquisition unit 25 of the cloud server 21, temperature information obtained from a temperature sensor arranged at the work site when the operator is working indoors, and the like.

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

[0113] Then, the evaluation determination unit 22 calculates the heat stroke onset risk of the operator 10 as a heat stroke onset risk index based on the obtained work load index and heat stress 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 load index. Therefore, the larger 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 slightly 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 take measures such as stopping work and taking a break, reducing the work load, or lowering the in-clothing temperature to reduce the heat load, and it becomes 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 Workload) <a. Pretreatment> First, pretreatment for calculating the work load index is performed on the heartbeat data and the acceleration data.

[0117] As described with reference to the flowchart of FIG. 5, 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 (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] For the acceleration data obtained by the acceleration sensor, the average value ΔA for the past one minute is obtained by the following procedure.

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

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

[0122] 2) Removal of the exponential moving average The exponential moving average described above is removed from the acceleration data of each axis to obtain the time-series acceleration after trend removal. For example, in the case of the x-axis, it becomes "Ax(t) - Sx(t)".

[0123] 3) Calculation of the sum of squares For the time-series acceleration after trend removal, the squares at each time are calculated using the following formula (Formula 2) to obtain the sum.

[0124]

Equation

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

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

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

[0128] <c. Calculation of resting heart rate and heart rate response coefficient> Regarding the set of history data, a linear response interval in which the central heart rate changes linearly with respect to the acceleration deviation is set, and a regression line is obtained for the data included in the linear response interval. The slope of this regression line is the heart rate response coefficient αr, and the intercept is the resting heart rate βr.

[0129] The method of fitting the regression line is not particularly limited. For example, an interval where the acceleration deviation is 0.05 to 0.4 is set as the linear response interval, and this interval is divided into m sub-intervals (m is, for example, 3 to 7). Next, the central values of the central heart rate and the acceleration deviation are obtained for each sub-interval. 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 history data of the operator 10 who is the subject to be measured, based on this history data and the standard heart rate response model, the standardized heart rate HR S is calculated. By calculating the standardized heart rate HR S it is possible to correct individual differences in calculating the workload index from the heartbeat data due to the characteristics of each subject to be measured.

[0131] On the other hand, the estimated standardized heart rate is the approximate formula F of the standard heart rate response model HR (A RMS ) is used, and the acceleration deviation A of the subject to be measured RMSIt estimates the standard heart rate. In calculating the actual workload index, the key point is which of the standardized heart rate and the estimated standardized heart rate to trust. 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 is obtained.

[0132] Figure 8 is the first example of the correction map.

[0133] As shown in Figure 8, in the correction map, an approximate curve F HR 71 is described, with the standard section heart rate βs as the y-axis intercept and the slope of the straight line part being the standard heart rate response coefficient αs. HR Here, the approximate curve F HR 71 is used as the determination line. It should be noted that from the part where the acceleration deviation exceeds 0.45, as shown in Figure 7, the determination line 71 is no longer a straight line, and it has been found that the degree of increase in the central heart rate with respect to the acceleration deviation decreases.

[0134] In the correction map shown in Figure 8, a boundary line 72 is provided at the part where the acceleration deviation indicating the movement of the operator, who is the subject to be measured, is 0.2. This is because in the region where the acceleration deviation is less than 0.2, the influence of emotion appears to be greater than the change in heart rate due to movement, and in the region where the acceleration deviation is greater than 0.2, the fluctuation of the heart rate due to the body's movement is considered to be large.

[0135] In the correction map shown in Figure 8, the relationship between the heart rate and the acceleration deviation is corrected so as to fall within the ranges of the regions 73 and 77 indicated by the hatching in the map. For example, in the range where the acceleration deviation is up to 0.2, when the standardized heart rate is large and located above the determination line 71, as shown by the arrow 74 in the figure, the value of the determination line 71, that is, the estimated standardized heart rate, is used as the corrected heart rate HR s , and when the standardized heart rate is smaller than the standard section heart rate βs, the value of the standard section heart rate βs is adopted as the corrected heart rate HR s . Also, when the standardized heart rate is below the determination line 71 and above the standard section heart rate βs, the standardized heart rate is directly used as the corrected heart rate HR sIt is adopted as such. By doing so, in a region where the acceleration deviation is smaller than 0.2, when a normalized heart rate larger than the estimated normalized heart rate is detected, this can be excluded as an influence caused by emotion.

