JP2022130199A - Operator support system, operator support method, and program - Google Patents

Abstract

To provide an operator support system capable of supporting a user so that the user can execute work appropriately.SOLUTION: An operator support system 1000 includes: an output unit 213 for outputting a task and notification information to a user 100; a brain wave measuring unit 201 for measuring a brain wave of the user 100; a brain wave measuring unit 202 for measuring a brain wave of the user; and a control unit 200. The control unit 200 estimates a selective attention level of the user on the basis of the brain wave measured by the brain wave measuring unit 201 in an object period, which is at least part of a predetermined period from the output of the task, estimates a mental agitation level of the user on the basis of the brain wave measured by the brain wave measuring unit 202 in the object period, and causes the output unit 213 to output the notification information on the basis of the estimated selective attention level and mental agitation level.SELECTED DRAWING: Figure 3

Description

The present invention relates to a worker support system, a worker support method, and a program for causing a computer to execute the worker support method.

Conventionally, there are systems that support workers so that they can work properly. For example, Patent Literature 1 discloses a worker support system capable of performing accurate work support according to the skill of a worker in maintenance and inspection work for equipment.

JP 2019-067050 A

However, in the technique described in Patent Document 1, whether or not the worker was able to perform the work appropriately is determined based on the working time, so the physical or mental state of the worker is not taken into account. Therefore, it is difficult to say that the worker is supported so that the worker can perform the work appropriately.

The present invention provides a worker support system and the like that can support a user so that the user can perform work appropriately.

A worker support system according to an aspect of the present invention includes an output unit that outputs task and notification information to a user, an electroencephalogram measurement unit that measures the electroencephalogram of the user, and a pulse wave measurement unit that measures the pulse wave of the user. and a control unit, wherein the control unit measures the user's activity based on the electroencephalogram measured by the electroencephalogram measurement unit during a target period, which is at least a part of a predetermined period after the task is output. estimating the degree of selective attention, estimating the degree of mental agitation of the user based on the pulse wave measured by the pulse wave measuring unit during the target period, the estimated degree of selective attention, and The notification information is output to the output unit based on the degree of mental agitation.

A worker support method according to an aspect of the present invention includes a first output step of outputting a task to be performed to a user, an electroencephalogram measurement step of measuring an electroencephalogram of the user, and a pulse wave of the user. A pulse wave measurement step and a control step, wherein the control step is based on the electroencephalogram measured by the electroencephalogram measurement step during a target period that is at least part of a predetermined period after the task is output, A first estimating step of estimating the user's selective attention level; and a second estimating step of estimating the user's mental agitation level based on the pulse wave measured by the pulse wave measuring step during the target period. and a second outputting step of outputting notification information based on the estimated selective attention level and the mental agitation level.

A program according to an aspect of the present invention is a program for causing a computer to execute the worker support method.

A worker support system or the like according to an aspect of the present invention can support a user so that the user can perform work appropriately.

FIG. 1 is a diagram showing a schematic configuration of a worker support system according to Embodiment 1. FIG. FIG. 2 is a diagram showing the hardware configuration of the worker support system according to Embodiment 1. As shown in FIG. FIG. 3 is a block diagram showing the functional configuration of the worker support system according to Embodiment 1. As shown in FIG. FIG. 4 is a time chart of operations of the worker support system according to the first embodiment. FIG. 5 is a diagram showing an example of temporal transition of indices of a user's selective attention level and mental agitation level. FIG. 6 shows an example of a two-axis visualization of selective attention and agitation estimates. FIG. 7 is a flow chart of operations from the output of a task to the visualization of the results of estimating the user's selective attention level and mental agitation level. FIG. 8 is a diagram showing four states of a user according to Embodiment 1. FIG. FIG. 9 is a flowchart of user state determination processing according to the first embodiment. FIG. 10 is a diagram showing an example of an input screen for wearing feeling information. FIG. 11 is a flowchart of work stagnation degree determination processing. FIG. 12 is a flow chart showing the procedure of tasks performed by the user. FIG. 13 is a diagram showing an example of a display screen. FIG. 14 is a diagram showing the relationship between the state of the user determined by the state determining unit, the display of the response display column, and the display of the task display column. FIG. 15 is a block diagram showing a functional configuration of a worker support system according to Embodiment 2. FIG. FIG. 16 is a time chart of operations of the worker support system according to the second embodiment. FIG. 17 is a diagram showing temporal transitions of the user's selective attention level, mental agitation level, and head movement. FIG. 18 is a flowchart of user state determination processing according to the second embodiment. FIG. 19 is a diagram showing four states of a user according to Embodiment 2. FIG. FIG. 20 is a diagram showing a schematic configuration of a worker support system according to Embodiment 3. As shown in FIG.

Hereinafter, embodiments will be specifically described with reference to the drawings. It should be noted that the embodiments described below are all comprehensive or show specific examples. Numerical values, shapes, materials, components, arrangement positions and connection forms of components, steps, order of steps, and the like shown in the following embodiments are examples and are not intended to limit the present invention. Further, among the constituent elements in the following embodiments, constituent elements not described in independent claims will be described as optional constituent elements.

Each figure is a schematic diagram and is not necessarily strictly illustrated. Moreover, in each figure, the same code|symbol is attached|subjected with respect to substantially the same structure, and the overlapping description may be abbreviate|omitted or simplified.

(Embodiment 1)
[Overview]
First, an overview of the worker support system according to Embodiment 1 will be described. FIG. 1 is a diagram showing a schematic configuration of a worker support system 1000 according to Embodiment 1. As shown in FIG.

The worker support system 1000 determines the physical and mental conditions of the user 100 based on the user's 100 selective attention level and mental agitation level, and prompts the user 100 to take a break at an appropriate timing, thereby allowing the user 100 to properly It is a system that assists the user 100 to perform work. Note that selective attention refers to selectively paying attention to specific information among multiple pieces of information. Here, the degree of selective attention indicates, for example, when the user processes a plurality of pieces of information, whether he or she selectively pays attention to necessary information among those pieces of information. Therefore, for example, when the degree of selective attention is low, the user has a low degree of attention to the information necessary for executing the task (the amount of attention is small), so there is a possibility of making a mistake or hindering the work. means high. At this time, the user pays attention to information other than the information necessary for executing the task, for example, the user is concerned about the surrounding sounds or the feeling of wearing the bioinstrumentation device 102 . A task is, in other words, work (office processing, information collection, report creation, brainstorming, meetings, meetings, etc.) to be performed at a fixed time.

The worker support system 1000 specifically includes a terminal device 101 and two bioinstruments 102 .

The terminal device 101 is, for example, a portable information terminal such as a smart phone or a tablet terminal, but may be a stationary information terminal such as a personal computer or a smart speaker.

The bioinstrumentation device 102 is, for example, a device worn by the user 100 to measure the user's electroencephalogram and pulse wave. The bioinstrumentation device 102 is not particularly limited as long as it can measure the brain wave and pulse wave of the user, for example. Alternatively, it may be a headgear type device or the like. Since even one bioinstrumentation device 102 can measure the electroencephalogram and pulse wave of the user 100, the worker support system 1000 may include only one bioinstrumentation device 102 (in FIG. 2 described later, one only shown). The details of the configuration of the bioinstrumentation device 102 will be described later.

The user 100 holds the terminal device 101 with the bioinstrumentation device 102 attached to each ear. The terminal device 101 outputs the task (hereinafter also referred to as presenting the task). The bioinstrumentation device 102 measures the electroencephalogram and pulse wave of the user 100 who confirmed the task output by the terminal device 101 and transmits the measured electroencephalogram and pulse wave to the terminal device 101 .

When the terminal device 101 acquires the electroencephalogram and pulse wave of the user 100 from the bioinstrumentation device 102, the terminal device 101 estimates the user's selective alertness and mental agitation based on the acquired electroencephalogram and pulse wave. On the other hand, the user 100 inputs the wearing feeling information of the bioinstrumentation device 102 by operating the terminal device 101 . The wearing feeling information is, for example, information based on the contents of the user's 100 answer to a questionnaire regarding the wearing feeling of the bioinstrumentation device 102 . For example, the wearing feeling information is information indicating a set of a question and an answer of the user 100 to the question.

Note that the selective caution degree estimation unit 204 estimates the selective caution degree of the user 100 means that the selective caution degree estimation unit 204 calculates the index of the selective caution degree of the user 100 . Further, the mental agitation degree estimation unit 205 estimating the mental agitation degree of the user 100 means that the mental agitation degree estimation unit 205 calculates the index of the mental agitation degree of the user 100 . The selective attention level index, the mental agitation level index, and the method of calculating these indexes (in other words, the method of estimating the selective attention level and the mental agitation level) will be described later.

The user 100 uses an information terminal (not shown) such as a personal computer, for example, to execute a task. There may be.

[Hardware configuration]
Next, a more detailed hardware configuration of the worker support system 1000 will be described with reference to FIG. 2 in addition to FIG. FIG. 2 is a diagram showing the hardware configuration of the worker support system 1000 according to Embodiment 1. As shown in FIG.

As described above, the worker assistance system 1000 specifically includes the terminal device 101 and the bioinstrumentation device 102 .

The terminal device 101 includes a display device 103, a CPU (Central Processing Unit) 104, a ROM (Read Only Memory) 105, a RAM (Random Access Memory) 107, and a communication unit 108, which are interconnected via a bus 109. Prepare.

The display device 103 is implemented by, for example, a liquid crystal display or an organic EL (Electro Luminescence) display. The display device 103 corresponds to the output unit 213 shown in FIG. 3 which will be described later. The display device 103 may have a speaker.

The CPU 104 is hardware corresponding to the control unit 200 shown in FIG. 3 which will be described later. Further, the CPU 104 may execute respective processes of the electroencephalogram measurement unit 201, the pulse wave measurement unit 202, and the acquisition unit 207, which will be described later.

The ROM 105 holds, for example, a program 106 that is read and executed by the CPU 104 . The CPU 104 executes the processing of the control unit 200 by executing this program 106 .

A RAM 107 temporarily stores data generated by the processing of the CPU 104 .

The communication unit 108 is a communication circuit (communication module) for the terminal device 101 to wirelessly communicate with the bioinstrumentation device 102 .

The bioinstrumentation device 102 includes an electroencephalograph 102c and a pulse wave meter 102d. The bioinstrumentation device 102 wirelessly communicates with the communication unit 108 . The communication method may be wired.

[System configuration]
Next, the functional configuration of the worker support system 1000 will be described. FIG. 3 is a block diagram showing the functional configuration of the worker support system 1000 according to Embodiment 1. As shown in FIG.

