Determination device, work system, determination method, and determination program

Abstract

To improve determination accuracy of a psychosomatic state.SOLUTION: Provided is a determination device that determines a psychosomatic state of a determination subject from a biological signal of the determination subject according to an embodiment, in which a control unit adjusts determination criteria of the psychosomatic state according to a determination subject status that is a status of the determination subject, and determines the psychosomatic state from the biological signal of the determination subject by using the adjusted determination criteria.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a determination device and an operation system. 、 Judgment method and Judgment Program Regarding. [Background technology]

[0002] Conventionally, there are techniques for detecting the level of arousal of a subject. In this technical field, for example, a technique has been proposed in which brain waves of a subject are detected and the level of arousal is detected from the detected brain waves (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 8-332871 Summary of the Invention [Problem to be solved by the invention]

[0004] However, with conventional technology, it is still difficult to ensure accuracy in determining mental and physical states such as arousal levels, because biological signals such as electroencephalograms vary from person to person.

[0005] The present invention has been made in view of the above, and has an object to provide a determination device, a work system, and a determination method that can improve the accuracy of determining mental and physical states. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the objectives, the judgment device of the present invention is a judgment device that judges the mental and physical state of a person to be judged from the person's biological signals, and the control unit adjusts the judgment criteria for the mental and physical state according to the person's situation, which is the situation of the person to be judged, and judges the mental and physical state of the person to be judged from the person's biological signals using the adjusted judgment criteria. [Effects of the Invention]

[0007] According to the present invention, it is possible to improve the accuracy of determining mental and physical states. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an overview of the work system. [Figure 2] FIG. 2 is a diagram showing an outline of the determination method. [Figure 3] FIG. 3 is a diagram showing an outline of the determination method. [Figure 4] FIG. 4 is a diagram showing an outline of the determination method. [Figure 5] FIG. 5 is a block diagram of the determination device. [Figure 6] FIG. 6 is a diagram illustrating an example of the model storage unit. [Figure 7] FIG. 7 is a diagram illustrating an example of the model storage unit. [Figure 8] FIG. 8 illustrates an example of the model storage unit. [Figure 9] FIG. 9 is a block diagram of the guide unit. [Figure 10] FIG. 10 is a flowchart showing the processing procedure executed by the determination device. [Figure 11] FIG. 11 is a flowchart showing a processing procedure executed by the determination device. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of a determination device, an operation system, and a determination method disclosed in the present application will be described in detail with reference to the accompanying drawings. Note that the present invention is not limited to the following embodiments.

[0010] First, an overview of a determination device, an operation system, and a determination method according to an embodiment will be described with reference to Figures 1 to 4. Figure 1 is a diagram showing an overview of the operation system. Figures 2 to 4 are diagrams showing an overview of the determination method.

[0011] As shown in Figure 1, the work system 1 is installed in a factory, for example, and by utilizing IoT (Internet of Things), it visualizes the mental and physical state of workers T working in the factory, thereby improving productivity from the perspective of their mental and physical state.

[0012] 1, the work system 1 includes a determination device 10, a biosensor 50, and a manufacturing system 100. The determination device 10 analyzes the physical and mental state of each worker T by analyzing the biosignal transmitted from the biosensor 50 of each worker T.

[0013] The physical and mental state here refers to the concentration level or alertness level of the worker T, but may also be, for example, a mental state. The biosensor 50 is, for example, an electroencephalograph that measures the brain waves of the worker T, or various wearable devices that measure the pulse rate, body temperature, etc. In the following explanation, for ease of understanding, the concentration level will be used as an example of the physical and mental state, but the same can be applied to other physical and mental states such as alertness and drowsiness.

[0014] The manufacturing system 100 is, for example, a system that controls a production line in a factory. For example, the manufacturing system 100 adjusts the speed of the production line or stops it depending on the determination result of the determination device 10. The manufacturing system 100 corresponds to an example of a control device that controls equipment in a factory. Furthermore, such a control device may include, for example, a device that controls lighting and air conditioning in the factory depending on the level of concentration. Furthermore, the control device may have a function that suggests taking a break depending on the level of concentration, for example.