[0136] On the other hand, in a region where the acceleration deviation is larger than 0.2, a parallel line 76 that defines a region where the heart rate value is determined to be too large is drawn parallel to the linear part of the approximation curve F, that is, the same slope as the aforementioned standard heart rate response coefficient αs. When it falls within the region 77 sandwiched between this parallel line 76 and the determination line 71, it is determined that the correct heart rate has been detected. When it corresponds to this region 77, the original normalized heart rate is used as the corrected heart rate HR HR and the workload index is calculated. When the normalized heart rate is larger than the parallel line 76 indicating the upper limit, as shown by the arrow 78 in the figure, the value on the parallel line 76 is adopted as the corrected heart rate HR s to eliminate the influence of errors. Also, for the numerical values that appear in the region below the determination line 71, as shown by the arrow 79 in the figure, the numerical value on the determination line 71, that is, the estimated normalized heart rate, is adopted as the corrected heart rate HR s to avoid the situation where a heart rate value that is too low is used in the calculation of the workload index even though the subject being measured is moving more than a certain amount. s The correction map shown in FIG. 9 is a correction map used when it is determined that the reliability of the detected heart rate data is higher.

[0137] As a case where the reliability of the heart rate data is high, it can be assumed that the detection rate of the heart rate data acquired from the biosensor 11 is higher than the determination criterion (for example, 50%), and for example, a state where it continues to exceed 80% continues.

[0138]

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

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

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

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

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

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

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

[0146] For example, a W = 0.2, b W When set to = -0.2, as an evaluation of the work load, if the work load index W is 0.6 or more, it is a high metabolic rate work, that is, a work with a large load. When the value of W is 1 or more, it can be considered as a work with an extremely high metabolic rate, that is, a work with a very large load on the worker.

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

[0148]

Number

[0149] When the heat stress load index H is less than 0, set H = 0.

[0150] When the heat stress load index H is 0.6 or more, it can be evaluated that the heat stress load is in a relatively high state. When the heat stress load index H is 1 or more, it can be evaluated that the heat stress load is in an extremely high state.

[0151] (Evaluation of heat stroke onset risk) Using the work load index W and the heat stress load index H obtained by the above calculation, as shown in the following formula (Formula 6), the heat stroke onset risk evaluation index R of the worker 10 to be evaluated is obtained.

[0152]

Number

[0153] Here, a is a numerical value defined corresponding to the heat acclimatization of the worker to be evaluated. When there is heat acclimatization, a = -1.8, and when there is no heat acclimatization, a = -1.3.

[0154] Regarding the heat stroke onset risk assessment value R obtained as described above, when R is less than 0.6, the onset risk is a low risk; when R is 0.6 or more and less than 1.0, it is a cautionary warning level; and when R is 1.0 or more, it is a high risk and the danger level of heat stroke onset, and it can be determined accordingly.

[0155] Note that since it is not possible to actually verify until the occurrence of heat stroke, when determining the criteria for judging the risk of heat stroke onset, it should be determined on the safer side so that the risk of heat stroke onset can be judged more strictly.

[0156] (Continuous evaluation of heat stroke onset risk) When continuously evaluating the heat stroke onset risk of an operator based on measurement results obtained from a biosensor 11, which is a measuring device worn by the operator 10, the exponentially weighted moving average values of the heat stress load index H and the work load index W are obtained from the following formulas (Formula 7) and (Formula 8) with a sampling interval of 1 minute.

[0157] [Number]

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

[0159] Furthermore, the exponentially weighted moving average value of the heat stroke onset risk index R is obtained from the following formula (Formula 9).

[0160] [Number]

[0161] For example, when the exponentially weighted moving average value of the heat stroke onset risk index R continues to be 1 or more for 30 minutes or more, it is judged that the risk of developing heat stroke is extremely high, and countermeasures such as prompting the operator to take a break are taken so as not to develop heat stroke.

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

[0163] Utilizing this, the heat stroke onset risk index can be displayed as a heat stroke onset risk indicator on a two-dimensional map with the heat stress index and the work load index as the respective axes. For example, by displaying symbols corresponding to the current heat stroke onset risk index for each of the multiple workers 10 managed by the on-site supervisor 30, who is the administrator, on a two-dimensional map, the on-site supervisor 30 can grasp the overall risk indicator of the workers to be managed at a glance. Note that a specific description of the display image showing the degree of the heat stroke onset risk of the worker 10 is omitted.

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

[0165] In the heat stroke onset risk management system according to this embodiment, the central heart rate is used as a common parameter for the heart rate data, and the acceleration deviation as a common parameter for the acceleration data is used to integrate the biometric information obtained by different measuring devices.

[0166] (Method for Processing Biometric Information Acquired by the Second Measuring Device) In the wireless type pulse meter 11b, the pulse wave intervals measured as heart rate data are output every second. In this regard, the timing of data output is different from that of the biosensor 11a which output the heart rate interval for each heartbeat.