As shown in FIG. 3, the worker support system 1000 includes an electroencephalogram measurement unit 201, a pulse wave measurement unit 202, a selective alertness estimation unit 204, a mental agitation estimation unit 205, an acquisition unit 207, and a work error extraction unit. 208 , work stagnation degree determination unit 209 , measurement result visualization unit 210 , state determination unit 211 , presentation unit 212 , and output unit 213 . Here, a selective attention level estimation unit 204, a mental agitation level estimation unit 205, an acquisition unit 207, a work error extraction unit 208, a work stagnation level determination unit 209, a measurement result visualization unit 210, a state determination unit 211, and a presentation unit 212 are components included in the control unit 200 . The control unit 200 is composed of, for example, at least one processor and a memory, and the processing performed by the control unit 200 below is realized, for example, by the processor executing a program stored in the memory. Further, each component other than the electroencephalogram measurement unit 201 and the pulse wave measurement unit 202 included in the worker support system 1000 is provided in the terminal device 101 . In addition, the worker support system 1000 may include a storage unit (such as a database described later) realized by a server or the like.

The electroencephalogram measurement unit 201 includes, for example, an electroencephalograph 102c and part of the functions of the terminal device 101 . Note that part of this function may be included in the control unit 200 of the terminal device 101 . Also, all functions of the electroencephalogram measurement unit 201 may be included in the control unit 200 of the terminal device 101 . Specifically, the electroencephalogram measurement unit 201 measures a voltage value between a first electrode in contact with the upper ear (in other words, temporal region) of the user 100 and a second electrode in contact with the earlobe of the user 100. This is a circuit for outputting as electroencephalogram data. Note that the positions of the first electrode and the second electrode are examples and are not particularly limited.

The pulse wave measurement unit 202 includes, for example, a pulse wave meter 102d and part of the functions of the terminal device 101. FIG. Note that part of this function may be included in the control unit 200 of the terminal device 101 . Also, all of the functions of pulse wave measuring section 202 may be included in control section 200 of terminal device 101 . Specifically, the pulse wave measurement unit 202 is a clip-type pulse wave sensor that measures a pulse wave using a transmissive photoplethysmographic method. Photoplethysmography irradiates the body surface with infrared light or red light (green light can also be used in the case of a reflective type), and measures the amount of change in the amount of light transmitted through the body (or the amount of change in the amount of light reflected inside the body). is a pulse wave measurement method that captures the change in blood flow. For example, the pulse wave measurement unit 202 measures a pulse wave near the earlobe of the user 100 and outputs pulse wave data.

The output unit 213 outputs tasks, response messages, image information, sound information, or the like to the user 100 . The output unit 213 may be, for example, a liquid crystal display or an organic EL display, and displays an image of content according to a signal from the presentation unit 212 included in the control unit 200 . Also, the output unit 213 may be, for example, a speaker or a smart speaker, and may output audio content corresponding to a signal from the presentation unit 212 included in the control unit 200 .

That is, the output unit 213 outputs a task to the user 100 means that the display displays the task. Further, the output unit 213 outputs a response to the user 100 means that the display displays image information and that the speaker outputs sound. Moreover, the output unit 213 outputs image information to the user 100 means that the display displays the image information. The fact that the output unit 213 outputs acoustic information to the user 100 means that the speaker outputs sound.

Acquisition unit 207 acquires wearing feeling information input by user 100 . Specifically, acquisition unit 207 is implemented by a part of the functions of the processor and hardware. This hardware is, for example, a keyboard, a mouse, a touch pen, a remote controller, a microphone, or a camera, which is a means for receiving the operation of the user 100, that is, a means for inputting information on the wearing feeling of the user 100 to the worker support system 1000. be. Note that the acquisition unit 207 may be realized by a touch panel, or may be realized by a touch panel and some functions of a processor. The acquisition unit 207 notifies the work stagnation degree determination unit 209 of the wearing feeling information and the timing of acquiring the wearing feeling information.

The wearing feeling information is information indicating the feeling of the user 100 regarding the wearing feeling of the bioinstrument 102 . For example, the acquiring unit 207 acquires, as the wearing feeling information, an answer input to the wearing feeling information input screen (for example, a questionnaire about wearing feeling) output to the output unit 213 .

Note that the acquisition unit 207 acquires subjective information such as, for example, information indicating the mood the user 100 feels when imagining that the user 100 is working on a task, or information regarding the physical condition of the user 100, in addition to the wearing feeling information. good too.

In addition, the acquisition unit 207 may acquire error information regarding work errors of the user 100 . For example, the error information may be information including an error log recorded when an error occurs on the terminal device 101 on which the user 100 is working, and the time when the error occurred. The acquisition unit 207 notifies the work error extraction unit 208 of the acquired error information.

The work stagnation degree determination unit 209 determines whether or not the work of the user 100 is stagnant. Specifically, the work stagnation degree determination unit 209 determines whether or not the work of the user 100 is stagnant based on the wearing feeling information obtained from the acquisition unit 207 . Further, the work stagnation degree determination unit 209 receives notification of the timing at which the wearing feeling information is acquired from the acquisition unit 207 . Furthermore, the work stagnation degree determination unit 209 notifies the state determination unit 211 of the determination result and the timing at which the wearing feeling information was acquired. Note that the work stagnation degree determination unit 209 may determine whether or not the work of the user 100 is stagnant based on, for example, subjective information of the user 100 described above, not limited to the wearing feeling information.

The electroencephalogram measurement unit 201 measures electroencephalograms of the user 100 . The electroencephalogram measurement unit 201 is implemented by the electroencephalograph 102c and some functions of the processor. Alternatively, the electroencephalogram measurement unit 201 may be implemented by a part of the functions of the processor, and in this case, measures the electroencephalogram of the user 100 by receiving the output signal from the electroencephalograph 102c. The electroencephalograph 102c is prepared to be attached to the user 100 and acquire the electroencephalogram of the user 100 as described above. The electroencephalograph 102c is, for example, an earhook type (ear hook type) device, and has a first electrode placed in contact with the head and a reference electrode placed in contact with the earlobe. As an example, as shown in FIG. 1, the first electrode is sandwiched between the auricle of the left ear and the temporal region and is included in the support portion 102a in contact with the head, and the reference electrode is the base in contact with the surface of the earlobe. It may be included in the base portion 102b. A ground electrode (an electrode that provides a body ground potential) may be included in the base portion 102b in contact with the back surface of the earlobe.

When the bioinstrumentation device 102 is an ear hook type device or a neckband type device, the user 100 wears the bioinstrumentation device 102 on both ears. In this case, the electroencephalogram of the user 100 measured and output by the electroencephalograph 102c is the first electrode of the bioinstrumentation device 102 worn on the right ear with reference to the reference electrode of the bioinstrumentation device 102 worn on the left ear. It shows time-series changes in voltage values. That is, the plurality of voltage values indicated by the electroencephalogram and the plurality of times during which the plurality of voltage values were measured correspond one-to-one. Further, when the bioinstrumentation instrument 102 is a device worn on the head such as a headgear type device, the bioinstrumentation instrument 102 includes a first electrode worn on the scalp of the user 100 or the forehead of the user 100, A reference electrode that attaches to the earlobe of the user 100 may also be included.

The electroencephalogram measurement unit 201 transmits the electroencephalogram acquired by the electroencephalograph 102 c to the selective attention level estimation unit 204 . The procedure for transmitting electroencephalograms from the electroencephalogram measurement unit 201 to the selective attention level estimation unit 204 is as follows. First, the electroencephalogram measurement unit 201 inputs an electrical signal representing the electroencephalogram of the user 100 to the amplifier. The amplifier differentially amplifies the electrical signal and outputs it to a low-pass filter (low-pass filter). Next, the low-pass filter removes unnecessary high frequency components and outputs to an AD (Analog-to-Digital) converter. The AD converter converts the analog signal output from the amplifier into a digital signal and outputs the digital signal. Finally, the digital signal converted by the AD converter is wirelessly transmitted to the selective attention level estimation unit 204 . Note that the amplifier, low-pass filter, and AD converter may be included in the electroencephalogram measurement unit 201 .

A pulse wave measurement unit 202 measures the pulse wave of the user 100 . The pulse wave measurement unit 202 is implemented by the pulse wave meter 102d and some functions of the processor. Alternatively, the pulse wave measurement unit 202 may be implemented by a part of the functions of the processor, and in this case, measures the pulse wave of the user 100 by receiving the output signal from the pulse wave meter 102d. As described above, the pulse wave meter 102d is prepared so that it can be worn by the user 100 and acquire the pulse wave of the user 100. FIG. Specifically, the pulse wave meter 102d is a pulse wave sensor that contacts the front and back surfaces of the earlobe of the user 100, is provided on the base portion 102b of the bioinstrumentation device 102, and sandwiches the front and back surfaces of the earlobe of the user 100. are arranged as follows. When the user 100 wears the bioinstrumentation device 102 on both ears, the pulse wave is measured by, for example, a pulse wave sensor in the base portion 102b of the bioinstrumentation device 102 worn on the right ear of the user 100 . The pulse wave sensor has a light-emitting diode (LED) as a light-emitting portion on its front surface and a photodetector (PD) as a light-receiving portion on its back surface.

Pulse wave measurement unit 202 transmits the pulse wave acquired by pulse wave meter 102 d to mental agitation degree estimation unit 205 . The procedure for transmitting the pulse wave from the pulse wave measurement unit 202 to the mental agitation estimation unit 205 is as follows. First, the pulse wave measurement unit 202 inputs an electric signal (periodic pulse current) indicating the pulse wave of the user 100 to the amplifier circuit. In the amplifier circuit, the current is converted into a voltage, and at the same time, the voltage is differentially amplified and output to a high-pass filter (high-pass filter). Next, the high-pass filter removes unnecessary low-frequency components (drift) and outputs to the low-pass filter (low-pass filter). Next, the low pass filter removes unnecessary high frequency components and outputs to the AD converter. The AD converter converts the analog signal output from the amplifier circuit into a digital signal and outputs the digital signal. Finally, the digital signal converted by the AD converter is wirelessly transmitted to the mental agitation estimation unit 205 . Note that the amplifier, high-pass filter, low-pass filter, and AD converter may be included in pulse wave measuring section 202 .

The selective attention level estimating unit 204 is based on brain waves measured during at least a part of a predetermined period (hereinafter also referred to as a target period) from when the task is output until the user 100 finishes the task. to estimate the degree of selective attention of the user 100 . The details of the target period and the method of estimating the degree of selective attention will be described later.

The mental agitation level estimation unit 205 determines the mental agitation level of the user 100 based on the pulse wave measured during the target period. A method of estimating the degree of mental agitation will be described later.

The measurement result visualization unit 210 determines a point determined by the selective attention level of the user 100 estimated by the selective attention level estimation unit 204 and the mental agitation level of the user 100 estimated by the mental agitation level estimation unit 205, It is plotted on a coordinate plane represented by two axes of selective attention and agitation. The measurement result visualization unit 210 may perform the plotting before and after the worker support system 1000 outputs the task to the user 100 .

