JP7795329B2 - Determination device, work system, determination method, and determination program - Google Patents

Description

The present invention relates to a determination device, an operation system , a determination method , and a determination program .

Technology for detecting a subject's level of alertness has been proposed in the past. For example, one proposed technology in this technical field involves detecting a subject's brain waves and determining the level of alertness from the detected brain waves (see, for example, Patent Document 1).

Japanese Patent Application Publication No. 8-332871

However, with conventional technology, it remains difficult to ensure accuracy in determining mental and physical states such as alertness, for example, because biological signals such as electroencephalograms vary from person to person.

The present invention has been made in light of the above, and aims to provide a determination device, work system, and determination method that can improve the accuracy of determining mental and physical states.

In order to solve the above-mentioned problems and achieve the objectives, the assessment device of the present invention is a device that assesses the mental and physical state of a person being assessed from the person's biosignals, and the control unit adjusts the assessment criteria for the mental and physical state in accordance with the person's status, which is the situation of the person being assessed, and assesses the mental and physical state of the person being assessed from the person's biosignals using the adjusted criteria.

This invention can improve the accuracy of determining mental and physical states.

FIG. 1 is a diagram showing an overview of a work system. FIG. 2 is a diagram showing an outline of the determination method. FIG. 3 is a diagram showing an outline of the determination method. FIG. 4 is a diagram showing an outline of the determination method. FIG. 5 is a block diagram of the determination device. FIG. 6 illustrates an example of the model storage unit. FIG. 7 illustrates an example of the model storage unit. FIG. 8 illustrates an example of the model storage unit. FIG. 9 is a block diagram of the guide unit. FIG. 10 is a flowchart showing a processing procedure executed by the determination device. FIG. 11 is a flowchart showing a processing procedure executed by the determination device.

Embodiments of the determination device, operation system, and determination method disclosed herein are described in detail below with reference to the accompanying drawings. Note that the present invention is not limited to the embodiments described below.

First, an overview of the determination device, operation system, and determination method according to the embodiment will be described using 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.

As shown in Figure 1, work system 1 is installed in a factory, for example, and utilizes IoT (Internet of Things) to visualize the mental and physical state of workers T working in the factory, thereby improving productivity from the perspective of their mental and physical state.

As shown in FIG. 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 biosignals transmitted from the biosensor 50 of each worker T.

The physical and mental state here refers to the concentration level and alertness level of worker T, but may also refer to, for example, mental state. The biosensor 50 is, for example, an electroencephalograph that measures worker T's brain waves, or various wearable devices that measure pulse rate, body temperature, etc. In the following explanation, for ease of understanding, 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.

The manufacturing system 100 is, for example, a system that controls a factory production line. 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 within a factory. Such a control device may also include, for example, a device that controls lighting and air conditioning within the factory depending on the level of concentration. The control device may also have a function that suggests breaks depending on the level of concentration, for example.

However, 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.

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

For example, even if the estimated concentration level when sitting (hereinafter simply referred to as the estimated value) is the same as the estimated value when standing, the concentration level may actually be high when sitting and low when standing, requiring adjustments to the assessment process.

These differences can vary depending on, for example, the position (posture) of 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, are adjusted depending on the state of worker T.

As shown in FIG. 3, the determination method according to the embodiment first performs data analysis of the biological signal (step S1) and calculates an estimated concentration level. Furthermore, the determination method according to the embodiment determines a range (a range within which the determination target value (here, the estimated value) exists when determining that a certain state exists) based on information about the worker T's body position and information about the illuminance around the worker T (step S2).

In the determination method according to the embodiment, as will be described later in 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 an appropriate state; if the mental and physical state is appropriate, the mental and physical state is considered to be an appropriate state, and if the mental and physical state deviates from the appropriate state, the mental and physical state is considered to be an inappropriate state. Note that an inappropriate state here may be, for example, a state in which rest is required, or a state in which work efficiency is reduced, etc.

Then, in the determination method according to the embodiment, the concentration level of worker T is determined based on the analysis results (i.e., estimated values) from step S1 and the range determined in step S2 (step S3).

As a result, the assessment method according to the embodiment can set an appropriate range depending on the state of the worker T (for example, when sitting or standing) that affects the assessment result, allowing the level of concentration to be assessed with high accuracy.

