JP2019154868A - Estimation system, estimation device, and estimation method - Google Patents

Abstract

To provide a technique that makes it possible to more accurately estimate the level of concentration of an operator on work.SOLUTION: An estimation system according to one aspect of the present invention determines, in a learning phase, a correspondence between a brain activity quantity and emotional state and a level of concentration, on the basis of brain activity data indicating a level of brain activity measured while a task is being executed, emotion data indicating an emotional state of the worker input in relation to the execution of the task, and execution result data indicating a level of concentration on the work identified from a result of execution of the task. In an estimation phase, the estimation system estimates, on the basis of the identified correspondence, the level of concentration of the worker on the task from the level of brain activity measured while the task is being executed and the emotional state of the worker input in relation to the execution of the task.SELECTED DRAWING: Figure 3

Description

  The present invention relates to an estimation system, an estimation device, and an estimation method.

  Factors that affect the performance of product productivity (especially quality, production volume, etc.) at work sites such as production lines that include worker tasks include factors resulting from worker conditions. ing. In a conventional work site, an administrator uses an operator as a method for increasing the degree of productivity due to such a worker's condition or suppressing the decrease in the degree of productivity (maintaining productivity). A method of visually observing the physical and mental state of the worker and giving the worker appropriate instructions to maintain and improve the productivity of the worker has been adopted.

  However, since such a method relies on the subjectivity of the manager to determine the state of the worker, there is a possibility that appropriate measures are not necessarily implemented due to oversight or misunderstanding. In other words, with the conventional method, it is difficult to measure the degree of productivity (performance) due to the worker's condition in an objective and repeatable manner, and the productivity / efficiency of the above production line is systematized. It has been difficult to maintain or enhance.

JP 2006-146173 A

  If the worker can maintain a more concentrated state for work for a longer time, the productivity / efficiency of the production line as described above can be increased or maintained. One clue for estimating whether or not the worker is concentrating on the work is to measure the amount of brain activity of the worker while working.

  For example, in Patent Document 1, a stimulus presentation system that determines whether or not a user is concentrated on content based on biological information such as heart rate, respiration, brain waves, brain magnetism, oxygen concentration, blood flow, facial expression, and body movement Has been proposed. Specifically, the stimulus presentation system proposed in Patent Document 1 includes the first brain activity information included in the biological information acquired after the concentration determination content is presented and before the next content is presented, and the stimulus. The second brain activity information included in the biological information acquired when the presented content is presented is compared. When the difference between the first brain activity information and the second brain activity information is greater than or equal to a predetermined amount, the stimulus presentation system determines that the user is concentrated on the stimulus presentation content, and if not, It is determined that the user is not concentrated.

  According to the method proposed in Patent Document 1, it is possible to estimate whether or not the worker is concentrated on work based on the measured brain activity. However, in order to accurately grasp whether or not the worker is concentrating on the work, it is not sufficient only based on this brain activity amount. This is because the amount of brain activity only indicates the amount of brain resources used for the worker's recognition, and it is unknown whether the brain resources are allocated to the work. For example, it is assumed that the worker is engaged in a specific work. In this case, when the worker is concentrated on the specific task, the amount of brain activity measured from the worker can be high, but when the worker is concentrated on some event other than the specific task. Moreover, the amount of brain activity measured by the worker can be high. In other words, in the method of determining the concentration state of the worker based only on the amount of brain activity, it is possible to distinguish whether the worker is concentrated on the target work or on other than the target work. Can not. For this reason, it is difficult to accurately measure the degree of productivity due to the state of the worker at the work site by the method using only the brain activity amount of the worker, and further improvement of the productivity at the work site is possible. It is difficult to plan.

  This challenge can occur not only in the above-described production line productivity / efficiency scene, but in any scene where a human performs a specific task. For example, in a driver monitoring system, it is important to identify whether or not the driver is concentrating on driving when grasping the state of the driver. Also, for example, in the educational scene, it is useful in measuring the educational effect or the like to specify whether or not the student is concentrating on learning.

  In one aspect, the present invention has been made in view of such a situation, and an object of the present invention is to provide a technique capable of more accurately estimating the degree of concentration of an operator on a task.

  The present invention employs the following configuration in order to solve the above-described problems.

  In other words, the estimation system according to one aspect of the present invention is configured to indicate a brain activity amount measured from a first worker while the first worker is executing a predetermined first task in a learning phase. A first acquisition unit that acquires one brain activity data, and a first emotion that indicates an emotional state of the first worker related to the execution of the first task by receiving an input of an emotion for the execution of the first task A second acquisition unit for acquiring data, and execution result data indicating a degree of concentration of the first worker with respect to the first task, which is specified based on a result of the first worker executing the first task And a correspondence relationship between the amount of brain activity and the emotional state and the degree of concentration based on the first acquisition unit, the first brain activity data, the first emotion data, and the execution result data Identify relationship And in the estimation phase, a fourth acquisition unit that acquires second brain activity data indicating a brain activity amount measured from the second worker while the second worker is executing the predetermined second task And a fifth acquisition unit for acquiring second emotion data indicating an emotion state of the second worker related to the execution of the second task by receiving an input of emotion for the execution of the second task; Based on the correspondence, the concentration of the second worker on the second task is determined from the amount of brain activity and the emotional state of the second worker indicated by the second brain activity data and the second emotion data. An estimation unit that estimates the degree; and an output unit that outputs a result of estimating the degree of concentration of the second worker on the second task.

  In the above configuration, in the learning phase, the first worker is caused to execute the predetermined first task, and the brain activity amount of the first worker while the first task is being executed is measured. Moreover, the emotional state of the 1st worker relevant to execution of the said 1st task is specified by receiving the input of the emotion with respect to execution of the 1st task. Furthermore, the degree of concentration of the first worker with respect to the first task is specified based on the result of the first worker executing the first task. Based on these, the estimation system according to the above configuration specifies a correspondence relationship between the amount of brain activity and emotional state and the degree of concentration. That is, the estimation system according to the above configuration specifies how much the worker is concentrated on the task when the worker is in what kind of brain activity and emotional state.