The state determination unit 211 determines the state of the user 100 based on, for example, the selective caution level estimated by the selective caution level estimation unit 204 and the mental agitation level estimated by the mental agitation level estimation unit 205. decide. In addition to the degree of selective attention and the degree of mental agitation, the state determination unit 211 determines at least one of the error information extracted by the work error extraction unit 208 and the determination result determined by the work stagnation degree determination unit 209. Based on this, the status of user 100 may be determined. Specifically, the state determination unit 211 determines which of states 1 to 4 (eg, see FIG. 8) the user's state corresponds to.

The presentation unit 212 causes the output unit 213 to output information based on the state of the user 100 determined by the state determination unit 211 . For example, the presentation unit 212 refers to a database that stores a plurality of scheduled tasks, selects a task to be presented to the user 100 from the database, and causes the output unit 213 to output the selected task. The presentation unit 212 also notifies the selective attention level estimation unit 204 and the mental agitation level estimation unit 205 of the timing at which the task was output.

[motion]
Next, operation of the worker support system 1000 will be described. Below, we will explain the operation from the output of the task to the visualization of the estimation results of selective attention and mental agitation, in the order of selective attention estimation method, mental agitation estimation method, and visualization. do.

[Behavior: method for estimating selective attention]
First, a method for estimating the degree of selective attention will be described with reference to FIGS. 4, 5, and 7. FIG. FIG. 4 is a time chart of operations of the worker support system 1000. FIG. FIG. 5 is a diagram showing an example of the temporal transition of the indices of the selective attention level and the mental agitation level of the user 100. As shown in FIG. FIG. 7 is a flow chart of operations from the output of a task to the visualization of the results of estimating the user's selective attention level and mental agitation level.

As shown in FIG. 7 , first, the output unit 213 outputs tasks to the user 100 . Specifically, the output unit 213 is, for example, a display, and displays the task on the display (S100). At this time, the presentation unit 212 may notify the selective attention level estimation unit 204 and the mental agitation level estimation unit 205 of the timing at which the output unit 213 outputs the task to the user 100 (not shown). In addition, the presentation unit 212, for example, refers to a database in which a plurality of scheduled tasks (for example, schedule information of the user 100) is stored, thereby specifying a task to be imposed on the user 100 from now on, and performing the specified task. may be output to the output unit 213 . For example, as shown in FIG. 5, the task imposed on the user may be multiple tasks such as task 1 and task 2. FIG. In this case, the output unit 213 outputs task 1 and task 2 to the user 100 .

Subsequently, as shown in FIG. 7, when the task is displayed in step S100, the selective attention level estimation unit 204 receives an electroencephalogram signal from the electroencephalogram measurement unit 201 (S101), and is notified by the presentation unit 212. Brain waves are extracted based on the output timing of the task (S102). The selective attention level estimation unit 204 extracts the electroencephalograms measured during the target period from the electroencephalograms of the user 100 measured by the electroencephalogram measurement unit 201 .

The target period is, for example, the period from when the user 100 starts performing the task to when the user 100 finishes the predetermined work within the predetermined period from when the task is output until the user 100 finishes the task. For example, as shown in FIG. 4, the target period is 30 seconds from timing t1 when the task (here, filtering) is started to timing t4 when predetermined work (here, input error correction) is finished. period. Also, for example, as shown in FIG. 5, the predetermined period is a period of 8000 seconds from when the task is output until the user 100 finishes the task. The target period may start from the time when the task is output, and the end point of the target period may be set by the user 100 as appropriate without being limited to the time at which the predetermined work is completed.

For example, the end point of the target period may be the timing when a period of 10 seconds or more and 30 seconds or less has passed since the task was output, or the timing when a period of 10 seconds or more and 20 seconds or less has passed. Alternatively, it may be the timing after a period of 10 seconds or more and 15 seconds or less has passed.

The electroencephalogram measured by the electroencephalogram measurement unit 201 during the first target period of the predetermined period is referred to as the first electroencephalogram, and the pulse wave measured by the pulse wave measurement unit 202 during the first target period of the predetermined period is referred to as the first electroencephalogram. , is called the first pulse wave.

Subsequently, as shown in FIG. 7, the selective caution degree estimation unit 204 estimates the selective caution degree of the user 100 (S103). As described above, estimating the degree of selective attention means calculating the index of the degree of selective attention. The index of selective attention is a parameter indicating the activity state of the central nervous system in the cerebral cortex of the user 100 (hereinafter also referred to as a central nervous system activity parameter), and the selective attention is determined based on the index. . Selective attention is as described above.

Specifically, in step S103, the selective attention degree estimating unit 204, for example, a band-pass filter capable of selectively extracting the components of the beta wave (β wave) band and the theta wave (θ wave) band, etc. are used to extract the components of the beta wave band and the theta wave band from the electroencephalograms measured by the electroencephalogram measurement unit 201 during the target period. Then, the selective attention level estimation unit 204 determines the selective attention level of the user 100 based on the ratio of theta wave power to the beta wave power (θ/β ratio) (so-called central nerve activity parameter). . By expressing the index of selective attention as a ratio of θ/β, it is possible to express the scale (transition of the balance of central nervous system activity) with step-like characteristics. There is an advantage that it is easy to realize one's own state during task execution. Theta waves are rhythmic waves with a frequency slower than alpha waves, and their frequency band is 4 to 7 Hz. It is known that theta waves are physiologically observed in the brain waves of infants and children, during mental work such as mental arithmetic, problem solving, continuous addition, and games, light sleep, or brain waves during REM (REM) sleep. there is Beta waves are rhythmic waves with a faster frequency than alpha waves, and their frequency band is 14 to 40 Hz. Beta waves are known to be physiologically observed in dominant brain waves and the like during mental activity, such as when a person is awake and the eyes are open.

Then, the selective attention level estimation unit 204 determines the selective attention level of the user 100 based on the ratio of the power of the beta wave band component to the power of all components (eg, 1 to 40 Hz). good. In this case, the selective attention level estimating unit 204 determines that the selective attention level of the user 100 is higher as the power ratio of the beta wave component to the power of all the components increases. Conversely, the selective attention level estimation unit 204 determines that the selective attention level of the user 100 is low as the power ratio of the beta wave component to the power of all components is small.

In addition, the selective attention level estimation unit 204 may determine the selective attention level of the user 100 based on the ratio of the power of the theta wave band component to the power of all components. In this case, the selective attention level estimation unit 204 determines that the selective attention level of the user 100 is higher as the power ratio of the theta wave component to the power of all components is smaller. Conversely, the selective attention level estimation unit 204 determines that the selective attention level of the user 100 is low as the power ratio of the theta wave component to the power of all components increases.

It should be noted that the method of estimating and determining the degree of selective attention as described above is merely an example, and other existing methods may be used. For example, instead of the above power, power spectral density (μV / √Hz), frequency integral value of power spectral density (μV) ² (hereinafter referred to as power), or μV _pp (micro Volt peak-to-peak ) may be used.

[Movement: method for estimating mental agitation]
Next, FIGS. 4, 6, and 7 will be described for methods of estimating the degree of mental agitation. Here, the points different from the method of estimating the degree of selective attention will be mainly described. FIG. 6 shows an example of a two-axis visualization of selective attention and agitation estimates.

In parallel with the selective attention level estimation (steps S101 to S103) shown in FIG. Specifically, the mental agitation estimation unit 205 receives a pulse wave signal from the pulse wave measurement unit 202 (S104), and extracts a pulse wave based on the task output timing notified by the presentation unit 212. (S105). The mental agitation estimation unit 205 extracts the pulse wave measured during the target period from the pulse wave of the user 100 measured by the pulse wave measurement unit 202 . The target period is as described above.

For example, the mental agitation estimation unit 205 extracts the pulse wave measured during the target period from the user's 100 pulse wave measured by the pulse wave measurement unit 202 . Then, the mental agitation level estimation unit 205 determines the mental agitation level of the user 100 based on the extracted pulse wave. The target period may be appropriately changed according to, for example, the level of the task imposed on the user 100, the type of task, or the working environment. After step S<b>103 , the mental agitation degree estimation unit 205 notifies the state determination unit 211 of the mental agitation degree of the user 100 .

Next, the mental agitation degree estimation unit 205 estimates the mental agitation degree of the user 100 (S107). As described above, estimating the degree of agitation means calculating an index of the degree of agitation. The index of the degree of mental agitation is a parameter (hereinafter referred to as an autonomic nerve activity parameter) that indicates the state of activity of the autonomic nervous system that mediates the brain (more specifically, the brain stem) and the heart of the user 100. The degree of mental agitation is determined based on the indicator.

Specifically, in step S107, the mental agitation degree estimating unit 205 extracts the pulse wave peak time interval from the pulse wave measured by the pulse wave measuring unit 202 during the target period, and extracts the extracted time interval Calculate the pulse rate based on Then, the mental agitation estimating unit 205, based on the ratio of the calculated pulse rate and the pulse rate calculated a predetermined time ago (for example, 10 seconds ago) (hereinafter referred to as ΔPR (ΔPulse Rate)), 100 mood swings are determined. The degree of mental agitation is an index of a phenomenon in which the pulse rate abruptly increases when the user 100 is psychologically agitated. The pulse wave peak time interval corresponds to the so-called interval (RR interval) between two consecutive R waves in the waveform of an electrocardiogram, and is also referred to as the PP interval.

In addition, the mental agitation degree estimating unit 205 uses data of the PP interval calculated from the pulse wave (pulse wave signal) extracted for a predetermined time (for example, 30 seconds), or the second derivative waveform of the pulse wave with respect to time ( LF (Low Frequency) component (frequency band 0.04 to 0.15 Hz) by frequency analysis of the data of the aa interval (interval between peaks of the acceleration pulse wave) calculated from the waveform of the acceleration pulse wave) The power and the power of HF (High Frequency) components (frequency band 0.15 to 0.40 Hz) may be calculated. In this case, the mental agitation degree estimating unit 205 calculates the LF/HF ratio, which is the ratio of the power of the LF component (index of activity of the sympathetic nervous system) and the power of the HF component (index of activity of the parasympathetic nervous system). and the degree of mental agitation may be determined based on the calculated LF/HF ratio. The LF/HF ratio may be regarded as the balance between the activities of the sympathetic and parasympathetic nervous systems that make up the autonomic nervous system, and is used as a so-called stress index (sympathetic activity).

Specifically, for example, the smaller the LF/HF ratio, the mental agitation level estimation unit 205 determines that the user 100 has a lower mental agitation level (the user 100 is more relaxed). It is determined that the greater is the user 100's degree of mental agitation (the user 100 is nervous).