[0167] In the biosensor 11a, as described above, the reliability of the heart rate data was judged by estimating the heart rate waveform detection rate, and the central heart rate HR was obtained using only the data that could be judged as normal heart rate data. Correspondingly, also in the pulse rate data of the wireless type pulse meter 11b, among the pulse rate data output every second, the data with low reliability were discarded and only the values considered to have high reliability were used.

[0168] More specifically, the data with a value of 0, the data less than 45 bpm or greater than 180 bpm were discarded as having low reliability. Also, since it is physiologically unnatural for the same pulse rate to continue continuously, when the same pulse rate data continued 5 times or more, the data after the 5th beat were discarded as bad data.

[0169] In this way, using only the highly reliable data, the 1-minute median value of the central heart rate with a window width of 60 seconds every 30 seconds was taken as the central heart rate HR.

[0170] Regarding the acceleration data, in the wireless type pulse meter 11b, as shown in FIG. 3, it is measured around the head or neck such as the rear part of the collar or the upper body, so only the high-frequency components of the acceleration data were extracted. As an example, by passing through a high-pass filter with a cut-off frequency of 0.1 Hz or less, it is considered that the noise components that are not the movements of the operator 10, who is the subject to be evaluated, are removed.

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

[0172] The values of the central heart rate and the acceleration deviation obtained in this way are considered to be the same as the central heart rate and the acceleration deviation acquired by the biological sensor 11a, which is the first measuring device. Therefore, as described above, the work load index can be calculated hereinafter.

[0173] As described above, some commercially available wireless-type pulse meters 11b do not have sensors for measuring environmental conditions such as temperature and humidity. For this reason, when calculating the heat stress index, it is preferable to estimate the environmental conditions where the worker 10 is located using the data acquired by the weather information acquisition unit 25 of the cloud server 21. In this case, since research materials (such as experimental data at Shinshu University as an example) regarding the comparison between the data of the in-clothing temperature sensor and WBGT are publicly available, it is expected that by appropriately referring to the content, it will lead to a more accurate calculation of the heat stress index.

[0174] (Method for processing biological information acquired by the third measuring device) As the sensing unit 11c, the pulse wave intervals measured as heart rate data are output at regular intervals (for example, every minute). Also, for the motion status of the person being measured, the METs estimated value is similarly output at regular intervals (for example, every minute).

[0175] In this case, by multiplying the pulse interval data for one minute by a predetermined coefficient, for example, 0.984, a correspondence with the heart rate interval output by the biological sensor 11a can be established, and the central heart rate value can be obtained. This coefficient is for correcting the difference between the median and the average value of the heart rate interval for one minute due to the asymmetry of the statistical distribution of the heart rate interval and the characteristics of the pulse wave measuring device.

[0176] Also, by using the fact that METs data and acceleration data linearly correspond in activities such as walking and light jogging, for example, the correlation coefficient can be obtained by actually simultaneously acquiring data from two measurement devices, and the output METs data can be converted into acceleration data. From the obtained converted acceleration data, the acceleration displacement is obtained in the same manner as when obtained by the biosensor 11a.

[0177] Since the sensing unit 11c is worn on the arm of the operator 10 who is the person to be evaluated, in addition to the movement of the entire body of the operator 10, fine movements of the worn arm are detected as acceleration data. For this reason, for example, even at a work site, the acceleration data as measurement results differs between an operation of carrying and assembling large members and a fine operation using the fingertips for handling fine parts. In such a case, by appropriately grouping the operators 10 performing the same type of work and taking measures such as making the criteria different for each group, the reliability of the measurement data can be improved.

[0178] In this way, by using the heartbeat data and METs data acquired by the sensing unit 11c to obtain the central heart rate and the acceleration deviation, the work load index can be calculated in the same manner as the data obtained by the biosensor 11a.

[0179] The sensing unit 11c has sensors for measuring environmental conditions such as temperature and humidity. However, the measurement location is the wrist part, which is naturally different from the in-clothing temperature measured by the biosensor 11a. Therefore, when calculating the heat stress index, similar to the case of the above-mentioned wireless pulse meter 11b, data obtained by the weather information acquisition unit 25 of the cloud server 21 is used to estimate the environmental conditions where the operator 10 is located, or the correlation between the in-clothing temperature and the temperature of the exposed body surface part is separately taken, etc., and it is preferable to appropriately correct the temperature data to calculate the heat stress index.

[0180] As described above, in the biological information processing method and biological information processing system disclosed in the present application, for the biological information of the evaluated person acquired by different biological information acquisition units, by mutually converting using a common index, it is possible to evaluate and manage the evaluated person included in a common system without being restricted by the form of the biological information acquisition unit.