  On the other hand, in the estimation phase, the estimation system according to the above configuration uses the knowledge (correspondence) obtained in the learning phase to estimate the degree of concentration of the target worker on the task. That is, in the estimation phase, the second worker is caused to execute a predetermined second task, and the amount of brain activity during the execution of the second task is measured. Moreover, the emotional state of the 2nd worker relevant to execution of the said 2nd task is specified by receiving the input of the emotion with respect to execution of a 2nd task. The estimation system according to the above configuration estimates the degree of concentration of the second worker with respect to the second task from the brain activity amount and emotional state of the second worker obtained based on the correspondence relationship. And the estimation system which concerns on the said structure outputs the result of having estimated the degree of concentration with respect to the 2nd task of a 2nd worker.

  Therefore, the estimation system according to the above configuration estimates the degree of concentration of the worker with respect to the work based on these two indices by associating the two indices of the brain activity amount and the emotional state with the degree of concentration with respect to the work. It is possible. Of these two indicators, the amount of brain activity can indicate the amount of brain resources used for worker recognition. On the other hand, the emotional state can be related to the amount of brain activity to indicate what the brain resources are allocated to. Therefore, based on the two indicators of the amount of brain activity and the emotional state, it is possible to more accurately grasp whether or not the worker is concentrated on the work. Therefore, according to the said structure, the degree of concentration with respect to a worker's work can be estimated more correctly.

  The first worker is a worker who acquires data used for learning, and the second worker is a worker who estimates the degree of concentration on the task. The first worker and the second worker may or may not match. The first task is a task that is executed by an operator when acquiring data to be used for learning, and the second task is a task for which the degree of concentration is estimated. The first task and the second task are preferably coincident or related. However, the first task and the second task do not necessarily have to match or do not have to be related. Each task may have a predetermined amount of work to be performed at a certain time such as work performed by an operator on the production line.

  In the estimation system according to the one aspect, the brain activity amount in the first brain activity data and the second brain activity data may be indicated by a value of a phase locking index. According to this configuration, since the value of the amount of brain activity can be accurately measured by an objective and repeatable method, it is possible to estimate the degree of concentration of the worker with respect to work more accurately.

  In the estimation system according to the above aspect, the emotional state in the first emotion data and the second emotion data may be indicated by two indicators, Arousal and Valence. According to this configuration, since the emotional state can be expressed more accurately, the degree of concentration of the worker with respect to the work can be estimated more accurately. The arousal and valence are two indices used in the Russell ring model. The arousal (or activation) is an index representing the degree of arousal and indicates the degree of arousal / sedation or the degree of activity / inactivity. Valence is an index representing the emotional value, that is, the quality of emotion, and indicates the degree of positive / negative or the degree of pleasure / discomfort.

  In addition, as another form of the estimation system according to each of the above forms, an information processing method that realizes each of the above-described configurations, a program, a computer that stores such a program, or the like It may be a storage medium that can be read by an apparatus, a machine, or the like. Here, the computer-readable storage medium is a medium that stores information such as programs by electrical, magnetic, optical, mechanical, or chemical action. In addition, the estimation system according to each of the above aspects may be configured by one or a plurality of information processing apparatuses. Further, by extracting a part of the configuration corresponding to the estimation phase from the estimation system according to each of the above forms, a system, an apparatus, a method, a program according to another form, and a storage medium storing the program are constructed. Also good.

  In addition, for example, the estimation device according to one aspect of the present invention includes a first acquisition unit that acquires brain activity data indicating a brain activity amount measured from a worker while the worker is executing a predetermined task. A second acquisition unit that acquires emotion data indicating an emotional state of the worker related to the execution of the task by accepting an input of emotion to the execution of the task, the amount of brain activity and the emotional state, The correspondence data indicating the correspondence between the degree of concentration is acquired, and the worker's indicated by the brain activity data and the emotion data is based on the correspondence indicated by the acquired correspondence data. From the amount of brain activity and the emotional state, an estimation unit that estimates the degree of concentration of the worker with respect to the task, and a result of estimating the degree of concentration of the worker with respect to the task are output. It includes a radical 19, a.

Further, for example, in the estimation method according to one aspect of the present invention, the computer acquires brain activity data indicating a brain activity amount measured from the worker while the worker is performing a predetermined task. And obtaining emotion data indicating an emotional state of the worker related to the execution of the task by receiving an input of emotion for the execution of the task;
Acquiring correspondence data indicating the correspondence between the brain activity amount and the emotional state and the degree of concentration; and based on the correspondence indicated by the acquired correspondence data, the brain activity data and Estimating the degree of concentration of the worker with respect to the task from the amount of brain activity and the emotional state of the worker indicated by the emotion data, and a result of estimating the degree of concentration of the worker with respect to the task Is output, and an information processing method is executed.

  ADVANTAGE OF THE INVENTION According to this invention, the technique which can estimate the degree of concentration with respect to a worker's work more correctly can be provided.

FIG. 1 schematically illustrates an example of a scene to which the present invention is applied. FIG. 2 schematically illustrates an example of a hardware configuration of the estimation system according to the embodiment. FIG. 3 schematically illustrates an example of the software configuration of the estimation system according to the embodiment. FIG. 4 schematically illustrates an example of brain activity data according to the embodiment. FIG. 5A is a diagram for explaining an example of a technique for expressing an emotional state. FIG. 5B schematically illustrates an example of emotion data according to the embodiment. FIG. 6 illustrates an example of a processing procedure in the learning phase of the estimation system according to the embodiment. FIG. 7 schematically illustrates an example of the first task according to the embodiment. FIG. 8 schematically illustrates an example of a screen that accepts an emotion input. FIG. 9A schematically illustrates an example of the transition of brain activity data and emotion data of a worker who is highly concentrated on a task. FIG. 9B schematically illustrates an example of the transition of brain activity data and emotion data of a worker who has a low degree of concentration on a task. FIG. 10 schematically illustrates an example of correspondence data indicating the correspondence between the amount of brain activity and emotional state and the degree of concentration according to the embodiment. FIG. 11 illustrates an example of a processing procedure in the estimation phase of the estimation system according to the embodiment.

  Hereinafter, an embodiment according to an aspect of the present invention (hereinafter, also referred to as “this embodiment”) will be described with reference to the drawings. However, this embodiment described below is only an illustration of the present invention in all respects. It goes without saying that various improvements and modifications can be made without departing from the scope of the present invention. That is, in implementing the present invention, a specific configuration according to the embodiment may be adopted as appropriate. Although data appearing in this embodiment is described in a natural language, more specifically, it is specified by a pseudo language, a command, a parameter, a machine language, or the like that can be recognized by a computer.

§1 Application Example First, an example of a scene to which the present invention is applied will be described with reference to FIG. FIG. 1 schematically illustrates an example of an application scene of the estimation system 1 according to the present embodiment.