Next, an example of model generation will be outlined using Figure 4. As shown in Figure 4, in the determination method according to the embodiment, first, a task is instructed to 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 reduces concentration and a task that increases concentration. An example of a task that reduces concentration is deep breathing, and an example of a task that increases concentration is mental arithmetic.

For example, in the determination method according to the embodiment, data analysis is performed on the biosignals obtained when 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, worker T is asked to perform a task multiple times, and the analysis results of the biosignals are used to determine the lower limit threshold of the range.

The example shown in the figure shows the progression of the estimated value when four mind's eye deep breathing sessions are performed, with the third session showing the minimum value Bp at which 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 threshold of the range (step S13). Then, although not shown in the figure, data analysis of the biological signals when worker T performs mental arithmetic, a task that increases concentration, is also performed in the same way as for the mind's eye deep breathing task, and a model is generated using the maximum value as the upper threshold of the range.

In addition, in the determination method according to the embodiment, a model of 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 various body positions and lighting environments.

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 this model in various body positions and under various lighting conditions, it is possible to appropriately determine the appropriate range for each worker T and each environment, and generate an appropriate model.

In other words, while worker T is conscious enough to perform a task, he or she is asked to perform tasks that increase and decrease his or her concentration level several times, thereby varying the level of concentration within the range of consciousness in which the task can be performed to the upper and lower limits. Then, by measuring and analyzing the biosignals at the upper and lower limits of concentration to determine the range, the range of consciousness in which the task can be performed, i.e., the range of estimated concentration levels (estimated from the biosignals) that can be determined to be appropriate, can be appropriately determined according to worker T's characteristics. Then, by similarly generating models for various work environments, it is possible to determine ranges that are suitable for a variety of environments and according to worker T's characteristics. Furthermore, in practice, the range is determined by setting appropriate offsets (set according to the content of the work (importance, required accuracy, level of danger), etc.) to the upper and lower thresholds.

It is preferable to generate the above model when worker T is assigned to the workplace and register the generated model data as new, or to generate and update model data periodically, such as during regular health checkups.

Next, an example configuration of the determination device 10 according to the embodiment will be described using Figure 5. Figure 5 is a block diagram of the determination device 10. As shown in Figure 5, the determination device 10 includes, for example, a communication unit 110, a control unit 120, and a memory unit 130.

The communication unit 110 is a communication module for communicating data with the biosensor 50 and the manufacturing system 100 (see Figure 1) via a specified network.

The storage unit 130 is realized, for example, by a semiconductor memory element such as RAM (Random Access Memory) or flash memory, or a storage device such as a hard disk or optical disk. In the example of Figure 5, the storage unit 130 has a model storage unit 131 and a concentration level information storage unit 132.

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 is described below, a model is generated for each worker T.

Note that the memory unit stores a group of associated data in a continuous sequence as a single record, but the diagram shows it in a format that is easy to understand.

For example, in the example shown in Figure 6, the model storage unit 131 stores information on items such as "pattern," "upper limit," and "lower limit" in association with each other. "Pattern" acts as an identification code, and each piece of corresponding data is stored in association with this "pattern." For example, as shown in Figure 6, "Illuminance: L1," "Position: M1," "Upper limit (range): A1," and "Lower limit (range): B1" are stored in association with "Pattern 1-1."

In this embodiment, the model is generated from measurements of various illuminances and body positions, so one illuminance and one body position is 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 described above, and the "lower limit" indicates the lower threshold of the appropriate range. In other words, this model (data) is used by searching for corresponding patterns using "illuminance" and "body position," and the "upper limit" and "lower limit" of the searched pattern are then used for subsequent processing.

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

For example, if the task requires a high level of concentration, the upper and lower limits are set high. In this case, for example, when creating a model, the upper limit can be raised by having worker T perform a task that increases concentration (for example, complex mental arithmetic), or by offering rewards for completing the task, and the upper and lower limits can be set high (appropriately) by such methods as raising the lower limit in accordance with the increase in the upper limit. In other words, one effective method of 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 and lower limits.

The example in Figure 8 also 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 related to illuminance, and B1 and D1 respectively represent coefficients related to body position. Furthermore, L represents the current illuminance, and M represents the current body position. Note that it is also possible to separately store a common calculation formula, and store different parameter values for each individual used in the calculation formula.