It should be noted that the method of estimating and determining the degree of mental agitation is merely an example, and other existing methods may be used. For example, the mental agitation degree estimating unit 205 calculates the peak time interval (for example, from the QRS wave of an electrocardiogram) of the acceleration pulse wave calculated from the pulse wave (pulse wave signal) extracted for a predetermined time (for example, 10 seconds). a-a interval corresponding to the RR interval to the next QRS complex), the longer the time interval, i.e., the lower the pulse rate (beats per minute), the more agitated the user. may be determined to be low. As an index for time-series analysis of pulse fluctuation (pulse fluctuation), for example, a vagus nerve tone strength index called RMSSD (Root Mean Square of Successive Difference) may be used. The RMSSD is a value obtained by taking the square root of the mean square of the difference between consecutively adjacent PP intervals, and the value of this RMSSD represents the transition between increasing and decreasing. If so, it may be determined that the degree of agitation is low. Also, if the RMSSD value sticks to a small value, it may be estimated that the degree of mental agitation is high. Furthermore, since pulse variability is dependent on pulse rate, the ratio of the RMSSD value divided by the average PP interval value, ie RMSSD/PP interval average (%), may be used to determine the degree of agitation.

As shown in FIG. 7, for example, when the work error extraction unit 208 acquires the error timing (S106), the mental agitation degree estimation unit 205 acquires the error timing from the work error extraction unit 208. may In this case, based on the calculated ΔPR and the timing of the error, the mental agitation degree estimation unit 205 may determine whether or not the user 100 was emotionally agitated at the timing when the error occurred. For example, as shown in FIG. 4, when the user 100 starts a task (here, filtering data) (at time t1) and an error occurs when inputting a numerical range for the filter, the user 100 performs the task. The terminal device 101 used for this outputs error information (time t2). User 100 corrects the error between times t3 and t4. At this time, as shown in FIG. 6, user 100's mental agitation level increases from time t2 to t3. Furthermore, at time t3, the threshold (ΔPR=10%) is exceeded. Accordingly, the mental agitation degree estimation unit 205 determines that the user 100 was emotionally agitated at the timing when the error occurred.

[Action: Visualization of estimation results of selective attention and mental agitation]
The measurement result visualization unit 210 visualizes the selective attention level and mental agitation level of the user 100 estimated in steps S103 and S107 (S108). Specifically, the measurement result visualization unit 210 creates a graph showing the estimation results of the selective attention level and the mental agitation level. More specifically, the measurement result visualization unit 210 plots the values of the central nerve activity parameter, which is an index of selective attention, and the autonomic nerve activity parameter, which is an index of mental agitation, on a coordinate plane. Create graphs. Then, the measurement result visualization unit 210 causes the output unit 213 to output the created graph.

For example, as shown in FIG. 6, the measurement result visualization unit 210 is represented by, for example, a vertical axis indicating θ/β, which is a central nerve activity parameter, and a horizontal axis indicating ΔPR, which is an autonomic nerve activity parameter. The values of θ/β and ΔPR obtained from the selective attention level estimation unit 204 and the mental agitation level estimation unit 205 are plotted for each time on a coordinate plane. A point indicating a set of a value of θ/β for each time (for example, an average value for 10 seconds) and a value of ΔPR (for example, a difference in the pulse rate 10 seconds before is converted into a percentage) is a point for each time. The state of the user 100 is shown. In the example of FIG. 6, a graph showing the estimation results of the selective attention level and mental agitation level of the user 100 from the time when the user starts the task (time t1) to the time when the filtering work ends (time t4). However, the starting time of visualization and the range of visualization may be appropriately set by the user 100 .

It should be noted that it is not essential to visualize the results of estimating the selective attention level and mental agitation level of the user 100, and the plotting of points as shown in FIG. 6 may be performed by internal processing.

The graph of FIG. 6 also shows the threshold (θ/β=0.5) for the index of selective attention (so-called central nerve activity parameter) and the index of mental agitation (so-called autonomic nerve activity parameter). A threshold (ΔPR=10%) is indicated by a dashed line. The four regions divided by these thresholds are used to determine the state of user 100, which will be described later. Hereinafter, of the four regions, the upper right region is also referred to as the first quadrant, the upper left region as the second quadrant, the lower left region as the third quadrant, and the lower right region as the fourth quadrant. For example, the second quadrant shown in FIG. 6 indicates ranges of selective attention and agitation levels suitable for user 100 to accomplish a task. Details of these areas will be described later in the process of determining the state of the user 100 . The thresholds for the selective attention level index and the mental agitation level index are determined empirically or experimentally by, for example, the designer of the worker support system 1000 or the like.

[Action: Determine User State]
Next, processing for determining the state of the user 100 (hereinafter also referred to as state determination processing for the user 100) will be described. FIG. 8 is a diagram showing four states of user 100 according to the first embodiment. FIG. 9 is a flowchart of the user 100 state determination process.

The state determination unit 211, for example, determines to which of the four states the user 100 belongs. Specifically, the state determination unit 211 determines whether or not the user 100 determine the state of More specifically, the state determination unit 211 determines whether the value of a central nervous activity parameter (eg, θ/β in FIG. 6), which is an index of selective attention, is greater than a threshold, The state of the user 100 is determined based on whether or not the value of the autonomic nerve activity parameter (eg, ΔPR in FIG. 6), which is an index of degree, is greater than a threshold. In addition to the selective caution level and the mental agitation level, the state determination unit 211 also determines at least one of the error information extracted by the work error extraction unit 208 and the determination result determined by the work stagnation level determination unit 209. Based on this, the status of user 100 may be determined.

First, four states of the user 100 will be described with reference to FIG. In the following, a high degree of selective attention means that the value of a central nerve activity parameter (eg, θ/β) is smaller than a threshold. Low selective attention means that the value of the central nerve activity parameter (θ/β) is greater than the threshold. A high degree of mental agitation means that the value of an autonomic nerve activity parameter (eg, ΔPR) is greater than a threshold. A low degree of mental agitation means that the value of the autonomic nerve activity parameter (ΔPR) is smaller than the threshold.

Referring to FIG. 6 again, the coordinate plane is divided into four regions by boundary lines indicating the thresholds of the central nerve activity parameters θ/β and boundary lines indicating the thresholds of the autonomic nerve activity parameter ΔPR shown in FIG. Each of the four areas delimited by these two boundary lines corresponds to the area indicating the four states 1 to 4 shown in FIG. More specifically, the first quadrant of FIG. 6 corresponds to the region showing state 2 of FIG. The second quadrant of FIG. 6 corresponds to the region showing state 1 of FIG. The third quadrant of FIG. 6 corresponds to the region showing state 3 of FIG. The fourth quadrant in FIG. 6 corresponds to the region showing state 4 in FIG.

The state determination unit 211 determines that the state of the user 100 is "state 1" when the degree of selective attention is high and the degree of mental agitation is low. State 1 is a state in which the user 100 maintains moderate selective alertness and agitation. In state 1, the user 100 can work without delay. Therefore, in state 1, it is better for the user 100 to continue working without taking a break.

The state determination unit 211 determines that the state of the user 100 is "state 2" when the degree of selective attention is high and the degree of mental agitation is high. State 2 is a state in which user 100 is agitated but maintains moderate selective attention. In State 2, the user 100 is upset, but is able to work. Therefore, in state 2, it is better for the user 100 to calm down once before starting work.

The state determination unit 211 determines that the state of the user 100 is "state 3" when the degree of selective attention is low and the degree of mental agitation is low. State 3 is a state in which the user 100 is not disturbed, but there are fewer objects to which the user 100 can pay attention in executing the work (in other words, the user 100's attention capacity for the work is reduced). . State 3 is a state in which the user 100 is tired. In such a state, the work efficiency of the user 100 is low, and errors are likely to occur (that is, it can be said to be a state that is a precursor to the occurrence of errors). Therefore, in State 3, it is better for the user 100 to continue working after taking a break.

The state determination unit 211 determines that the state of the user 100 is "state 4" when the degree of selective attention is low and the degree of mental agitation is high. State 4 is a state in which the user 100 is agitated and has a very low attention capacity for work. More specifically, State 4 is a state in which the user 100 is agitated and has very few objects to which he can turn his attention in performing the work. State 4 is a state in which the user 100 is confused. In such a state, even if the user 100 continues to work, there is a high possibility that errors will pile up on top of each other. Therefore, in State 4, it is better to stop working and take a break.

Subsequently, the state determination processing of the user 100 will be described with reference to FIG. 9 . The selective attention level shown in FIG. 9 refers to a central nerve activity parameter (eg, θ/β) that is an index of the selective attention level, and the mental agitation is an index of the mental agitation. shall refer to an autonomic nerve activity parameter (eg, ΔPR).

As shown in FIG. 9, the state determination unit 211 acquires the degree of selective caution estimated by the selective caution degree estimation unit 204 (S201), After acquiring the degree (S202), it is determined whether or not the degree of selective attention (here, θ/β) is less than 0.5 (S211). In other words, in step S211, the state determination unit 211 determines whether or not the degree of selective attention is high.

In step S211, when the state determination unit 211 determines that the selective attention level (θ/β) is less than 0.5 (Yes), it determines whether the mental agitation level (ΔPR) is less than +10%. Determine (S212). In other words, if the state determining unit 211 determines in step S211 that the degree of selective attention is high, it determines whether the degree of mental agitation is low.

In step S212, when the state determination unit 211 determines that the degree of mental agitation is less than +10% (Yes), the output unit 213 outputs a message “Please continue working” via the presentation unit 212. (S221). More specifically, when determining in step S212 that the degree of mental agitation is low, the state determination unit 211 determines that the state of the user 100 is “state 1”, and for example, it is associated with “state 1”. The above message may be output by referring to the database in which the message is stored. Alternatively, the state determination unit 211 may notify the state of the user 100 to the presentation unit 212, and the presentation unit 212 may refer to the database and output a message linked to state 1 to the output unit 213. . In this manner, state determination unit 211 does not output a message prompting user 100 to take a break when user 100 is in "state 1".

On the other hand, in step S212, when the state determination unit 211 determines that the degree of mental agitation (ΔPR) is not less than (that is, is greater than) +10% (No), the state of the user 100 is “state 2”. A decision is made (not shown), and a message "Please take a deep breath" is output to the output unit 213 via the presentation unit 212 (S222). In other words, the state determining unit 211 determines that the degree of selective attention is high in step S211, and determines that the degree of mental agitation is high in step S212. , outputs a message prompting the user 100 to take a deep breath, rest, and work calmly. A specific example of processing up to the output of the message is the same as the content described in state 1, so the description will be omitted hereafter.

On the other hand, in step S211, if the state determination unit 211 determines that the selective attention level (θ/β) is not less than 0.5 (that is, is greater than 0.5) (No), the mental agitation level (ΔPR) is It is determined whether or not it is smaller than +10% (S213). In other words, if the state determination unit 211 determines in step S211 that the degree of selective attention is low, it determines whether the degree of mental agitation is low.