[0015] Incidentally, when determining the concentration level of each worker T from the biosignal of the biosensor 50, there will be individual differences in the relationship between the biosignal and the concentration level for each worker T. For this reason, it is necessary to determine in advance the relationship between the biosignal and the concentration level for each worker T.

[0016] Furthermore, as shown in Figure 2, even if the worker T is the same person, it becomes necessary to adjust the concentration determination threshold when the worker T works while sitting (corresponding to the sitting position in Figure 2) and when the worker T works while standing (corresponding to the standing position in Figure 2).

[0017] For example, even if the estimated value of concentration level when sitting (hereinafter simply referred to as the estimated value) is the same as the estimated value when standing, in reality, the state of concentration may be high when sitting and low when standing, and adjustments to the judgment process may be necessary.

[0018] These differences may vary depending on, for example, the position (posture) of the worker T or the illuminance. Therefore, in the determination method according to the embodiment, the criteria for determining the concentration level, in this case the determination threshold, is adjusted according to the state of the worker T.

[0019] 3, in the determination method according to the embodiment, first, data analysis of the biological signal is performed (step S1) to calculate an estimated value of the concentration level. In addition, in the determination method according to the embodiment, a range (a range in which a determination target value (here, an estimated value) exists when determining that a certain state exists) is determined from information on the body position of the worker T and information on illuminance around the worker T (step S2).

[0020] In the determination method according to the embodiment, as will be described later with reference to Fig. 4, a model is generated in which the relationship between body position information, illuminance information, and range is determined in advance, and the range is determined using this model. Note that the range here refers to an appropriate range within which the mental and physical state of worker T is considered to be appropriate; if the mental and physical state is appropriate, the mental and physical state is considered to be appropriate, and if the mental and physical state deviates from the appropriate state, the mental and physical state is considered to be inappropriate. Note that an inappropriate state here may be, for example, a state in which rest is required, a state in which work efficiency is reduced, etc.

[0021] Thereafter, in the determination method according to the embodiment, the concentration level of the worker T is determined based on the analysis result (that is, the estimated value) in step S1 and the range determined in step S2 (step S3).

[0022] As a result, in the determination method of the embodiment, the range can be appropriately set depending on the state of the worker T (for example, when sitting or standing) that affects the determination result, so that the concentration level can be determined accurately.

[0023] Next, an overview of an example of generating a model will be described with reference to Fig. 4. As shown in Fig. 4, first, in the determination method according to the embodiment, a task is instructed to the worker T by display or voice (step S11). Here, a task is an action suitable for setting a range, and includes, for example, a task that decreases the level of concentration and a task that increases the level of concentration. An example of a task that decreases the level of concentration is deep breathing using one's mind's eye, and an example of a task that increases the level of concentration is mental arithmetic.

[0024] For example, in the determination method according to the embodiment, data analysis is performed on the biological signals when the worker T performs deep eye breathing in a predetermined body position and illuminance (step S12), and the analysis results are stored. For example, in the determination method according to the embodiment, the worker T is asked to perform a task multiple times, and the analysis results of the biological signals are used to determine the lower limit threshold of the range.

[0025] The example shown in the figure shows the transition of the estimated value when a total of four mind's eye deep breathing sessions are performed, and the estimated value reaches the minimum value Bp at the third session, at which point the estimated value decreases the most. Therefore, in the determination method according to the embodiment, a model is generated using the minimum value Bp as the lower limit threshold of the range (step S13). Then, although not shown, data analysis of the biological signals when the worker T performs mental arithmetic, a task that increases concentration, is also performed in the same way as in the mind's eye deep breathing task, and a model is generated using the maximum value as the upper limit threshold of the range.

[0026] In addition, in the determination method according to the embodiment, a model of the worker T is generated by performing a series of processes from the task instruction in step S11 to the model generation in step S13 in each body position and in each illumination environment.

[0027] In this way, a model is generated by determining the lower threshold based on the biosignal when performing mind's eye deep breathing and the upper threshold based on the biosignal when performing mental arithmetic.By generating such a model for each body position and in each lighting environment, it is possible to appropriately determine the appropriate range for each worker T and each environment and generate an appropriate model.