  The estimation system 1 according to the present embodiment is an information processing apparatus that estimates the degree of concentration of a worker with respect to work from two indicators of the amount of brain activity and emotional state of the worker. Specifically, in the learning phase, the estimation system 1 indicates the first brain activity amount measured from the first worker 50 while the first worker 50 is executing the predetermined first task 40. 1 Brain activity data is acquired. Further, the estimation system 1 receives the input of emotion for the execution of the first task 40, thereby acquiring first emotion data indicating the emotion state of the first worker 50 related to the execution of the first task 40. Further, the estimation system 1 acquires execution result data indicating the degree of concentration of the first worker 50 with respect to the first task 40 specified based on the result of the first worker 50 executing the first task 40. To do. Then, the estimation system 1 identifies the correspondence relationship between the amount of brain activity and the emotional state and the degree of concentration based on the first brain activity data, the first emotion data, and the execution result data.

  On the other hand, in the estimation phase, the estimation system 1 performs the second brain activity indicating the amount of brain activity measured from the second worker 51 while the second worker 51 is executing the predetermined second task 41. Get the data. In addition, the estimation system 1 receives second emotion data indicating the emotional state of the second worker 51 related to the execution of the second task 41 by receiving an input of emotion for the execution of the second task 41. Next, the estimation system 1 calculates the second activity based on the brain activity amount and emotional state of the second worker 51 indicated by the second brain activity data and the second emotion data based on the correspondence specified in the learning phase. The degree of concentration of the worker 51 on the second task 41 is estimated. Then, the estimation system 1 outputs a result of estimating the degree of concentration of the second worker 51 with respect to the second task 41.

  As described above, in the learning phase, the estimation system 1 according to the present embodiment associates the two indicators of the brain activity amount and the emotional state with the degree of concentration on work. Then, in the estimation phase, the estimation system 1 according to the present embodiment, based on the correspondence relationship specified in the learning phase, determines the worker (second worker 51) from the two indicators of brain activity and emotional state. The degree of concentration on the work (second task 41) is estimated. Of these two indicators, the amount of brain activity may indicate the amount of brain resources used for worker recognition. On the other hand, the emotional state can be related to the amount of brain activity to indicate what the brain resources are allocated to. Therefore, by using the two indexes of the brain activity amount and the emotional state, it is possible to more accurately grasp whether or not the worker is concentrated on the work. Therefore, according to the estimation system 1 according to the present embodiment, it is possible to more accurately estimate the degree of concentration of the worker with respect to the work.

  The first worker 50 is a worker who is a target for acquiring data used for learning, and the second worker 51 is a worker who is a target for estimating the degree of concentration with respect to work. The first worker 50 and the second worker 51 may be the same person or not the same person. The first task 40 is an operation that is performed by an operator when acquiring data used for learning, and the second task 41 is an operation that is a target for estimating the degree of concentration. The first task 40 and the second task 41 are preferably coincident or related. However, the 1st task 40 and the 2nd task 41 do not necessarily need to correspond, and do not need to be related. Each task (40, 41) may have a predetermined amount of work to be performed at a certain time such as work performed by an operator on the production line, but is not limited thereto. Note that the worker here means a subject (human) that performs the task. For example, when the estimation system is used in education, the worker may be a student who performs learning.

  Further, the method for measuring the amount of brain activity and the method for inputting emotion may be appropriately selected according to the embodiment. In the present embodiment, the electroencephalogram 30 attached to the head of the first worker 50 is used to measure the amount of brain activity of the first worker 50, and the emotion input of the first worker 50 is input to A touch panel 31 is used. Similarly, an electroencephalograph 35 attached to the head of the second worker 51 is used to measure the amount of brain activity of the second worker 51, and the touch panel 36 is used to input the emotion of the second worker 51. Is used.

§2 Configuration example [Hardware configuration]
Next, an example of the hardware configuration of the estimation system 1 according to the present embodiment will be described with reference to FIG. FIG. 2 schematically illustrates an example of a hardware configuration of the estimation system 1 according to the present embodiment.

  As shown in FIG. 2, the estimation system 1 according to the present embodiment is a computer in which a control unit 11, a storage unit 12, an external interface 13, an input device 14, an output device 15, and a drive 16 are electrically connected. . In FIG. 2, the external interface is described as “external I / F”.

  The control unit 11 includes a CPU (Central Processing Unit) that is a hardware processor, a RAM (Random Access Memory), a ROM (Read Only Memory), and the like, and is configured to execute information processing based on programs and various data. The The storage unit 12 is an example of a memory, and includes, for example, a hard disk drive or a solid state drive. In the present embodiment, the storage unit 12 includes the program 8, the first brain activity data 121, the first emotion data 122, the execution result data 123, the correspondence data 124, the second brain activity data 125, the second emotion data 126, and the like. Various types of information are stored.

  The program 8 performs information processing (FIG. 6), which will be described later, related to the learning phase for specifying the correspondence between the brain activity amount and emotional state and the degree of concentration on work, and the brain activity amount and This is a program for causing the estimation system 1 to execute information processing (FIG. 11), which will be described later, relating to an estimation phase for estimating the degree of concentration with respect to work from the emotional state. The program 8 includes a series of instructions for each information processing. The first brain activity data 121 indicates the amount of brain activity measured from the first worker 50 while the first worker 50 is executing the predetermined first task 40. The first emotion data 122 indicates the emotion state of the first worker 50 related to the execution of the first task 40. The execution result data 123 indicates the degree of concentration of the first worker 50 with respect to the first task 40 specified based on the result of the first worker 50 executing the first task 40. Correspondence relationship data 124 indicates a correspondence relationship between the amount of brain activity and emotion state and the degree of concentration specified based on the first brain activity data 121, the first emotion data 122, and the execution result data 123. The second brain activity data 125 indicates the amount of brain activity measured from the second worker 51 while the second worker 51 is executing the predetermined second task 41. The second emotion data 126 indicates the emotion state of the second worker 51 related to the execution of the second task 41. Details will be described later.

  The external interface 13 is, for example, a USB (Universal Serial Bus) port, a dedicated port, or the like, and is an interface for connecting to an external device. The type and number of external interfaces 13 may be appropriately selected according to the type and number of external devices to be connected. In the present embodiment, the estimation system 1 is connected to each electroencephalograph (30, 35) and each touch panel (31, 36) via the external interface 13.