In step S213, if the state determining unit 211 determines that the degree of mental agitation (ΔPR) is less than +10% (Yes), it determines that the state of the user 100 is "state 3" (not shown), A message "Please look up for a moment" is output to the output unit 213 via the presentation unit 212 (S223). In other words, if the state determination unit 211 determines that the degree of selective attention is low in step S211 and that the degree of mental agitation is low in step S213, the user 100 looks up for a moment and rests, Outputs a message prompting you to temporarily empty your mind.

On the other hand, in step S213, when the state determination unit 211 determines that the degree of mental agitation (ΔPR) is not less than (that is, is greater than) +10% (No), the state of the user 100 is “state 4”. It decides (not shown), and outputs a message to the output unit 213 via the presentation unit 212, "Let's resume work after a break" (S224). In other words, if the state determining unit 211 determines that the degree of selective attention is low in step S211 and determines that the degree of mental agitation is high in step S213, it determines that the state of the user 100 is “state 4”. , outputs a message telling the user 100 that a break (for example, about 5 minutes) is required. In this case, the state determination unit 211 may cause the output unit 213 to output music that the user 100 likes so that the user can take a break more effectively.

As described above, the state determination unit 211 instructs the user 100 to switch his/her mind or mind when the state of the user 100 is "state 2" or "state 3". Encourage temporary suspension of work. In addition, when the state of user 100 is “state 4”, state determining unit 211 prompts user 100 to take a break so that user 100 can work. As a result, the worker support system 1000 can provide appropriate support according to the state of the user 100 so that the user 100 can properly perform the work.

[Action: Determination of work stagnation]
Subsequently, the work stagnation degree determination unit 209 performs processing for determining the degree of work stagnation of the user 100 (also referred to as work stagnation degree determination processing) based on the wearing feeling information acquired by the acquisition unit 207 . FIG. 10 is a diagram showing an example of an input screen for wearing feeling information. FIG. 11 is a flowchart of work stagnation degree determination processing.

As described above, the acquisition unit 207 acquires the wearing feeling information input by the user 100 and outputs it to the work stagnation degree determination unit 209 . Here, after a questionnaire about wearing feeling (hereinafter also referred to as a questionnaire or wearing feeling questionnaire) is output (that is, presented), a predetermined message is output based on the answers to the questionnaire (so-called wearing feeling information). Explain the flow up to

As shown in FIG. 11, the output unit 213 acquires the wearing feeling questionnaire output from the presentation unit 212 (S301). The output unit 213 outputs the acquired questionnaire (not shown). The acquisition unit 207 outputs the answers to the questionnaire input by the user 100 to the work stagnation degree determination unit 209 (not shown).

The work stagnation degree determination unit 209 determines that the answer of the user 100 to the question "Where are you concerned?" (S302). If the work stagnation degree determination unit 209 determines that the answer from the user 100 is "very concerned" (see FIG. 10) or "very concerned" (Yes in S302), the user 100 (not shown), and outputs a message "Shall we take a break?" to the output unit 213 via the presentation unit 212 (S304).

On the other hand, if it is determined in step S302 that the answer from the user 100 is not "I am very concerned" or "I am very concerned" (No), in other words, if the answer from the user 100 is "I am not concerned" or "slightly worrisome" (see FIG. 10), the user 100's answer to the question "Do you have pain in your earlobe?" It hurts" is determined (S303). If the work stagnation degree determination unit 209 determines that the answer from the user 100 is "It hurts a lot" or "It hurts a lot" (Yes in S303), it determines that the work of the user 100 is stagnating. (not shown), and the process of step S304 is performed.

On the other hand, if it is determined in step S303 that the user's 100 answer is neither ``it hurts considerably'' or ``it hurts very much'' (No), in other words, if the answer of the user 100 is ``it doesn't hurt'' or ``it hurts'' 10), the work stagnation degree determination unit 209 determines that the work of the user 100 is not stagnant (not shown), and the output unit 213 via the presentation unit 212 A message "Please continue the work" is output to (S305).

Note that the work stagnation degree determination unit 209 may perform the determination process described above, and the state determination unit 211 or the presentation unit 212 may issue a message output instruction based on the determination result.

In this manner, the work stagnation degree determination unit 209 can determine whether or not the work of the user 100 is stagnant based on the wearing feeling information. In addition, by using information based on subjective evaluation of the user 100, such as wearing feeling information, in combination with information based on sensing such as electroencephalograms and pulse waves, it is possible to accurately estimate the state of the user 100. is considered to be high. Therefore, it can be expected that the work of the user 100 can be more appropriately supported.

Note that the flowchart of FIG. 11 shows an example of the work stagnation degree determination process. For example, when a large number of questions are displayed on the input screen of the feeling of wearing information, it is determined that the work of the user 100 is stagnant when there are a predetermined number or more of the lowest answers in five levels to the questions. good too.

In addition, in the example of FIG. 9, the user's answer to the questionnaire about the wearing feeling of the bioinstrumentation device 102 is input in four stages. Note that such an input screen is only an example, and the number of questions, scale steps, and the like are not particularly limited.

Although the processing for determining the degree of work stagnation based on the feeling of wearing information has been described here, the degree of work stagnation determination unit 209 determines whether at least one of the work error and the feeling of wearing information extracted by the work error extraction unit 208 If the work stagnation degree indicating the work stagnation degree of the user 100 is determined based on the above, and the determined work stagnation degree is higher than a predetermined value, it may be determined that the work of the user 100 is stagnating. For example, if a work error is extracted twice or more within a predetermined period of time (for example, within 10 seconds), it may be determined that the work of the user 100 is stagnant.

[Output next task and response message]
Next, display screens output to the output unit 213 based on the state of the user 100 will be described. The presentation unit 212 causes the output unit 213 to output the notification information. The presentation unit 212 may cause the output unit 213 to output based on the state of the user 100, for example. As described above, the output unit 213 outputs the notification information means that the output unit 213 presents the notification information. For example, the output unit 213 may output at least one of display information and audio information indicating an instruction to prompt the user 100 to take a break.

The notification information includes, for example, an instruction to the user 100 such as an instruction to urge the user 100 to take a break or an instruction to urge the user 100 to continue working, the next task (or a message suggesting the next task), a questionnaire about wearing comfort, They are information such as measurement data, a graph showing the state of the user 100, and a response message. As described above, the output unit 213 is, for example, a display unit such as a display, and may further have a speaker or the like.

FIG. 12 is a flow chart showing the procedure of tasks performed by the user. FIG. 13 is a diagram showing an example of a display screen.

A display screen 300 shown in FIG. 13 is a display screen output by the output unit 213 . The display screen 300 includes, for example, a work screen 301 for executing tasks, a presentation information field 302, a task display field 303, a wearing feeling information input field 304, a measurement data display field 305, and a response display field 306. .

The work screen 301 displays a plurality of tasks in the order in which the user processes the tasks. For example, referring to FIGS. 12 and 13, the work screen 301 of the display screen 300 has a certain direction, as shown in FIG. They are presented in order (eg, from left to right).

As a result, the user 100 can input data (S401), perform filtering (S402), and then calculate the vertical and horizontal axes because the tasks are displayed in the order of processing on the work screen 301. (S403), and finally graphing (S404).

The presentation information column 302 includes a task display column 303 , a wearing feeling information input column 304 , a measurement data display column 305 and a response display column 306 .

The task display column 303 is a column used to output the task (first task) in step S401 and the task (second task) in step S402. For example, when the task display column 303 displays "Please enter data", the user 100 executes the data input task displayed on the work screen 301. FIG.

The wearing feeling information input field 304 is a field for the user 100 to input wearing feeling information, similar to the wearing feeling information input screen of FIG. 10 . The wearing feeling information input field 304 may be always displayed, may be displayed before the user 100 executes a task, may be displayed at a predetermined time, or may be displayed according to changes in the state of the user 100. good too.

The measured data display column 305 may display data measured by the electroencephalogram measurement unit 201 and the pulse wave measurement unit 202, and includes central nerve activity parameters (eg, θ/β) and autonomic nerve activity parameters (eg, ΔPR). and their thresholds may be displayed.

A response display field 306 is a field in which a response message is displayed. The next task displayed in the task display field 303 and the message displayed in the response display field 306 differ according to the state of the user determined by the state determination unit 211 . FIG. 14 is a diagram showing the relationship between the state of the user 100 determined by the state determining unit 211, the display of the response display column 306, and the display of the task display column 303. As shown in FIG.

As described above, state 1 is a state in which user 100 maintains moderate levels of selective attention and agitation. In state 1, the user 100 can work without delay. Therefore, when it is determined that the user 100 is in state 1, the response display field 306 displays a response message, for example, "You look fine." Also, the next task displayed in the task display column 303 is, for example, a task that is more difficult than the current task. Note that the next task displayed in the task display field 303 may be, for example, a task having the same level of difficulty as the current task (a task having the same difficulty level).

State 2 is a state in which the user 100 is agitated but maintains an appropriate degree of selective attention. In State 2, the user 100 is upset, but is able to work. Since this is a state in which the performance of the task is not affected, the difficulty level of the task may be maintained. Therefore, when it is determined that the user 100 is in state 2, the response display field 306 displays a response message such as "Take a deep breath" or "Shall we take a break?" Also, the next task displayed in the task display field 303 may be a task with the same difficulty level as the current task, or may be a task easier than the current task.

State 3 is a state in which the user 100 is not disturbed, but there are fewer objects to which the user 100 can pay attention in executing the work (in other words, the user 100 has less attention to the work). is. State 3 is a state in which the user 100 is tired. In such a state, the work efficiency of the user 100 is low and errors are likely to occur (that is, it can be said to be a state that is a precursor to the occurrence of errors in work). Therefore, when it is determined that the user 100 is in state 3, the response display field 306 displays a response message such as "Please look up for a moment" or "Shall we take a break?", for example. Also, the next task displayed in the task display column 303 is, for example, a simpler task than the current task. Note that the next task displayed in the task display field 303 may be, for example, a task having the same degree of difficulty as the current task (a task having the same difficulty level).

State 4 is a state in which the user 100 is agitated and has very little attention to work. More specifically, State 4 is a state in which the user 100 is agitated and has very few objects to which he can turn his attention in performing the work. State 4 is a state in which the user 100 is confused. In such a state, even if the user 100 continues the work, there is a high possibility that the user 100 will not be conscious of the target of handling the error, and error will be repeated error after error. Therefore, when it is determined that the user 100 is in state 4, the response display field 306 displays, for example, a response message "I will present the next task after taking a break." That is, a message prompting a break (in other words, information prompting a break) is displayed. By taking a break, it is possible to prevent the user 100 from falling into an abnormal state in which errors pile up on top of each other. Also, the next task is not displayed in the task display column 303 for a while.