[0028] In other words, while worker T is conscious enough to perform a task, the concentration level is varied between the upper and lower limits by having him / her perform tasks that increase and decrease his / her concentration level several times. Then, by measuring and analyzing the biosignals at the upper and lower limits of the concentration level to determine the range, the concentration level within which the task can be performed can be appropriately determined according to the characteristics of worker T. Then, by generating similar models for various work environments, it is possible to determine a range that is suitable for a variety of environments and according to the characteristics of worker T. In practice, the range is determined by setting appropriate offsets (set according to the content of the work (importance, required accuracy, risk, etc.)) to the upper and lower thresholds.

[0029] It is preferable that the above-described model generation is performed when worker T is assigned to the workplace and the generated model data is newly registered, or that model data is generated and updated periodically, for example, during regular health checkups.

[0030] Next, a configuration example of the determination device 10 according to the embodiment will be described with reference to Fig. 5. Fig. 5 is a block diagram of the determination device 10. As shown in Fig. 5, the determination device 10 includes, for example, a communication unit 110, a control unit 120, and a storage unit 130.

[0031] The communication unit 110 is a communication module for performing data communication with the biosensor 50 and the manufacturing system 100 (see FIG. 1) via a predetermined network.

[0032] The storage unit 130 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. In the example of FIG. 5, the storage unit 130 has a model storage unit 131 and a concentration level information storage unit 132.

[0033] The model storage unit 131 is a storage unit that stores a model for adjusting the upper and lower thresholds that indicate the appropriate range. Figures 6 to 8 are diagrams showing an example of the model storage unit 131. Note that, although a model for one worker T will be described below, it is assumed that a model is generated for each worker T.

[0034] Although a group of associated data is stored in the memory unit in a continuous array as one record, the diagram shows it in a format that is easy to understand.

[0035] For example, in the example shown in Fig. 6, the model storage unit 131 stores information on items such as "pattern," "upper limit," and "lower limit" in association with one another. The "pattern" functions as an identification code, and each piece of corresponding data is stored in association with this "pattern." For example, as shown in Fig. 6, "illuminance: L1," "position: M1," "upper limit (range): A1," and "lower limit (range): B1" are stored in association with "pattern 1-1."

[0036] In this embodiment, the model is generated from measurements of various illuminances and body positions, so one illuminance and one body position are treated as one pattern, and an "upper limit" and a "lower limit" are set for each pattern. The "upper limit" indicates the upper threshold of the appropriate range, and the "lower limit" indicates the lower threshold of the appropriate range. In other words, this model (data) is used in such a way that a corresponding pattern is searched for using "illuminance" and "body position," and the "upper limit" and "lower limit" of the searched pattern are used for subsequent processing.

[0037] 7 shows a case where an operation is further added as a parameter to each pattern of illuminance and body position. Here, the operation refers to the operation content performed by the worker T. That is, the example of FIG. 7 focuses on the fact that the required concentration level differs depending on the operation content, and shows an example where a model in which "upper limits" and "lower limits" are determined for various "illuminance," "body position," and "operation content" is stored in the model storage unit 131.

[0038] For example, if the task requires a high level of concentration, the upper and lower limits are set to be high. In this case, for example, when creating a model, the task that worker T is to perform to increase the level of concentration is assigned a task that increases the level of concentration (e.g., complex mental arithmetic), or rewards are given for completing the task, thereby raising the "upper limit." The upper and lower limits are then raised (appropriately) by such methods as raising the "lower limit" in accordance with the increase in the "upper limit." In other words, one effective method for creating a model is to have the worker perform a task that corresponds to the level of concentration (level of concentration) required for the target task, measure and analyze biosignals, and then set the "upper limit" and "lower limit."

[0039] 8 shows an example in which a model in which the upper and lower limits are mathematically expressed is stored in the model storage unit 131. For example, A1 and C1 respectively represent coefficients relating to illuminance, and B1 and D1 respectively represent coefficients relating to body position. Furthermore, L represents the current illuminance, and M represents the current body position. Note that it is also possible to apply a format in which a common calculation formula is separately stored, and different parameter values ​​for each individual used in the calculation formula are stored.