  Each electroencephalograph (30, 35) is attached to the head of each worker (50, 51), for example, and is used to measure the amount of brain activity of each worker (50, 51). The type of each electroencephalograph (30, 35) is not particularly limited, and may be appropriately selected according to the embodiment. Further, the electroencephalograph 30 used for measuring the brain activity amount of the first worker 50 and the electroencephalograph 35 used for measuring the brain activity amount of the second worker 51 may be the same, Different types may be used.

  Each touch panel (31, 36) is disposed in the vicinity of each worker (50, 51), for example, and is used for receiving an input of emotion for execution of each task (40, 41). The type of each touch panel (31, 36) is not particularly limited, and may be appropriately selected according to the embodiment. For example, each touch panel (31, 36) may be a computer such as a smartphone or a tablet terminal. Further, the touch panel 31 used for inputting the emotion of the first worker 50 and the touch panel 36 used for inputting the emotion of the second worker 51 may be the same or different. May be.

  The input device 14 is a device for performing input using, for example, a mouse or a keyboard. The output device 15 is a device for outputting, for example, a display or a speaker. The user can operate the estimation system 1 using the input device 14 and the output device 15.

  The drive 16 is, for example, a CD drive, a DVD drive, or the like, and is a drive device for reading a program stored in the storage medium 9. The type of the drive 16 may be appropriately selected according to the type of the storage medium 9. The program 8 may be stored in the storage medium 9.

  The storage medium 9 stores information such as a program by an electrical, magnetic, optical, mechanical, or chemical action so that information such as a program recorded by a computer or other device or machine can be read. It is a medium to do. The estimation system 1 may acquire the program 8 from the storage medium 9.

  Here, in FIG. 2, as an example of the storage medium 9, a disk type storage medium such as a CD or a DVD is illustrated. However, the type of the storage medium 9 is not limited to the disk type and may be other than the disk type. Examples of the storage medium other than the disk type include a semiconductor memory such as a flash memory.

  In addition, regarding the specific hardware configuration of the estimation system 1, components can be omitted, replaced, and added as appropriate according to the embodiment. The estimation system 1 may be a general-purpose PC (Personal Computer) or the like.

Software configuration
Next, an example of the software configuration of the estimation system 1 according to the present embodiment will be described with reference to FIG. FIG. 3 schematically illustrates an example of the software configuration of the estimation system 1 according to the present embodiment.

  As illustrated in FIG. 3, the estimation system 1 according to the present embodiment executes a program by the control unit 11, whereby the first acquisition unit 111, the second acquisition unit 112, the third acquisition unit 113, and the relationship specifying unit 114. , A fourth acquisition unit 115, a fifth acquisition unit 116, an estimation unit 117, and an output unit 118.

  In the learning phase, the first acquisition unit 111 shows the first brain activity indicating the amount of brain activity measured from the first worker 50 while the first worker 50 is executing the predetermined first task 40. Data 121 is acquired. The second acquisition unit 112 acquires the first emotion data 122 indicating the emotional state of the first worker 50 related to the execution of the first task 40 by receiving the input of the emotion for the execution of the first task 40. . The third acquisition unit 113 stores execution result data 123 indicating the degree of concentration of the first worker 50 with respect to the first task 40, which is specified based on the result of the first worker 50 executing the first task 40. get. Based on the first brain activity data 121, the first emotion data 122, and the execution result data 123, the relationship identifying unit 114 identifies and identifies the correspondence between the brain activity amount and emotion state and the degree of concentration. Correspondence relation data 124 indicating the correspondence relation is generated.

  On the other hand, in the estimation phase, the fourth acquisition unit 115 outputs a second amount of brain activity measured from the second worker 51 while the second worker 51 is executing the predetermined second task 41. Brain activity data 125 is acquired. The fifth acquisition unit 116 acquires the second emotion data 126 indicating the emotional state of the second worker 51 related to the execution of the second task 41 by receiving the input of the emotion for the execution of the second task 41. . Based on the correspondence relationship indicated by the correspondence relationship data 124, the estimation unit 117 calculates the second activity 51 based on the brain activity amount and emotion state of the second worker 51 indicated by the second brain activity data 125 and the second emotion data 126. The degree of concentration of the worker 51 on the second task 41 is estimated. The output unit 118 outputs a result of estimating the degree of concentration of the second worker 51 with respect to the second task 41.

  The operation of each component of the estimation system 1 will be described in detail in an operation example described later. The software configuration of the estimation system 1 may be omitted, replaced, and added as appropriate according to the embodiment.

(Brain activity)
Next, an example of a method for expressing the amount of brain activity will be described with reference to FIG. FIG. 4 schematically illustrates an example of each brain activity data (121, 125). As shown in FIG. 4, in this embodiment, the brain activity amount of each worker (50, 51) in each brain activity data (121, 125) is the value of a phase synchronization index (hereinafter also referred to as PLI value). Indicated by.

  In general, the response to the auditory steady state response (ASSR) to sound stimuli at a given frequency is at rest (eg, before work) and when a task is being executed (eg, during work). Differences are known to occur. Therefore, according to the difference in the responsiveness of the auditory steady-state reaction, the amount of brain activity can be objectively measured.

  Therefore, in this embodiment, sound stimulation of a predetermined frequency is given to each worker (50, 51) by an earphone (not shown) or the like, and an electroencephalogram in an auditory steady state reaction is measured by each electroencephalograph (30, 35). Then, the value of the phase synchronization index is calculated based on the measured electroencephalogram. The phase synchronization index indicates phase synchronism between measurement channels of electroencephalogram measurement. The PLI value is calculated by a known method (for example, Yusuke Yokota, Yasushi Naruse, “Phase coherence of auditory steady-state response corresponding the amount of cognitive workload in a modified N-back task”, Neuroscience Research 100 (2015 ) 39-45) may be used. The portion 131 that appears as the PLI value indicates the amount of resources for the auditory steady state response, and the difference 132 between the rest time and the task execution time may indicate the amount of resources for the perception of work (task).

  In this embodiment, the brain activity amount for each task (40, 41) of each worker (50, 51) is represented by such a PLI value. However, the method of expressing the amount of brain activity is not limited to the example using such a PLI value, and may be appropriately selected according to the embodiment. It is known that the auditory steady-state reaction is strongly induced by a sound stimulus of 40 Hz. Therefore, in this embodiment, when obtaining the PLI value, a sound stimulus of 40 Hz may be used.