Supplementing the task difficulty, for example, when the task is a meeting, the presentation unit 212 increases the task difficulty by increasing the agenda of the meeting, and decreases the task difficulty by reducing the agenda of the meeting. Let In addition, the presentation unit 212 increases the difficulty of the task by moving the place of performing the task away from the current position of the user 100, and increases the difficulty of the task by moving the place of performing the task closer to the current position of the user 100. You can lower it. Note that the specific contents of the tasks are stored, for example, in the database storing the plurality of scheduled tasks described above.

In addition, the difficulty levels of the plurality of tasks are determined in advance based on the input of the user 100, etc., and the presentation unit 212 adjusts the difficulty levels of the tasks by changing (rescheduling) the order of the tasks. good too.

It is not essential that the difficulty level of the task is changed by the presentation unit 212, and the user 100 may adjust the difficulty level of the task, for example, by changing the difficulty level of a task with a high difficulty level to a low level.

(Embodiment 2)
A worker support system according to Embodiment 2 will be described below. FIG. 15 is a block diagram showing the functional configuration of a worker support system 1000A according to the second embodiment.

A worker assistance system 1000A shown in FIG. 15 has a configuration in which a motion measurement unit 214 and a motion estimation unit 215 are added to the configuration of the worker assistance system 1000 described in the first embodiment. In Embodiment 2, the description of the components of worker support system 1000A that are common to worker support system 1000 is omitted.

The motion measurement unit 214 measures the direction of motion of the user 100, velocity, displacement, and the like. The motion measuring unit 214 is implemented by a triaxial acceleration sensor or the like. The motion measuring unit 214 is built into the bioinstrumentation device 102, for example. For example, the three axes are forward/backward, left/right, and up/down of the user 100 in FIG. Note that if one of the three axes indicating left and right is a direction perpendicular to the scalp with which the support portion 102a is in contact, the motion measurement unit 214 determines that the electrode provided on the support portion 102a is away from the scalp. can be detected.

The motion estimating unit 215 estimates the motion of the user 100 based on the presence or absence of the motion of the user 100 measured by the motion measuring unit 214, the direction of the motion, the speed of the motion, and the like. The motion speed may be calculated by time integration of acceleration. At this time, the motion estimating unit 215 may determine whether the user 100 is moving or stationary using a binary value. In addition, the motion estimation unit 215 may use a binary value to determine whether or not the user 100 has moved when a condition determined by the direction or speed of the motion of the user 100 is satisfied. Also, the motion estimation unit 215 may estimate a series of motions performed by the user 100 from the data measured by the motion measurement unit 214 . Here, the series of motions is defined by the direction of motion and the speed of motion.

FIG. 16 is a time chart of operations of the worker support system 1000A. FIG. 17 is a diagram showing temporal transitions of the user's selective attention level, mental agitation level, and head movement. 16 and 17 are the same as those shown in FIG. 4, except that the absolute value of acceleration measured by the motion measuring unit 214 (hereinafter referred to as the magnitude of acceleration) integrated for one second (G·s) is shown. and FIG. In FIG. 16, the acceleration measured by the motion measuring unit 214 during the first target period among the predetermined periods is called the first acceleration. Since the first electroencephalogram and the first pulse wave have been described above, description thereof will be omitted here. The reason why the time window for clipping the waveform of the acceleration value is set to 1 second is that by matching the PP interval (about 1 second) of the normal pulse wave, the user 100 can see the time width that is easy for the user 100 to feel. This is to show the estimation result of the motion of The term "time window" means a window obtained by cutting time-series data for a predetermined period of time from long-time time-series data. Here, it refers to a time region limited as a range for calculating the integral value of the absolute value of acceleration. .

Motion estimation processing based on data acquired by the motion measuring unit 214 will be described below. First, the motion estimation unit 215 estimates the motion of the head of the user 100 in each of the X-axis, Y-axis, and Z-axis directions based on the one-second integral value of the magnitude of acceleration measured by the motion measurement unit 214. . The X-axis direction is, for example, the front-rear direction of the user 100 . The Y-axis direction is, for example, the horizontal direction of the user 100 . The Z-axis direction is, for example, the up-down direction (vertical direction) of the user 100 .

When the acceleration equal to or greater than the predetermined threshold occurs, it is considered that the user 100 is moving the head to some extent (or violently). For example, when the head of the user 100 moves greatly, the body of the user 100 also moves greatly corresponding to the movement of the head. Therefore, as the movement of the head of the user 100 increases, the change in the posture of the user 100 also increases. In such a case, it is presumed that the user's 100 attention capacity for the work is small, or that the user 100 is at a dead end in the work.

Based on the acceleration measured by the motion measuring unit 214, the motion estimating unit 215 determines that the 1-second integrated value of the acceleration in at least one of the X-axis direction, the Y-axis direction, and the Z-axis direction exceeds a predetermined threshold value (for example, , 0.3 G·s), it is estimated that the user 100 is moving the head to some extent or violently. For example, there is a possibility that the user 100 unconsciously moves their head to some extent or violently because they are concerned about the feeling of wearing the bioinstrumentation device 102 or because they are at a dead end in their work. For example, FIG. 17 omits the description (see FIG. 5) that “I am worried about my earlobes and is upset”, but in FIG. 17 as well as in FIG. By being anxious and agitated, the pulse rate increases and the degree of mental agitation also increases. At this time, since the movement of the head is larger than the threshold, it can be estimated that the reason why the head moved to some extent is that the user was concerned about the feeling of wearing the bioinstrumenting device 102 .

On the other hand, based on the acceleration measured by the motion measuring unit 214, the motion estimating unit 215 determines that the 1-second integrated value of the acceleration in at least one of the X-axis direction, the Y-axis direction, and the Z-axis direction is greater than or equal to If it is estimated that no acceleration has occurred, then it is estimated that the user 100 is not moving their head too much (or too hard). In this case, for example, the user 100 may be able to work without getting stuck, or the user 100 may not be moving their head too much (or too vigorously) because they are not bothered by the feeling of wearing the bioinstrumentation device 102 . have a nature.

[Action: Determine User State]
Next, processing for determining the state of user 100 (so-called state determination processing for user 100) will be described. FIG. 18 is a flowchart of state determination processing for user 100 according to the second embodiment. FIG. 19 is a diagram showing four states of a user according to Embodiment 2. FIG. Differences from the first embodiment will be mainly described below.

In Embodiment 1, the state determination unit 211 determines whether the user is alerted based on the degree of selective caution estimated by the selective caution level estimation unit 204 and the degree of mental agitation estimated by the mental agitation degree estimation unit 205. Although the state of 100 is determined, in the second embodiment, the state of user 100 is further determined based on the amount of movement of user's 100 head. In addition to the selective attention level, the mental agitation level, and the magnitude of the head movement, the state determination unit 211 determines the error information extracted by the work error extraction unit 208 and the work stagnation level determination unit 209. The state of the user 100 may be determined based on at least one of the determination results. The state determination unit 211, for example, determines to which of the four states the user 100 belongs.

First, four states of the user 100 will be described with reference to FIG. In FIG. 19, similarly to FIG. 8, high selective attention means that the value of the central nerve activity parameter (eg, θ/β) is smaller than the threshold. Low selective attention means that the value of the central nerve activity parameter (θ/β) is greater than the threshold. A high degree of mental agitation means that the value of an autonomic nerve activity parameter (eg, ΔPR) is greater than a threshold. A low degree of mental agitation means that the value of the autonomic nerve activity parameter (ΔPR) is smaller than the threshold. Further, "small head movement" means that the 1-second integrated value of acceleration is smaller than the threshold, and "large head movement" means that the 1-second integrated value of acceleration is smaller than the threshold.

The state determination unit 211 determines that the state of the user 100 is “state 1” when the degree of selective attention is high, the degree of mental agitation is low, and the head movement is small. State 1 is a state in which the user 100 maintains moderate selective alertness and agitation. In state 1, the user 100 can work without delay. Therefore, in state 1, it is better for the user 100 to continue working without taking a break.

Moreover, the state determination unit 211 determines that the state of the user 100 is “state 2” when the degree of selective attention is high, the degree of mental agitation is high, and the movement of the head is large. State 2 is a state in which user 100 is agitated but maintains moderate selective attention. In State 2, the user 100 is upset, but is able to work. Therefore, in state 2, it is better for the user 100 to calm down once before starting work.

Moreover, the state determination unit 211 determines that the user 100 is in "state 3" when the degree of selective attention is low, the degree of mental agitation is low, and the movement of the head is small. State 3 is a state in which the user 100 is not disturbed, but there are fewer objects to which the user 100 can pay attention in executing the work (in other words, the user 100's attention capacity for the work is reduced). . State 3 is a state in which the user 100 is tired. In such a state, the work efficiency of the user 100 is low, and errors are likely to occur (that is, it can be said to be a state that is a precursor to the occurrence of errors). Therefore, in State 3, it is better for the user 100 to continue working after taking a break.

Moreover, when the estimated selective attention level is low, the mental agitation level is high, and the head movement is large, the state determination unit 211 determines that the user 100 is in "state 4". State 4 is a state in which the user 100 is agitated and has a very low attention capacity for work. More specifically, State 4 is a state in which the user 100 is agitated and has very few objects to which he can turn his attention in performing the work. State 4 is a state in which the user 100 is confused. In such a state, even if the user 100 continues to work, there is a high possibility that errors will pile up on top of each other. Therefore, in state 4, it is better to stop working and take a break.

Subsequently, the state determination processing of the user 100 will be described with reference to FIG. 18 . In FIG. 18, as in FIG. 9, selective attention refers to a central nervous activity parameter (eg, θ/β) that is an index of selective attention, and mental agitation is an index of mental agitation. It shall refer to some autonomic nerve activity parameter (eg, ΔPR).

As shown in FIG. 18, the state determination unit 211 acquires the degree of selective caution estimated by the selective caution degree estimation unit 204 (S201), degree is acquired (S202). Further, the motion estimation unit 215 acquires the acceleration measured by the motion measurement unit 214 (S203), and calculates the 1-second integral value of the magnitude of the acceleration based on the acquired acceleration (S204).

In addition to the selective attention level and the mental agitation level, the state determination unit 211 acquires the 1-second integral value of the acceleration calculated by the motion estimation unit 215 (not shown), and obtains the selective attention level (here, θ/ β) is smaller than 0.5 (S211). In other words, in step S211, the state determination unit 211 determines whether or not the degree of selective attention is high.

In step S211, when the state determination unit 211 determines that the selective attention level (θ/β) is less than 0.5 (Yes), the mental agitation level (ΔPR) is greater than +10%, and the acceleration is greater than 0.3 G·s (S214). In other words, when determining in step S211 that the degree of selective attention is high, the state determining unit 211 determines whether the degree of mental agitation is low and the head movement of the user 100 is large.