Returning to the example of Figure 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 a predetermined time interval (for example, every 5 minutes), and the calculated concentration level is stored linked to the calculation time. This stored information makes it possible to track the progress of the concentration level of each worker T.

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

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

The biosignal acquisition unit 121 acquires the biosignals of each worker T from the biosensor 50 worn by each worker T. The biosignal acquisition unit 121 also passes the acquired generated signals to the biosignal analysis unit 123.

The image data acquisition unit 122 acquires, for example, image data of each worker T. For example, the image data acquisition unit 122 acquires image data of each worker T from a camera installed on the ceiling of the factory. The image data acquisition unit 122 also passes the acquired image data to the range adjustment unit 124.

The biological signal analysis unit 123 analyzes the biological signals acquired by the biological signal acquisition unit 121, calculates an estimate of 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 and heart rate. Note that the method for estimating the concentration level using brain waves and heart rate is not particularly limited, and any known algorithm may be used.

The range adjustment unit 124 adjusts the judgment thresholds (upper and lower thresholds) that make up the appropriate range depending on the condition of the worker T. For example, the range adjustment unit 124 analyzes the condition 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 through facial recognition processing of the image data, recognizes their body position through body part recognition and body part position recognition processing of the image data, and calculates the illuminance through statistical processing of the brightness of each pixel in the image data. 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.

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

This allows the range to be adjusted to an appropriate range depending on each worker T, their position, the surrounding environment (illumination), and the work they are doing.

The range adjustment unit 124 may estimate the body position of the worker T, for example, 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.

The range adjustment unit 124 then passes information about the adjusted appropriate range to the concentration determination unit 125.

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 for each worker T received from the biosignal analysis unit 123 with the appropriate range for the worker T received from the range adjustment unit 124.

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

Then, this determination result is output from the communication unit 110 so that measures can be taken according to the state of concentration. Specifically, for example, the concentration state (e.g., "Your concentration is declining") and appropriate measures (e.g., "We recommend taking a 10-minute rest") may be notified by display or sound, or a signal to stop or slow down the manufacturing machine may be output.

The guide unit 126 provides guidance to the subject (worker T) regarding actions appropriate for setting the judgment threshold (appropriate range). In other words, the model storage unit 131 stores a model for adjusting the upper and lower thresholds that indicate the appropriate range, 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 or outputting audio guidance on the terminal device used for model generation or on the terminal device of each worker T (e.g., a smartphone, etc.).

Figure 9 is a block diagram of the guide unit 126. As shown in Figure 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 that is appropriate for setting the appropriate range.

Here, the task used to determine the upper threshold of the appropriate range is a task that increases alertness, such as mental arithmetic, and the task used to determine the lower threshold of the appropriate range is a task that decreases alertness, such as deep mind's eye breathing.

For example, the task instructing unit 126a may use video and audio to present the actual task execution method and then instruct the worker T to execute the task. The worker T can execute the task according to the video and audio, and can easily perform the task that is appropriate for the appropriate range setting. The task instructing unit 126a may also instruct the worker T to execute the task multiple times in the same environment.

The task confirmation unit 126b confirms whether the tasks performed by worker T are being performed appropriately. For example, the task confirmation unit 126b analyzes image data of worker T to confirm whether worker T is performing the tasks appropriately.

For example, if the task is mental arithmetic, a UI may be provided on worker T's terminal device to allow the answer to be written, and the task confirmation unit 126b may check whether the task is being performed properly based on the progress of the mental arithmetic via such a UI.

When the task confirmation unit 126b confirms that the worker T is performing the task properly, it acquires the biosignals obtained while the worker is performing the task and passes them to the model generation unit 127. In other words, biosignals obtained when the task is not being performed properly are excluded from the model generation target. This makes it possible to generate a model using appropriate biosignals.

When worker T performs a task, the environment determination unit 126c determines the environment, such as the task content to be performed and the environmental position 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, position, and work conditions in each pattern shown in Figures 6 and 7. Therefore, when performing a task, the environment determination unit 126c generates data to realize these environments and outputs this to the related devices via the task instruction unit 126a. Specifically, based on the content determined by the environment determination unit 126c, operations such as displaying the specified position on a display device, controlling the brightness of a lighting device (sending a brightness instruction signal), and displaying task content appropriate for the target work content on a display device are performed.

In addition, 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.).

For example, if the biosignal has an appropriate value when worker T performs a task in a specific environment, the environment determination unit 126c determines that the task should be performed in that environment.