(Emotional state)
Next, an example of a method for expressing an emotional state will be described with reference to FIGS. 5A and 5B. FIG. 5A is a diagram for explaining an example of a technique for expressing an emotional state. FIG. 5B schematically illustrates an example of each emotion data (122, 126). In the present embodiment, the emotional state of each worker (50, 51) in each emotion data (122, 126) is indicated by two indices, Arousal and Valence.

  Arrosal and Valence are two indicators used in Russell's torus model. The arousal (or activation) is an index representing the degree of arousal and indicates the degree of arousal / sedation or the degree of activity / inactivity. On the other hand, the valence is an index representing the emotion value, that is, the quality of the emotion, and indicates the degree of positive / negative or the degree of pleasure / discomfort.

  In the example of FIG. 5A, the arousal is assigned to the vertical axis and the valence is assigned to the horizontal axis, and the arousal and valence are each indicated by a numerical value from −100 to +100. As an example of the correspondence between each numerical value and the emotional state, the emotional state of “enthusiastic / elated” is associated with the first quadrant in which both the arousal and valence values are positive. The emotional state “stressed” is associated with the second quadrant in which the arousal value is positive and the valence value is negative. The emotion of “fatigued” is associated with the third quadrant where the values of both arousal and valence are negative. The emotion of “relaxed” is associated with the fourth quadrant where the arousal value is negative and the valence value is positive.

  In the present embodiment, the emotional state of each worker (50, 51) is represented by the two indices such as the arousal and valence. As illustrated in FIG. 5B, the emotional state of each worker (50, 51) can be indicated by, for example, the amount of change in arousal and valence, the change in quadrant, and the like. However, the method of expressing the emotional state may not be limited to an example using such a Russell's ring model, and may be appropriately selected according to the embodiment.

§3 Example of operation [Learning phase]
Next, information processing in a learning phase for associating the amount of brain activity and emotional state with the degree of concentration on work will be described with reference to FIG. FIG. 6 is a flowchart illustrating an example of a processing procedure of the estimation system 1 in the learning phase. However, the processing procedure described below is merely an example, and each processing may be changed as much as possible. Further, in the processing procedure described below, steps can be omitted, replaced, and added as appropriate according to the embodiment.

(Step S101)
In step S <b> 101, the control unit 11 outputs a message for instructing the first worker 50 to execute the predetermined first task 40. The message output destination and output method may not be particularly limited, and may be appropriately selected according to the embodiment. The control part 11 may output the said message via the output device 15 and the output device (not shown) etc. which are arrange | positioned in the vicinity of the 1st worker 50, for example. For example, the control unit 11 may display the message on a display, or output the voice of the message from a speaker.

  The type of the first task 40 that instructs execution is not particularly limited, and may be appropriately selected according to the embodiment. The first task 40 may be, for example, part ordering work.

  FIG. 7 illustrates an example of the first task 40 according to the present embodiment. The first task 40 exemplified by FIG. 7 is an operation of tracing the line 401 displayed on the touch panel 31. Image data including the line 401 may be stored in the storage unit 12 or may be acquired from an external device such as a NAS (Network Attached Storage) via a network. Further, the shape and size of the line 401 may be determined as appropriate according to the embodiment.

  The control unit 11 transfers image data to the touch panel 31 to display an image including the line 401 on the touch panel 31 and outputs a message instructing to trace the line 401 via the output device 15. The control unit 11 receives an operation on the touch panel 31 and monitors the execution of the first task 40 by the first worker 50. The first task 40 may be such a simple operation. When the execution of the first task 40 is instructed, the control unit 11 advances the processing to the next step S102.

(Step S102)
Returning to FIG. 6, in step S <b> 102, the control unit 11 operates as the first acquisition unit 111 and is measured from the first worker 50 while the first worker 50 is executing the first task 40. First brain activity data 121 indicating the amount of brain activity is acquired.

  In this embodiment, the amount of brain activity is indicated by the PLI value in the auditory steady-state response. Therefore, the control unit 11 measures the electroencephalogram of the first worker 50 who is executing the first task 40 with the electroencephalograph 30, calculates the PLI value from the obtained electroencephalogram data, and based on the calculated PLI value. Thus, the first brain activity data 121 indicating the amount of brain activity is generated. Specifically, the control unit 11 may acquire the calculated PLI value as the first brain activity data 121 as it is, or the calculated PLI value and a predetermined reference value (for example, a PLI value at rest). The difference value may be acquired as the first brain activity data 121. Further, the control unit 11 may acquire a value obtained by applying a predetermined calculation process such as normalization to the calculated PLI value as the first brain activity data 121. Furthermore, when the electroencephalogram is measured a plurality of times, the control unit 11 may acquire the average value, variance, and the like of the PLI values calculated from the electroencephalogram data obtained by each measurement as the first brain activity data 121.

  Thereby, the control part 11 can acquire the 1st brain activity data 121 in which the brain activity amount was expressed by the PLI value. When the first brain activity data 121 is acquired, the control unit 11 advances the processing to the next step S103.

  Note that the method of acquiring the first brain activity data 121 is not limited to such an example, and may be appropriately selected according to the embodiment. For example, the generation of the first brain activity data 121 may be performed by another information processing apparatus other than the estimation system 1. In this case, the control unit 11 may acquire the first brain activity data 121 generated by another information processing apparatus via a network, a storage medium, or the like.

(Step S103)
In step S <b> 103, the control unit 11 operates as the second acquisition unit 112 and accepts an emotion input for the execution of the first task 40, whereby the emotion of the first worker 50 related to the execution of the first task 40. First emotion data 122 indicating a state is acquired. In the present embodiment, the touch panel 31 is used for inputting emotions for the execution of the first task 40.

  FIG. 8 schematically illustrates an example of a screen displayed on the touch panel 31 according to the present embodiment when accepting an emotion input. In the present embodiment, the emotional state is indicated by two indicators, arousal and valence. Therefore, the control unit 11 displays a screen including two axes of the arousal and the valence on the touch panel 31 and accepts an emotion input for the execution of the first task 40. The data of this screen may be held in the storage unit 12, or may be acquired from an external device such as NAS via a network. The input of emotion may be performed by 50 first workers, or may be performed by another person other than the first worker 50.