In step S214, if the state determination unit 211 determines that the degree of mental agitation (ΔPR) is not greater than +10% and that the 1-second integral value of the magnitude of acceleration is not greater than 0.3 G·s (No), it determines that the state of the user 100 is "state 1" (not shown), and outputs a message "Please continue the work" to the output section 213 via the presentation section 212 (S221). In other words, when the state determining unit 211 determines that the degree of selective attention is high in step S211 and determines that the degree of mental agitation is low and the movement of the head of the user 100 is small in step S214, the user 100 It determines that the state is "state 1" and outputs a message prompting the user 100 to continue working instead of a message prompting him to take a break.

On the other hand, in step S214, if the state determination unit 211 determines that the degree of mental agitation (ΔPR) is greater than +10% and that the 1-second integral value of the magnitude of acceleration is greater than 0.3 G·s ( Yes), the state of the user 100 is determined to be "state 2" (not shown), and a message "Please take a deep breath" is output to the output section 213 via the presentation section 212 (S222). In other words, when the state determining unit 211 determines that the degree of selective attention is high in step S211 and determines that the degree of mental agitation is large and the movement of the head of the user 100 is large in step S214, the user 100 It determines that the state is "state 2" and outputs a message prompting the user 100 to take a deep breath and rest.

On the other hand, in step S211, if the state determination unit 211 determines that the selective attention level (θ/β) is not less than 0.5 (that is, is greater than 0.5) (No), the mental agitation level (ΔPR) is It is determined whether or not it is greater than +10% and the 1-second integrated value of the magnitude of acceleration is greater than 0.3 G·s (S215). In other words, when determining in step S211 that the degree of selective attention is low, the state determining unit 211 determines whether the degree of mental agitation is high and the head movement of the user 100 is large.

In step S215, the state determination unit 211 determines that the degree of mental agitation (ΔPR) is not greater than a threshold value (+10%) and that the 1-second integrated value of the magnitude of acceleration is greater than 0.3 G·s. If determined (No), the state of the user 100 is determined to be “state 3” (not shown), and a message “please look up for a moment” is output to the output unit 213 via the presentation unit 212 . (S223). In other words, when the state determining unit 211 determines that the degree of selective attention is low in step S211 and determines that the degree of mental agitation is low and the movement of the head of the user 100 is small in step S215, the user 100 It determines that the state is "state 3" and outputs a message prompting the user 100 to look up and rest for a moment.

On the other hand, in step S215, if the state determination unit 211 determines that the degree of mental agitation (ΔPR) is greater than +10% and that the 1-second integral value of the magnitude of acceleration is greater than 0.3 G·s (Yes), the state of the user 100 is determined to be "state 4" (not shown), and a message "Let's resume work after a break" is output to the output unit 213 via the presentation unit 212 (S224). . In other words, when the state determining unit 211 determines that the degree of selective attention is low in step S211, and determines that the degree of mental agitation is large and the movement of the head of the user 100 is large in step S215, the user 100 It determines that the state is "state 4" and outputs a message telling the user 100 that a break is required.

(Embodiment 3)
A worker support system according to Embodiment 3 will be described below. FIG. 20 is a diagram showing a schematic configuration of a worker support system 1000B according to Embodiment 3. As shown in FIG.

The worker support system 1000B specifically includes a terminal device 101, two biometric instruments 102, a wireless device 110, and a server 112. FIG. The terminal device 101 and the server 112 according to the third embodiment function as the terminal device 101 according to the first or second embodiment by communicating via the wireless device 110 and the Internet 111 . That is, in the worker support system 1000B, the terminal device 101 according to Embodiment 3 is not closed to the terminal device 101, and the terminal device 101 according to Embodiment 1 or 2 includes With at least one, the server 112 comprises the remaining components. For example, the terminal device 101 according to Embodiment 3 has an acquisition unit 207 and an output unit 213 , and the server 112 has a control unit 200 . Such a worker support system 1000B can perform operations similar to those of the worker support system 1000 and the worker support system 1000A. That is, in the worker support system 1000B, the terminal device 101 and the server 112 perform the same operations as those of the worker support system 1000 and the worker support system 1000A while communicating via the wireless device 110 and the Internet 111. I do.

(summary)
As described above, the worker support system 1000 includes the output unit 213 that outputs task and notification information to the user 100, the electroencephalogram measurement unit 201 that measures the electroencephalogram of the user 100, and the pulse wave that measures the pulse wave of the user 100. A wave measuring unit 202 and a control unit 200 are provided. Based on the electroencephalograms measured by the electroencephalogram measuring unit 201 during a target period, which is at least a part of the predetermined period after the task is output, the control unit 200 Estimate the selective attention level of the user 100, estimate the mental agitation level of the user 100 based on the pulse wave measured by the pulse wave measuring unit 202 during the target period, estimate the selective attention level, and The notification information is output to the output unit 213 based on the degree of mental agitation. Note that "estimated" means that a certain state quantity is calculated quantitatively (in other words, numerically). For example, estimating the degree of selective attention means that the degree of selective attention is quantitatively calculated, and more specifically, the index of the degree of selective attention is calculated.

Such a worker support system 1000 can present notification information to the user 100 based on the estimated selective attention level and mental agitation level. Therefore, the worker support system 1000 can support the user 100 so that the user 100 can work appropriately by presenting notification information based on the user's 100 selective attention level and mental agitation level. .

Also, for example, the notification information is at least one of display information and audio information indicating an instruction to prompt the user 100 to take a break.

The worker support system 1000 as described above presents notification information indicating an instruction to prompt the user 100 to take a break based on the estimated selective attention level and the estimated mental agitation level. can assist user 100 in taking proper breaks. Moreover, since the notification information is presented to the user 100 as at least one of the display information and the audio information, the user 100 can set the notification information to be output in a desired format, for example. Therefore, the worker support system 1000 can present the notification information to the user 100 more effectively.

Further, for example, when the control unit 200 determines that the estimated selective attention level is high and the estimated mental agitation level is low, the control unit 200 does not output the notification information to the output unit 213, and the estimated selection If it is determined that the degree of mental alertness is low or the estimated degree of mental agitation is high, the output unit 213 is caused to output notification information.

Such a worker support system 1000 has a high selective attention level and a low mental agitation level of the user 100 based on the estimated selective attention level and the estimated mental agitation level ( In other words, it is determined that the user 100 is in a state of being able to smoothly perform the work). In this case, the worker support system 1000 does not present notification information to the user 100 concerned. On the other hand, the worker support system 1000 has a low degree of selective attention or a high degree of mental agitation (that is, the user 100 is in a state where there is a possibility that the performance of the task may be hindered, such as being stuck in the work). ). In this case, the worker support system 1000 presents notification information to the user 100 concerned. In this way, the worker support system 1000 can switch the output of the notification information according to the state of the user 100, and thus can provide support according to the state of the user 100. FIG.

Further, for example, the control unit 200 calculates a selective attention index based on the electroencephalograms measured by the electroencephalogram measurement unit 201 during the target period, and the calculated selective attention index is smaller than the first value. If it is determined that the selective attention level is high, and if it is determined that the index of the selective attention level is equal to or higher than the first value, it is determined that the selective attention level is low, and the electroencephalogram measurement unit during the target period The index of mental agitation is calculated based on the pulse wave measured by 201, and if it is determined that the calculated index of mental agitation is smaller than the second value, the degree of mental agitation is determined to be low. , if it is determined that the index of mood swings is greater than or equal to a second value, then the mood swings are determined to be high.

Such a worker support system 1000 determines whether the selective attention level of the user 100 is high and whether the mental agitation level is high based on the balance between the activities of the central nervous system and the autonomic nervous system. Since it can be determined, the notification information can be output according to the physical and mental conditions of the user 100 .

Further, for example, the control unit 200 extracts at least one of the theta wave and beta wave components from the electroencephalogram measured by the electroencephalogram measurement unit 201 during the target period, and extracts at least one of the theta wave and beta wave. An index of selective attention level is calculated using the band component, the pulse wave peak time interval is extracted from the pulse wave measured by the pulse wave measurement unit 202 during the target period, and based on the extracted time interval Then, the pulse rate is calculated, and the ratio of the calculated pulse rate to the pulse rate calculated a predetermined time ago is calculated as an index of the degree of mental agitation.

Such a worker support system 1000 is based on the index of selective attention calculated based on the components of at least one of the theta wave and beta wave, and the degree of mental agitation calculated based on the pulse rate. Based on this, the selective alertness and agitation of the user 100 can be determined.

Further, for example, in calculating the index of the degree of mental agitation, the predetermined time can be changed.

Such a worker support system 1000 can use an appropriate index according to, for example, the character of the user 100 (for example, the posture or personality of the user 100 during work). Therefore, the worker support system 1000 can more accurately estimate the mental state of the user 100 (that is, the degree of mental agitation).

Also, for example, the target period for extracting an electroencephalogram and a pulse wave can be changed.

Such a worker support system 1000 can adjust the frequency of estimating the degree of selective attention and the degree of mental agitation by changing the target period for extracting electroencephalograms and pulse waves.

Further, for example, the worker support system 1000A further includes a motion measuring unit 214 that measures acceleration associated with the motion of the head of the user 100, Based on the acceleration measured by the motion measuring unit 214, the magnitude of the head movement of the user 100 is estimated, and in addition to the estimated selective attention level and mental agitation level, the head movement of the user 100 is calculated. Output notification information based on the magnitude.

Such a worker support system 1000A can be appropriately adjusted according to the state of the user 100 based on the amount of movement of the head of the user 100 in addition to the balance between the activity of the central nervous system and the activity of the autonomic nervous system of the user 100. notification information can be output.

Further, for example, the control unit 200 determines that the estimated selective attention level is high, the estimated mental agitation level is low, and the estimated magnitude of head movement of the user 100 is the third value. If it is determined to be smaller, the notification information is not output to the output unit 213, the estimated selective attention level is low, or the estimated mental agitation level is high, and the estimated head of the user 100 When it is determined that the magnitude of motion is equal to or greater than the third value, the output unit 213 is caused to output notification information.

The worker support system 1000A as described above is configured to calculate the selective attention level of the user 100 based on the estimated degree of selective attention and the degree of mental agitation as well as the estimated magnitude of head movement of the user 100. is high, the degree of mental agitation is low, and the degree of head movement of the user 100 is small (that is, the state of the user 100 is a state in which the user 100 can smoothly perform the work). In this case, the worker support system 1000A does not present notification information to the user 100 concerned. On the other hand, the worker support system 1000A has a low degree of selective attention or a high degree of mental agitation (that is, the user 100 is in a state where there is a possibility that the performance of the task may be hindered, such as being stuck in the work). ). In this case, the worker support system 1000A presents notification information to the user 100 concerned. In this way, the worker support system 1000A can switch the output of notification information according to the state of the user 100, so that it is possible to provide support according to the state of the user 100. FIG.