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

In other words, in this case, the environment determination unit 126c determines the task execution environment for worker T so that the task is performed at home in each body position and at each illuminance level.

Returning to the explanation of Figure 5, the model generation unit 127 will now be described. The model generation unit 127 generates a model. For example, the model generation unit 127 operates in conjunction with the guide unit 126. For example, the guide unit 126 guides the worker T through a task, and the model generation unit 127 generates a model for each body position and each illuminance level based on the biological signals of the worker T while he or she is performing the task.

At this time, the model generation unit 127 determines whether the biosignals being displayed during the task are appropriate (for example, the measured values do not fall within an expected range, or the measured values do not converge to an appropriate value due to fluctuations in the measured values), and generates a model using the biosignals if they are determined to be appropriate. For example, if the biosignals displayed during deep mind's eye breathing do 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" or "repeatedly issue task instructions after a short time has elapsed." 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.

Next, the processing steps executed by the determination device 10 according to the embodiment will be described using Figures 10 and 11. Figures 10 and 11 are flowcharts showing the processing steps executed by the determination device 10 (control unit 120).

First, the processing procedure for determining the concentration level will be described using Figure 10. This processing procedure is triggered, for example, by a manual start operation by the work manager or by a concentration level confirmation signal output at predetermined intervals by the work management device.

As shown in FIG. 10, when the determination device 10 starts the concentration level determination process, it acquires a biosignal from a sensor or the like worn by the worker (step S101), analyzes the biosignal, and calculates an estimated concentration level (step S102).

Next, the determination device 10 identifies the illuminance and 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 uses the estimated concentration level calculated in step S102 and the determination threshold adjusted in step S104 to determine the concentration level of worker T (e.g., whether or not the worker T is suitable for the task) (step S105), and ends the process.

Next, the processing procedure for the model generation process will be explained using Figure 11. This processing procedure is triggered, for example, by a manual start operation (model generation process start operation) by the work manager.

As shown in FIG. 11, the determination device 10 issues task instructions to the worker T and also controls the necessary environment, such as the operation of related devices in the vicinity (brightness control of lighting devices) (step S111). Next, the determination device 10 acquires biosignals from sensors or the like worn by the worker T while the worker T is performing the task.

Next, the determination device 10 determines whether 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 processing in step S113. If the task has not been performed multiple times (step S112; No), the determination device 10 returns to processing in step S111.

Next, the determination device 10 analyzes the biosignal collected in step S112 and calculates 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 determines that it is an appropriate value (step S114; Yes), it determines the maximum and minimum values of the analysis result as the determination thresholds (step S115) and terminates the process.

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 process.

As described above, the determination device 10 according to the embodiment is a determination device that determines the mental and physical state (level of concentration) of worker T (an example of a person to be determined) from the subject's biosignals. The control unit 120 adjusts the determination threshold for the mental and physical state (level of concentration) according to the condition of worker T, and determines the mental and physical state (level of concentration) from the subject's biosignals using the adjusted determination threshold. Therefore, the determination device 10 according to the embodiment can accurately determine the mental and physical state (level of concentration) of subject T.

The above-described embodiment uses concentration as an example of a mental and physical state, but it can also 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.

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

In addition, by using the concentration level of a group, rather than an individual, such as the average concentration level of a group or statistical processing of concentration levels, appropriate measures can be taken according to the physical and mental state of individuals and groups, such as replacing workers in response to a decline 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 decline in a group's concentration level.

Further advantages and modifications may 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.

1 Work system 10 Determination device 50 Biosensor 100 Manufacturing system 121 Biosignal acquisition unit 122 Image data acquisition unit 123 Biosignal analysis unit 124 Range adjustment unit 125 Concentration level determination unit 126 Guide unit 126a Task instruction unit 126b Task confirmation unit 126c Environment determination unit 127 Model generation unit 131 Model storage unit 132 Concentration level information storage unit T Worker (an example of a person to be determined)

Claims (10)

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. The state of the person to be determined is the posture of the person to be determined.
The determination device according to claim 1 . 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. 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. 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 . The control unit
providing guidance regarding the execution of said tasks;
The determination device according to any one of claims 1 to 5 . 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 . 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. 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. A determination program for determining a mental and physical state of a subject based on a biological signal of the subject,
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.