  Then, the control unit 11 identifies the values of the arousal and the valence based on the touched position on the screen (that is, the answer result), and indicates the emotional state based on the identified values of the arousal and the valence. First emotion data 122 is generated. Specifically, the control unit 11 may acquire the specified values of the arousal and valence as the first emotion data 122. Further, the control unit 11 may receive a plurality of emotion inputs, and may acquire the sum, average value, distribution, and the like of the values of the arousal and valence obtained by each input as the first emotion data 122. In addition, the control unit 11 accepts input of emotions before and after the execution of the first task 40, and each of the values of the arousal and the valence obtained by each input is used as a change amount of each of the arousal and valence by the execution of the first task 40. The difference may be calculated. The control unit 11 may acquire the change amounts of the arousal and the valence by the execution of the first task 40 as the first emotion data 122.

  Thereby, the control part 11 can acquire the 1st emotion data 122 by which the emotional state was expressed by two indicators, arousal and valence. When the first emotion data 122 is acquired, the control unit 11 advances the processing to the next step S104.

  Note that the method of acquiring the first emotion data 122 may not be limited to such an example, and may be appropriately selected according to the embodiment. For example, the generation of the first emotion data 122 may be performed by another information processing apparatus other than the estimation system 1. In this case, the control unit 11 may acquire the first emotion data 122 generated by another information processing apparatus via a network, a storage medium, or the like.

(Step S104)
In step S <b> 104, the control unit 11 operates as the third acquisition unit 113 and is identified based on the result of the first worker 50 executing the first task 40, and the first task 40 of the first worker 50 is identified. The execution result data 123 indicating the degree of concentration with respect to is acquired.

  The expression method of the degree of concentration is not particularly limited, and may be appropriately selected according to the embodiment. The degree of concentration may be represented by, for example, a binary value indicating whether or not the user is concentrated.

  Also, the method for specifying the degree of concentration is not particularly limited, and may be appropriately selected according to the embodiment. For example, the first worker 50 or an observer who observes that the first worker 50 is executing the first task 40 evaluates the degree of concentration of the first worker 50 with respect to the first task 40. May be. In this case, the control unit 11 can specify the degree of concentration with respect to the first task 40 by receiving an input from the first worker 50 or the observer using the input device 14, the touch panel 31, or the like.

  For example, the control unit 11 specifies the degree of concentration of the first worker 50 with respect to the first task 40 by any one of the methods described above, and generates the execution result data 123 based on the specified degree of concentration. Thereby, the control unit 11 can acquire the execution result data 123. When the execution result data 123 is acquired, the control unit 11 advances the processing to the next step S105.

  Note that the method of acquiring the execution result data 123 is not limited to such an example, and may be appropriately selected according to the embodiment. For example, the execution result data 123 may be generated by another information processing apparatus other than the estimation system 1. In this case, the control unit 11 may acquire the execution result data 123 generated by another information processing apparatus via a network, a storage medium, or the like.

(Steps S105 and S106)
In step S105, the control unit 11 operates as the relationship identifying unit 114, and based on the first brain activity data 121, the first emotion data 122, and the execution result data 123 acquired in steps S102 to S104, Identify correspondence between emotional state and degree of concentration.

  In the next step S106, the control unit 11 generates correspondence data 124 indicating the identified correspondence. Then, the control unit 11 stores the generated correspondence relationship data 124 in the storage unit 12. Thereby, the control part 11 complete | finishes the information processing of the phase of learning which concerns on this operation example.

  The number of cases of the first brain activity data 121, the first emotion data 122, and the execution result data 123 used to specify the correspondence between the amount of brain activity and the emotional state and the degree of concentration depends on the embodiment. May be appropriately determined. The control unit 11 can collect the first brain activity data 121, the first emotion data 122, and the execution result data 123 by appropriately repeating the processes of steps S101 to S104.

  Further, the method for specifying the correspondence relationship between the amount of brain activity and the emotional state and the degree of concentration is not particularly limited, and may be appropriately determined according to the embodiment.

  Here, using FIG. 9A and FIG. 9B, in the process of learning the execution of the first task 40 in the case where the degree of concentration with respect to the first task 40 is high and the case where it is low, it is obtained by steps S102 and S103. An example of the tendency seen in the 1st brain activity data 121 and the 1st emotion data 122 is demonstrated. FIG. 9A schematically illustrates an example of the transition of the first brain activity data 121 and the first emotion data 122 obtained from an operator having a high degree of concentration on the first task 40. FIG. 9B schematically illustrates an example of the transition of the first brain activity data 121 and the first emotion data 122 obtained from an operator having a low degree of concentration on the first task 40.

  When the operator is concentrated in the execution of the first task 40, most of the resources of the worker's brain are used for the execution of the first task 40 in the early stage T10 of learning which is not familiar with the first task 40. In addition, the amount of variation is assumed to be small. For this reason, as shown in FIG. 9A, in the stage T10 in the early stage of learning, the obtained PLI value becomes relatively small, and the fluctuation amount (for example, variance) of the PLI value can also become small. In addition, when the worker is concentrated in the execution of the first task 40, the awakening degree is relatively high in the emotional state of the worker in the initial stage T10 of the learning, while the execution of the first task 40 is performed. From this tension, it is assumed that negative emotional value will increase. Therefore, the value indicating the emotional state can appear in the second quadrant in the arousal and valence graphs.

  Subsequently, when the worker starts to get used to the first task 40 by repeatedly executing the first task 40 while concentrating on the execution of the first task 40, the first task 40 of the worker is executed. The ability to do is improved. This makes it possible to complete the first task 40 without recognizing the execution of the first task 40 in detail, so that it is assumed that the amount of brain resources used to execute the first task 40 is reduced. The In addition, when the user begins to get used to the first task 40, the tension of execution of the first task 40 is reduced, so that it is assumed that the positive emotional value is increased. Therefore, when the operator is concentrated in the execution of the first task 40, the obtained PLI value becomes relatively large at the middle stage T11 of learning that has begun to get used to the first task 40, and is a value indicating the emotional state. Can appear in the first quadrant in the arousal and valence graphs.

  Furthermore, if you are fully accustomed to the first task 40, the amount of brain resources used to execute the first task 40 will be further reduced, and you will be bored with the execution of the first task 40, so negative emotional values. Is expected to be high. Therefore, when the operator is concentrated on the execution of the first task 40, the obtained PLI value becomes relatively large at the end stage T12 of learning that is used to the first task 40, and the value indicating the emotional state is It can appear in the second quadrant in the graph of arousal and valence.