[0040] Returning to the example of FIG. 5, the concentration level information storage unit 132 will be described. The concentration level information storage unit 132 is a storage unit that stores the determination results regarding the concentration level of each worker T. For example, the concentration level information storage unit 132 stores the concentration level of each worker T linked to the concentration level calculation time. For example, the concentration level is calculated at predetermined time intervals (for example, five-minute intervals), and the calculated concentration level is stored linked to the calculation time. This stored information makes it possible to grasp the transition of the concentration level of each worker T.

[0041] Next, a description will be given of the control unit 120. The control unit 120 is a controller, and is realized, for example, by a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) executing various programs (not shown) stored in the storage unit 130 using RAM as a work area. The control unit 120 can also be realized, for example, by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0042] In the example shown in FIG. 5, the control unit 120 includes a biological signal acquisition unit 121, an image data acquisition unit 122, a biological signal analysis unit 123, a range adjustment unit 124, a concentration level determination unit 125, a guide unit 126, and a model generation unit 127.

[0043] The biological signal acquisition unit 121 acquires the biological signal of each worker T from the biological sensor 50 attached to each worker T. The biological signal acquisition unit 121 also passes the acquired generated signal to the biological signal analysis unit 123.

[0044] The image data acquisition unit 122 acquires, for example, image data obtained by photographing each worker T. For example, the image data acquisition unit 122 acquires image data obtained by photographing each worker T from a camera installed on the ceiling of the factory. In addition, the image data acquisition unit 122 passes the acquired image data to the range adjustment unit 124.

[0045] The biological signal analysis unit 123 analyzes the biological signals acquired by the biological signal acquisition unit 121, calculates an estimated value related to the concentration level, and passes the analysis result to the concentration level determination unit 125. For example, the biological signal analysis unit 123 estimates the concentration level using brain waves or heartbeats. Note that the method for estimating the concentration level using brain waves or heartbeats is not particularly limited, and any known algorithm may be used.

[0046] The range adjustment unit 124 adjusts the judgment thresholds (upper and lower thresholds) that constitute the appropriate range depending on the state of the worker T. For example, the range adjustment unit 124 analyzes the state of the worker T by performing various image analysis processes on the image data acquired by the image data acquisition unit 122. For example, the range adjustment unit 124 recognizes each worker T by face recognition processing or the like of the image data, recognizes the body position by body part recognition processing and body part position recognition processing or the like of the image data, and calculates the illuminance by statistical processing of the brightness of each pixel in the image data. Note that the work content and type are recognized by image recognition of the image data (recognition of image elements related to the work in the image) or by acquiring work content and type data from a separately installed work management device.

[0047] Then, the range adjustment unit 124 applies the recognized and calculated worker, body position, worker ambient illuminance, and work content (if the model shown in FIG. 7 has work parameters) to the model stored in the model storage unit 131, and adjusts the range to an appropriate range.

[0048] This allows the range to be adjusted to an appropriate range depending on each worker T, their body position, the surrounding environment (illuminance), and the type of work.

[0049] The range adjustment unit 124 may estimate the body position of the worker T, for example, by using the value of a wearable G sensor worn by the worker T, and may obtain information regarding illuminance from lighting equipment in the factory.

[0050] Then, the range adjustment unit 124 passes information about the adjusted appropriate range to the concentration level determination unit 125.

[0051] The concentration level determination unit 125 determines the concentration level of each worker T (whether the concentration level is within a concentration level range appropriate for the task). Specifically, the concentration level determination unit 125 determines the concentration level of each worker T by comparing the estimated concentration level of each worker T received from the biological signal analysis unit 123 with the appropriate range for the worker T received from the range adjustment unit 124.

[0052] For example, the concentration level determination unit 125 determines the concentration level state of the worker T from the relative relationship between the appropriate range and the estimated concentration level value (whether the estimated concentration level value is within the appropriate range), and stores the determination result in the concentration level information storage unit 132. For example, the concentration level state is determined to be inappropriate when the concentration level (estimated value) is below 3, with an appropriate range (full range: 0 to 10) being a concentration level (estimated value) of 3 to 8.