[0181] As a result, for example, it becomes possible to select and use the type of biological information that the evaluated person thinks is more preferable, or to directly use the biological information acquisition unit that the evaluated person already owns. Thus, it is possible to construct a biological information processing system that targets more evaluated persons at a lower cost according to the needs of customers. As the number of evaluated persons increases, the data processing examples in the biological information processing system increase, so it becomes possible to correct using past data, and the accuracy of the evaluation results obtained from the biological information can be further improved.

[0182] Also, regarding the difference in the accuracy of the acquired data due to the difference in the measuring devices, it is possible to grasp the tendency by referring to a large amount of data, so the accuracy of the conversion of the data acquired by different biological information acquisition units can be improved.

[0183] In addition, in the above embodiment, the case where the central heart rate is used as the common parameter of the heart rate data is shown, but in addition to the central heart rate, the average heart rate, etc. can also be used as parameters.

[0184] Also, in the above embodiment, the acceleration deviation is used as the common parameter of the acceleration data, but in addition to the acceleration deviation, the square root of the mean square deviation of the combined acceleration, etc. can also be used as parameters.

[0185] In addition, in the above embodiment, as the biological information processing system disclosed in the present application, a heat stroke onset risk management system with workers working at a construction site or the like as the subjects to be evaluated was exemplified. However, the present invention is not limited to the above-exemplified system, and it is possible to acquire biological information of a plurality of subjects to be evaluated, and based on the acquired biological information, it can be used as a biological information processing system for evaluating the work load index, physical condition evaluation index, heat load index, exercise load index, and other respective indexes of each subject to be evaluated.

[0186] For example, it can be used in systems for physical condition management during training of athletes, physical condition management systems for residents in elderly facilities, etc., which perform biological information processing in a wide range of contents where the subjects to be evaluated, the biological information to be measured, and the evaluation purposes are different.

Industrial Applicability

[0187] Since the biological information acquisition unit worn by the subject to be evaluated is not limited in the biological information processing method and the biological information processing system disclosed in the present application, it is possible to realize a biological information processing method with a larger number of subjects to be evaluated, and a highly versatile biological information processing system can be configured at low cost, which is extremely useful.

Explanation of Signs

[0188] 10 Worker (Subject to be evaluated) 11a Biological sensor (First measuring device, Biological information acquisition unit) 11b Wireless type pulse meter (Second measuring device, Biological information acquisition unit) 11c Sensing unit (Third measuring device, Biological information acquisition unit) 22 Evaluation determination unit (Data processing unit)

Claims

1. A step of acquiring biometric information of an evaluated person by a biometric information acquisition unit; A step of calculating an index representing the state of the evaluated person based on the acquired biometric information, There are a plurality of the evaluated persons, Biometric information of each of the plurality of evaluated persons is acquired by any one of three types of biometric information acquisition units that detect data of biometric information of at least a clothing type, a wristwatch type, and a type worn on the earlobe or fingertip, with different frequencies of detecting biometric information data and frequencies of data transmission, In a data processing unit located away from the evaluated person, when the biometric information is heart rate data, it is converted into a central heart rate as a common parameter, and when the biometric information is acceleration data or METs, it is converted into an acceleration deviation or the square root of the squared deviation of the combined acceleration as a common parameter, and the biometric information acquired by two or more different types of the biometric information acquisition units is integrated into these common parameters, and the index is calculated. A biometric information processing method characterized by this.

2. The index is at least one of a work load index indicating the degree of influence of the load received by the evaluated person, a physical condition evaluation index indicating the degree of change from the normal state of the physical condition of the evaluated person, and a heat stroke onset risk index indicating the degree of risk of the evaluated person developing heat stroke. The biometric information processing method according to Claim 1.

3. The biometric information is at least one of heart rate data, acceleration data, and METs. The biometric information processing method according to Claim 1 or 2.

4. The common parameter used for the acceleration data or the METs is an acceleration deviation. The biometric information processing method according to Claim 3.

5. Among at least three types of biological information acquisition units that acquire biological information of each of a plurality of subjects to be evaluated, and that have different frequencies of detecting data of biological information and frequencies of data transmission for each of a clothing type, a wristwatch type, and a type worn on the earlobe or fingertip, two or more different types of biological information acquisition units, and a data processing unit that is located away from the subject to be evaluated and calculates an index representing the state of the subject to be evaluated based on the acquired biological information. The data processing unit converts the biological information acquired by the biological information acquisition unit into a central heart rate as a common parameter when the biological information is heart rate data, and converts the biological information into an acceleration deviation as a common parameter, or the square root of the squared deviation of the combined acceleration when the biological information is acceleration data or METs, and integrates the biological information acquired by two or more different types of the biological information acquisition units into these common parameters to calculate the index. A biological information processing system characterized by this.

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