  On the other hand, when the worker is not concentrated on the execution of the first task 40, the tendency of the brain activity amount and the emotional state may vary. In this case, at the initial stage T20 of learning that is not familiar with the first task 40, the operator's brain resources are used not only for the execution of the first task 40 but also for any event other than the first task 40. It is assumed that Therefore, as shown in FIG. 9B, in the stage T20 in the initial stage of learning, the obtained PLI value can be relatively small, but the variation amount (for example, variance) of the PLI value can be relatively large. In addition, when the worker is not concentrated on the execution of the first task 40, it is assumed that the worker's arousal level is low. Therefore, in the stage T20 in the early stage of learning, the value indicating the emotional state can appear in the third quadrant in the arousal and valence graphs.

  Subsequently, when the worker is not concentrated on the execution of the first task 40, even if the first task 40 is repeatedly executed, it is assumed that the improvement in the ability to execute the first task 40 is small. . Therefore, it is assumed that the amount of brain resources used for execution of the first task 40 does not decrease much even in the middle stage T21 of learning that has begun to get used to the first task 40. Therefore, even at this stage T21, the obtained PLI value can remain relatively small. Moreover, although the positive emotional value can be increased by starting to get used to the first task 40, it is assumed that the worker's arousal level remains low. Therefore, in this stage T21, the value indicating the emotional state can appear in the fourth quadrant in the arousal and valence graphs. When the worker is not concentrated on the execution of the first task 40, it is assumed that the trend of the brain activity amount and the emotional state does not converge in a certain direction and varies after the stage T21 in the middle stage of learning. .

  Next, an example of a correspondence relationship between the amount of brain activity and emotional state derived from the tendency illustrated in FIGS. 9A and 9B and the degree of concentration will be described with reference to FIG. FIG. 10 schematically illustrates an example of the correspondence data 124 generated in steps S105 and S106 when the data 121 to 123 matching the tendency illustrated in FIGS. 9A and 9B are obtained. .

  The correspondence relationship data 124 illustrated in FIG. 10 is expressed in a table format. Each record (row data) is identified based on the brain activity amount indicated by the first brain activity data 121, the emotional state indicated by the first emotion data 122, and the concentration degree distribution indicated by the execution result data 123. One correspondence relationship between the amount of brain activity and emotional state and the degree of concentration is shown. Specifically, a threshold value for determining the degree of concentration is set for each of the brain activity amount and the emotional state based on the distribution state. Each record stores a threshold value for the amount of brain activity and the emotional state set thereby.

  For example, in the first record, when the average PLI value is less than Th1, the variance of the PLI values is less than Th2, and the emotional state value belongs to the second quadrant of the arousal and valence graphs, It shows that the degree of concentration is medium. This first record corresponds to the initial stage T10 of learning when the first task 40 is concentrated.

  Also, for example, in the second record, when the average PLI value is Th3 or more, the variance of the PLI value is less than Th4, and the emotional state value belongs to the second quadrant of the arousal and valence graphs It shows that the degree of concentration is high. This second record corresponds to stage T12 at the end of learning when the first task 40 is concentrated.

  Also, for example, in the third record, when the average PLI value is less than Th5, the variance of the PLI values is greater than or equal to Th6, and the emotional state value belongs to the third quadrant of the arousal and valence graphs This shows that the degree of concentration is low. This third record corresponds to the initial stage T20 when learning is not concentrated on the first task 40.

  The contents of each record illustrated in FIG. 10 are merely examples of correspondence relationships derived from the trends illustrated in FIGS. 9A and 9B, and the correspondence between the amount of brain activity and emotional state and the degree of concentration. The relationship is not limited to these examples, and may be appropriately determined according to the embodiment.

[Estimation Phase]
Next, information processing in the estimation phase for estimating the degree of concentration of the target worker with respect to the work based on the correspondence specified in the learning phase will be described with reference to FIG. FIG. 11 is a flowchart illustrating an example of a processing procedure of the estimation system 1 in the estimation phase. The processing procedure described below is an example of the “estimation method” of the present invention. However, the processing procedure described below is merely an example, and each processing may be changed as much as possible. Further, in the processing procedure described below, steps can be omitted, replaced, and added as appropriate according to the embodiment.

(Step S201)
In step S <b> 201, the control unit 11 operates as the fourth acquisition unit 115, and the amount of brain activity measured from the second worker 51 while the second worker 51 is executing the predetermined second task 41. 2nd brain activity data 125 which shows is acquired. If the 2nd brain activity data 125 is acquired, the control part 11 will advance a process to following step S202.

  This step S201 can be executed in the same manner as step S102. That is, the control unit 11 can acquire the second brain activity data 125 indicating the amount of brain activity of the second worker 51 by the same method as the method of acquiring the first brain activity data 121. For example, the control unit 11 measures the electroencephalogram of the second worker 51 who is executing the second task 41 with the electroencephalograph 35 and calculates the PLI value from the obtained electroencephalogram data, whereby the second worker 51 The second brain activity data 125 indicating the amount of brain activity can be acquired. However, the process of step S201 may not coincide with the process of step S102, and the brain activity of the second worker 51 is different from that of step S102 (for example, by a method not adopted in step S102). You may acquire the 2nd brain activity data 125 which shows quantity.

  Further, the type of the second task 41 is not particularly limited as in the case of the first task 40, and may be appropriately selected according to the embodiment. The second task 41 may be, for example, part ordering work.

  In addition, the control part 11 may start execution of the process of this step S201 by having started the execution of the 2nd task 41 by the 2nd worker 51 as a trigger.

(Step S202)
In step S <b> 202, the control unit 11 operates as the fifth acquisition unit 116 and accepts an emotion input for the execution of the second task 41, whereby the emotion of the second worker 51 related to the execution of the second task 41. The second emotion data 126 indicating the state is acquired. When the second emotion data 126 is acquired, the control unit 11 advances the processing to the next step S203.

  This step S202 can be executed in the same manner as step S103. That is, the control unit 11 can acquire the second emotion data 126 indicating the emotional state of the second worker 51 by a method similar to the method of acquiring the first emotion data 122. For example, the control unit 11 displays a screen including the two axes of the arousal and valence illustrated in FIG. 8 on the touch panel 36 and receives an emotion input for the execution of the second task 41. And the control part 11 specifies the value of each arousal and valence based on the touched position (namely, answer result) on a screen, and is 2nd emotion data which shows the emotional state of the 2nd worker 51 126 can be obtained.