Further, for example, when the amount of movement of the user's head is equal to or greater than the third value, the control unit 200 determines that the change in posture of the user 100 is large in performing the task.

When the head of the user 100 moves, the worker support system 1000A moves the body of the user 100 in conjunction with the movement of the head. It is possible to determine whether or not the change in the posture of the user 100 is large.

Also, for example, the third value can be changed.

Such a worker support system 1000A can use an appropriate index according to the user 100's character (for example, posture during work or character).

Further, for example, the notification information further includes the estimated selective attention level and mental agitation level, and at least the selective attention level and mental agitation level of the estimated magnitude of head movement of the user 100. The control unit 200 further causes the output unit 213 to output the graph.

Such a worker assistance system 1000A can assist the user 100 to grasp at least his or her selective attention level and mental agitation level.

Further, for example, the worker support systems 1000 and 1000A further include an acquisition unit 207, and the control unit 200 causes the terminal device 101 used by the user 100 to perform the task to display the electroencephalogram measurement unit 201 and pulse rate information. The output unit 213 is caused to output a questionnaire regarding the feeling of wearing the bioinstrumentation device 102 provided with the wave measuring unit 202, and based on the answers to the questionnaire of the user 100 acquired by the acquisition unit 207, the wearing feeling of the bioinstrumentation device 102 is determined. The feeling information is acquired, and notification information is output to the output unit 213 based on the wearing feeling information.

Such worker support systems 1000 and 1000A can output appropriate notification information according to whether the feeling of wearing of the bioinstrumentation device 102 affects the performance of the task of the user 100 based on the feeling of wearing information. can be done. For example, the worker support systems 1000 and 1000A output notification information indicating an instruction to prompt the user 100 to take a break when there is a high possibility that the feeling of wearing the bioinstrumentation device 102 will hinder the user 100 from performing the task. , it is possible to output notification information that instructs the user 100 to continue working when there is a low possibility of hindering the execution of the task.

Further, for example, the acquisition unit 207 further acquires the log of the terminal device 101, and the control unit 200 further extracts work errors in the terminal device 101 of the user 100 based on the log acquired by the acquisition unit 207. Then, a work stagnation degree indicating the work stagnation degree of the user 100 is determined based on at least one of the extracted work error and wearing feeling information, and if the determined work stagnation degree is higher than the fourth value, notification The information is output to the output unit 213 .

Such worker support systems 1000 and 1000A can determine the stagnation degree of the work of the user 100 based on the information indicating the work error of the user 100 and the wearing feeling information regarding the wearing feeling of the bioinstrumentation device 102 . Further, the worker support systems 1000 and 1000A determine whether or not there is a high possibility that the degree of stagnation in the work of the user 100 will affect the performance of the task by setting a threshold based on the frequency of errors and the feeling of wearing. can judge. As a result, the worker support systems 1000 and 1000A can prompt the user 100 to take a break when there is a high possibility that the stagnation of the work of the user 100 will affect the performance of the task.

A worker support method executed by a computer such as the worker support system 1000 includes a first output step of outputting a task to be worked on to the user 100, an electroencephalogram measurement step of measuring the electroencephalogram of the user 100, It includes a pulse wave measurement step of measuring 100 pulse waves, and a control step, wherein the control step measures the electroencephalogram measured by the electroencephalogram measurement step during a target period that is at least a part of the predetermined period after the task is output. a first estimation step of estimating the selective attention level of the user 100 based on and a second outputting step of outputting notification information based on the estimated selective attention level and mental agitation level.

Such a worker assistance method can present notification information to the user 100 based on the estimated selective attention level and mental agitation level. Therefore, the worker support method can support the user 100 so that the user 100 can work appropriately by presenting notification information based on the user's 100 selective attention level and mental agitation level.

(Other embodiments)
Although the embodiments have been described above, the present invention is not limited to the above embodiments.

For example, in the above embodiments, a process executed by a specific processing unit may be executed by another processing unit. In addition, the order of multiple processes may be changed, and multiple processes may be executed in parallel.

Also, in the above embodiments, each component may be realized by executing a software program suitable for each component. Each component may be realized by reading and executing a software program recorded in a recording medium such as a hard disk or a semiconductor memory by a program execution unit such as a CPU or processor.

Also, each component may be realized by hardware. Each component may be a circuit (or integrated circuit). These circuits may form one circuit as a whole, or may be separate circuits. These circuits may be general-purpose circuits or dedicated circuits.

Also, general or specific aspects of the present invention may be implemented in a system, apparatus, method, integrated circuit, computer program or recording medium such as a computer-readable CD-ROM. Also, any combination of systems, devices, methods, integrated circuits, computer programs and recording media may be implemented.

For example, the present invention may be realized as a worker support method, a program for causing a computer to execute the worker support method, or a computer-readable program in which such a program is recorded. It may be implemented as a non-temporary recording medium.

Further, the present invention may be implemented as the terminal device of the above embodiment, or may be implemented as an application program for causing a general-purpose terminal device to operate as the terminal device of the above embodiment. Also, the present invention may be implemented as a computer-readable non-temporary recording medium in which such an application program is recorded.

Also, in the above embodiments, the worker support system is realized by a plurality of devices. It may also be implemented as a single device. When the worker support system is realized by a plurality of devices, the components included in the worker support system described in the above embodiments may be distributed to the plurality of devices in any way.

In addition, forms obtained by applying various modifications to each embodiment that a person skilled in the art can think of, or realized by arbitrarily combining the constituent elements and functions of each embodiment without departing from the spirit of the present invention. Also included in the present invention.

100 User 200 Control Unit 201 Electroencephalogram measurement unit 202 Pulse wave measurement unit 207 Acquisition unit 213 Output unit 214 Motion measurement unit 1000, 1000A, 1000B Operator support system

Claims (16)

an output unit that outputs task and notification information to a user;
an electroencephalogram measurement unit that measures electroencephalograms of the user;
a pulse wave measuring unit that measures the user's pulse wave;
and a control unit,
The control unit
estimating the selective attention level of the user based on the electroencephalogram measured by the electroencephalogram measurement unit during a target period that is at least part of a predetermined period after the task is output;
estimating the degree of mental agitation of the user based on the pulse wave measured by the pulse wave measuring unit during the target period;
causing the output unit to output the notification information based on the estimated selective attention level and the mental agitation level;
Worker support system. The notification information is at least one of display information and audio information indicating an instruction to prompt the user to take a break,
The worker support system according to claim 1. The control unit
When it is determined that the estimated selective attention level is high and the estimated mental agitation level is low, the notification information is not output to the output unit,
If it is determined that the estimated selective attention level is low or the estimated mental agitation level is high, output the notification information to the output unit;
The worker support system according to claim 1 or 2. The control unit
If the index of selective attention is calculated based on the electroencephalogram measured by the electroencephalogram measurement unit during the target period, and the calculated index of selective attention is determined to be smaller than a first value, If it is determined that the selective attention level is high and the index of the selective attention level is equal to or greater than the first value, the selective attention level is determined to be low,
a case where the index of mental agitation is calculated based on the pulse wave measured by the electroencephalogram measurement unit during the target period, and the calculated index of mental agitation is determined to be smaller than a second value; , determining that the mental agitation degree is low, and determining that the mental agitation degree is high if it is determined that the index of the mental agitation degree is equal to or greater than the second value;
The worker support system according to claim 3. The control unit
At least one of the theta wave and beta wave band components is extracted from the electroencephalogram measured by the electroencephalogram measurement unit during the target period, and the extracted at least one of the theta wave and beta wave band components is used. to calculate the index of selective attention,
From the pulse wave measured by the pulse wave measuring unit during the target period, the time interval between the peaks of the pulse wave is extracted, the pulse rate is calculated based on the extracted time interval, and the calculated calculating the ratio of the pulse rate and the pulse rate calculated a predetermined time ago as an index of the degree of mental agitation;
The worker support system according to claim 4. In calculating the index of agitation, the predetermined time can be changed.
The worker support system according to claim 5. The target period for extracting the electroencephalogram and the pulse wave can be changed.
The worker support system according to any one of claims 1-6. The worker support system further includes:
A motion measurement unit that measures acceleration accompanying movement of the user's head,
The control unit estimates the magnitude of movement of the user's head based on the acceleration measured by the motion measurement unit during the target period that is at least part of the predetermined period,
Outputting the notification information based on the size of the user's head movement in addition to the estimated selective attention level and the mental agitation level;
The worker support system according to any one of claims 1-7. The control unit
When it is determined that the estimated selective attention level is high, the estimated mental agitation level is low, and the estimated size of the user's head movement is smaller than a third value , without outputting the notification information to the output unit;
determined that the estimated degree of selective attention is low, or the estimated degree of agitation is high, and the estimated magnitude of movement of the user's head is equal to or greater than the third value; When the notification information is output to the output unit,
The worker support system according to claim 8. When determining that the magnitude of movement of the user's head is equal to or greater than the third value, the control unit determines that the change in posture of the user is large in performing the task.
The worker support system according to claim 9. the third value is modifiable;
The worker support system according to claim 10. The notification information further includes: the estimated selective attention level and the mental agitation level; including a graph showing
The control unit further causes the output unit to output the graph,
The worker support system according to any one of claims 8-11. The worker support system further comprises an acquisition unit,
The control unit
causing the output unit to output a questionnaire regarding the wearing comfort of the sensor comprising the electroencephalogram measurement unit and the pulse wave measurement unit, which is displayed on the information terminal used by the user to perform the task;
acquiring wearing feeling information about the wearing feeling of the sensor based on the user's answer to the questionnaire acquired by the acquiring unit;
causing the output unit to output the notification information based on the wearing feeling information;
The worker support system according to any one of claims 1-12. The acquisition unit further acquires a log of the information terminal,
The control unit further extracts a work error in the information terminal of the user based on the log acquired by the acquisition unit,
determining a degree of work stagnation indicating a degree of work stagnation of the user based on at least one of the extracted work error and the wearing feeling information;
outputting the notification information to the output unit when the determined degree of work stagnation is higher than a fourth value;
The worker support system according to claim 13. a first output step of outputting a task to be worked on to a user;
an electroencephalogram measurement step of measuring electroencephalograms of the user;
a pulse wave measuring step of measuring the user's pulse wave;
including a control step,
The control step includes:
a first estimation step of estimating the selective attention level of the user based on the electroencephalograms measured by the electroencephalogram measurement step during a target period that is at least part of a predetermined period after the task is output;
a second estimation step of estimating the degree of mental agitation of the user based on the pulse wave measured by the pulse wave measurement step during the target period;
a second output step of outputting notification information based on the estimated selective attention level and the mental agitation level;
including,
Worker assistance method. A program for causing a computer to execute the worker support method according to claim 15.

Images