[0053] The result of this determination is then output from the communication unit 110 for taking measures according to the state of concentration. Specifically, for example, the state of concentration (e.g., "Your concentration is declining") and guidance on appropriate measures (e.g., "We recommend you take a 10-minute rest") may be notified by display or sound, or a stop instruction signal or a speed reduction instruction signal may be output to the manufacturing machine.

[0054] The guide unit 126 provides guidance to the subject (worker T) regarding actions appropriate for setting the judgment threshold (appropriate range). That is, a model for adjusting the upper and lower thresholds indicating the appropriate range is stored in the model storage unit 131, and the guide unit 126 provides guidance on actions to be performed by the model subject when generating this model. For example, the guide unit 126 provides guidance by displaying and outputting audio guidance on a terminal device for generating the model or on a terminal device (e.g., a smartphone) of each worker T.

[0055] Fig. 9 is a block diagram of the guide unit 126. As shown in Fig. 9, the guide unit 126 has a task instructing unit 126a, a task checking unit 126b, and an environment determining unit 126c. The task instructing unit 126a instructs the worker T to perform a task suitable for setting the appropriate range.

[0056] Here, the task for determining the upper threshold of the appropriate range is, for example, a task that increases alertness, such as mental arithmetic, and the task for determining the lower threshold of the appropriate range is, for example, a task that decreases alertness, such as deep mind's eye breathing.

[0057] For example, the task instructing unit 126a presents an actual task execution method using video and audio, and then instructs the worker T to execute the task. The worker T can execute the task according to the video and audio, and can easily execute the task that is suited to the setting of the appropriate range. Furthermore, the task instructing unit 126a instructs the worker T to execute the task multiple times in the same environment.

[0058] The task confirmation unit 126b confirms whether the task executed by the worker T is being performed properly. For example, the task confirmation unit 126b analyzes image data of the worker T to confirm whether the worker T is performing the task properly.

[0059] For example, if the task is mental arithmetic, a UI may be provided to allow worker T to write the answer on his / her terminal device, and task confirmation unit 126b may check whether the task is being performed properly based on the progress of the mental arithmetic via such UI.

[0060] When the task confirmation unit 126b confirms that the worker T is performing the task properly, it acquires the biological signals obtained while the worker is performing the task and passes them to the model generation unit 127. In other words, the biological signals obtained when the task is not performed properly are excluded from the targets for generating a model. This allows a model to be generated using appropriate biological signals.

[0061] The environment determination unit 126c determines the environment, such as the task content to be performed by the worker T and the environmental posture of the worker when performing the task. In other words, it is necessary to generate a model based on biological signals in an environment according to the illuminance, posture, and task conditions in each pattern shown in Figures 6 and 7. Therefore, when performing a task, the environment determination unit 126c generates data for realizing these environments and outputs them to related devices via the task instruction unit 126a. Specifically, based on the content determined by the environment determination unit 126c, operations such as displaying a specified posture on a display device, controlling the brightness of a lighting device (sending a brightness instruction signal), and displaying task content adapted to the target work content on a display device are performed.

[0062] Additionally, the environment determination unit 126c determines a response (environment change) when worker T performs a task but the biosignal does not take an appropriate value (the measured value does not fall within the expected range, the measured value fluctuations do not subside and the measured value does not converge to an appropriate value, etc.).

[0063] For example, when the worker T performs a task in a specific environment, if the biological signal has an appropriate value, the environment determining unit 126c determines that the task should be performed in that environment.

[0064] To give a more specific example, in a stressful environment such as the workplace, even if you perform mind's eye deep breathing, you may not be able to relax sufficiently and the lower threshold value may not be determined. On the other hand, if you perform mind's eye deep breathing at home, you will be able to relax sufficiently and the lower threshold value may be determined appropriately.

[0065] That is, in this case, the environment determination unit 126c determines the task execution environment for the worker T so that the worker T performs the task at home in each body position and at each illuminance.

[0066] Returning to the description of Fig. 5, the model generation unit 127 will be described. The model generation unit 127 generates a model. For example, the model generation unit 127 operates in cooperation with the guide unit 126. For example, the guide unit 126 guides the worker T in a task, and the model generation unit 127 generates a model of each body position and each illuminance based on a biological signal of the worker T while he or she is performing the task.

[0067] At this time, the model generation unit 127 determines whether the biological signal during the task is appropriate (for example, if the measured value does not fall within an expected range, if the fluctuation of the measured value does not subside and the measured value does not converge to an appropriate value), and generates a model using the biological signal if it is determined to be appropriate. For example, if the biological signal during mind's eye deep breathing does not indicate an appropriate value as the lower threshold, the model generation unit 127 instructs the guide unit 126 to provide guidance such as "repeatedly issue task instructions to continue" or "wait a short time and then issue task instructions to continue." At this time, the model generation unit 127 may also instruct the guide unit 126 to change the environment in which the task is performed.

[0068] Next, a process procedure executed by the determination device 10 according to the embodiment will be described with reference to Fig. 10 and Fig. 11. Fig. 10 and Fig. 11 are flowcharts showing the process procedure executed by the determination device 10 (control unit 120).

[0069] First, the processing procedure for determining the concentration level will be described with reference to Fig. 10. This processing procedure is triggered, for example, by a manual start operation by the work manager or a concentration level confirmation signal output at predetermined intervals by the work management device.

[0070] As shown in FIG. 10, when the determination device 10 starts the process of determining the concentration level, it acquires a biological signal from a sensor or the like attached to the worker (step S101), analyzes the biological signal, and calculates an estimated concentration level (step S102).

[0071] Next, the determination device 10 identifies the illuminance and the body position (step S103), and adjusts the determination threshold (appropriate range) based on the data stored in the model storage unit 131 shown in Figures 6 and 7 (step S104). Next, the determination device 10 determines the state of the concentration level of the worker T (for example, whether or not the worker T is suited to the task) using the estimated concentration level calculated in step S102 and the determination threshold adjusted in step S104 (step S105), and ends the process.

[0072] Next, the processing procedure of the model generation processing will be described with reference to Fig. 11. This processing procedure is triggered by, for example, a manual start operation (model generation processing start operation) by the work manager.

[0073] 11, the determination device 10 issues task instructions to the worker T and also controls the necessary environment, for example, controlling the operation of related devices in the vicinity (brightness control of lighting devices) (step S111). Next, the determination device 10 acquires a biological signal from a sensor or the like attached to the worker T while the worker T is performing the task.

[0074] Next, the determination device 10 determines whether the worker T has performed the task a predetermined number of times (a number of times sufficient to generate a preset model) (step S112), and if the task has been performed a predetermined number of times (step S112; Yes), the determination device 10 proceeds to the processing of step S113. If the task has not been performed a plurality of times (step S112; No), the determination device 10 returns to the processing of step S111.

[0075] Next, the determination device 10 analyzes the biological signals collected in step S112 to calculate an estimated concentration level (step S113). Then, the determination device 10 determines whether the analysis result (estimated concentration level) is an appropriate value (step S114), and if it is determined that the value is appropriate (step S114; Yes), it determines the maximum and minimum values ​​of the analysis result as determination thresholds (step S115), and ends the process.

[0076] Furthermore, if the determination device 10 determines in step S114 that the value is not appropriate (step S114; No), it issues an environment change instruction (an instruction to perform the model generation process again after changing the environment) (step S116) and terminates the processing.

[0077] As described above, the determination device 10 according to the embodiment is a determination device that determines the mental and physical state (concentration level) of a worker T (an example of a person to be determined) from a biological signal of the subject, and the control unit 120 adjusts a determination threshold for the mental and physical state (concentration level) according to the condition of the worker T, and determines the mental and physical state (concentration level) from the biological signal of the subject using the adjusted determination threshold. Therefore, the determination device 10 according to the embodiment can accurately determine the mental and physical state (concentration level) of the subject T.

[0078] The above-described embodiment uses concentration as an example of a mental and physical state, but it can be applied to other mental and physical states, such as alertness, drowsiness, excitement, calmness, and fatigue, and is particularly useful for mental and physical states that have strong mental elements and cannot be measured directly by a sensor but must be estimated from a sensor signal.

[0079] In the above-described embodiment, the working system 1 is installed in a factory, but the present invention is not limited to this. For example, the working system 1 may be applied to an automobile. In this case, the driver is the target, and a response such as switching between manual driving and automatic driving can be implemented depending on the driver's physical and mental state.

[0080] Furthermore, by using the concentration level of a group, rather than the concentration level of an individual, for example, the average concentration level of a group or statistical processing knowledge of concentration levels, appropriate measures can be taken according to the physical and mental state of the individual and group, such as replacing the worker in response to a decrease in an individual's concentration level, or stopping the production line in charge of the group and allowing the entire group to rest in response to a decrease in the group's concentration level.

[0081] Further advantages and modifications will readily occur to those skilled in the art. Therefore, the invention in its broader aspects is not limited to the specific details and representative embodiments shown and described above. Accordingly, various modifications may be made without departing from the spirit or scope of the general inventive concept as defined by the appended claims and their equivalents. [Explanation of symbols]

[0082] 1. Work System 10 Judgment device 50 Biometric Sensor 100 Manufacturing Systems 121 Biosignal Acquisition Unit 122 Image data acquisition unit 123 Biosignal Analysis Unit 124 Range adjustment section 125 Concentration level determination section 126 Guide part 126a Task instruction section 126b Task confirmation section 126c Environmental Determination Department 127 Model Generation Unit 131 Model Memory Unit 132 Concentration information storage unit T Worker (an example of a person to be judged)

Claims

1. A determination device for determining a mental and physical state of a subject based on a biological signal of the subject, comprising: The control unit The criteria for assessing the physical and mental condition are adjusted according to the situation of the person being assessed, adjusting the judgment criteria based on a biological signal of the person to be judged after the person to be judged performs a task corresponding to the mental and physical state of the person to be judged; Using the adjusted judgment criteria, the mental and physical state of the person to be judged is judged from the biological signals of the person to be judged. Judgment device.

2. The state of the person to be determined is the posture of the person to be determined. The determination device according to claim 1 .

3. The situation of the person to be determined is the surrounding environment of the person to be determined. The determination device according to claim 1 or 2.

4. The status of the person to be evaluated is the work content performed by the person to be evaluated. The determination device according to claim 1, 2 or 3.

5. The judgment criterion is a threshold value for judging a physical and mental state, The control unit determining the threshold value based on a biological signal of the subject after the subject performs a task that changes the subject's mental and physical state in a predetermined direction; The determination device according to any one of claims 1 to 4.

6. The control unit providing guidance regarding the execution of said tasks; The determination device according to any one of claims 1 to 5.

7. The control unit determining whether the behavior of the person to be judged relative to the guide is appropriate based on an image of the person to be judged; adjusting the determination criteria based on the biological signal when the behavior of the person to be determined is appropriate; The determination device according to claim 6.

8. a work device on which a worker performs work; a determination device for determining the physical and mental state of the worker from a biological signal of the worker at the working device; A working system comprising: The determination device The criteria for assessing the physical and mental condition are adjusted according to the worker's situation, adjusting the judgment criteria based on a biological signal of the person to be judged after the person to be judged performs a task corresponding to the mental and physical state of the person to be judged; Using the adjusted judgment criteria, the mental and physical state of the worker is judged from the biological signals; The working device is changing the action according to the mental and physical state of the worker in accordance with the mental and physical state determination result of the determination device; Working system.

9. A method for determining a mental and physical state of a subject based on a biological signal of the subject, comprising: The criteria for assessing the physical and mental condition are adjusted according to the situation of the person being assessed, adjusting the judgment criteria based on a biological signal of the person to be judged after the person to be judged performs a task corresponding to the mental and physical state of the person to be judged; Using the adjusted judgment criteria, the mental and physical state of the person to be judged is judged from the biological signals of the person to be judged. The decision method that the controller will perform.

10. A determination program for determining the mental and physical state of a subject based on the subject's biological signals, comprising: a step of adjusting the criteria for judging the mental and physical state according to the situation of the person to be judged; adjusting the judgment criteria based on a biological signal of the person to be judged after the person to be judged performs a task corresponding to the mental and physical state of the person to be judged; a step of determining a mental and physical state of a subject from a biological signal using the adjusted determination criteria; A judgment program that causes a computer to execute the above.

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