(Step S203)
In step S <b> 203, the control unit 11 operates as the estimation unit 117 and acquires the correspondence relationship data 124. Then, based on the correspondence relationship indicated by the acquired correspondence relationship data 124, the control unit 11 determines the brain activity amount and emotion state of the second worker 51 indicated by the second brain activity data 125 and the second emotion data 126. The degree of concentration of the second worker 51 on the second task 41 is estimated.

  For example, the control unit 11 corresponds to the brain activity amount and emotional state of the second worker 51 indicated by the second brain activity data 125 and the second emotion data 126 based on the correspondence indicated by the correspondence data 124. The degree of concentration on the second task 41 is derived. Derivation of the degree of concentration may be appropriately performed according to the data format of the correspondence data 124.

  In the present embodiment, the correspondence relationship data 124 is expressed in the table format illustrated in FIG. The control unit 11 collates the brain activity amount and emotional state of the second worker 51 obtained by the processes of steps S201 and S202 with the values of the fields of the brain activity amount and emotional state of each record. And the control part 11 extracted the record suitable for the brain activity amount and emotional state of the 2nd worker 51 obtained by the process of step S201 and S202, and was stored in the field of the degree of concentration of the extracted record. Get the value.

  As a result, the control unit 11 responds to the brain activity amount and emotional state of the second worker 51 indicated by the second brain activity data 125 and the second emotion data 126 based on the correspondence indicated by the correspondence data 124. The degree of concentration on the second task 41 can be derived. When the estimation (derivation) of the degree of concentration on the second task 41 is completed, the control unit 11 advances the processing to the next step S204.

(Step S204)
In step S204, the control unit 11 operates as the output unit 118, and outputs a result of estimating the degree of concentration of the second worker 51 with respect to the second task 41 via the output device 15 or the like. The control unit 11 may display the result of estimating the degree of concentration of the second worker 51 with respect to the second task 41 by the process of step S203, for example, on a display, or output the result via voice through a speaker. May be.

  The output destination of the estimation result is not particularly limited, and may be appropriately selected according to the embodiment. The output of the estimation result may be performed, for example, toward the second worker 51 itself or to a person other than the second worker 51 such as a supervisor who supervises the second worker 51. Good. When the output of the estimation result is completed, the control unit 11 ends the information processing of the estimation phase according to this operation example.

[Characteristic]
As described above, the estimation system 1 according to this embodiment includes the first brain activity data 121 and the first emotion obtained from the first worker 50 who has executed the first task 40 in the learning phase of steps S101 to S106. Using the data 122 and the execution result data 123, a correspondence relationship between the amount of brain activity and emotional state and the degree of concentration on work is learned. Then, the estimation system 1 according to the present embodiment, in the estimation phase of steps S201 to S204, based on the correspondence relationship obtained in the learning phase, the operator (first step) The degree of concentration of the two workers 51) on the work (second task 41) is estimated.

  Of these two indicators, the amount of brain activity may indicate the amount of brain resources used for worker recognition. However, it is unclear whether the brain resources are allocated to the work or to other objects other than the work only with this brain activity amount index. Therefore, in this embodiment, the emotional state is further used in addition to the brain activity amount as an index for estimating the degree of concentration of the worker with respect to the work. Since this emotional state can vary depending on the subject of cognition, it can indicate what brain resources are allocated by being linked to the amount of brain activity. Therefore, by using the two indexes of the brain activity amount and the emotional state, it is possible to more accurately grasp whether or not the worker is concentrated on the work. Therefore, according to the estimation system 1 according to the present embodiment, it is possible to estimate the degree of concentration of the worker with respect to work more accurately.

§4 Modifications Embodiments of the present invention have been described in detail above, but the above description is merely an illustration of the present invention in all respects. It goes without saying that various improvements and modifications can be made without departing from the scope of the present invention. For example, the following changes are possible. In the following, the same reference numerals are used for the same components as in the above embodiment, and the description of the same points as in the above embodiment is omitted as appropriate. The following modifications can be combined as appropriate.

<4.1>
In the above embodiment, the amount of brain activity of each worker (50, 51) is measured by each electroencephalograph (30, 35). In addition, the amount of brain activity of each worker (50, 51) is indicated by the PLI value in the auditory steady-state response to sound stimulation of a predetermined frequency. However, the method of acquiring and expressing the brain activity amount may not be limited to such an example, and may be appropriately selected according to the embodiment.

<4.2>
In the said embodiment, the input of the emotional state of each worker (50, 51) is received using each touch panel (31, 36). In addition, the emotional state of each worker (50, 51) is indicated by two indices, arousal and valence. However, the emotion state input method and the expression method may not be limited to such examples, and may be appropriately selected according to the embodiment.

1 ... Estimation system,
11 ... Control unit, 12 ... Storage unit, 13 ... External interface,
14 ... input device, 15 ... output device, 16 ... drive,
111 ... 1st acquisition part, 112 ... 2nd acquisition part, 113 ... 3rd acquisition part,
114 ... Relationship identification unit, 115 ... Fourth acquisition unit, 116 ... Fifth acquisition unit,
117 ... estimation unit, 118 ... output unit,
121 ... 1st brain activity data, 122 ... 1st emotion data,
123 ... execution result data, 124 ... correspondence data,
125 ... second brain activity data, 126 ... second emotion data,
30/35 ... EEG, 31/36 ... touch panel,
40 ... first task, 41 ... second task,
50 ... 1st worker, 51 ... 2nd worker,
8 ... Program, 9 ... Storage medium

Claims (5)

A first acquisition unit that acquires first brain activity data indicating a brain activity amount measured from the first worker while the first worker is executing a predetermined first task in the learning phase;
A second acquisition unit that acquires first emotion data indicating an emotion state of the first worker related to the execution of the first task by receiving an input of an emotion for the execution of the first task;
A third acquisition unit for acquiring execution result data indicating a degree of concentration of the first worker with respect to the first task, which is specified based on a result of the first worker executing the first task;
Based on the first brain activity data, the first emotion data, and the execution result data, a relationship specifying unit that specifies a correspondence relationship between the brain activity amount and the emotional state and the degree of concentration;
A fourth acquisition unit that acquires second brain activity data indicating a brain activity amount measured from the second worker while the second worker is executing the predetermined second task in the estimation phase;
A fifth acquisition unit for acquiring second emotion data indicating an emotion state of the second worker related to the execution of the second task by receiving an input of emotion for the execution of the second task;
Based on the correspondence, the concentration of the second worker with respect to the second task from the amount of brain activity and the emotional state of the second worker indicated by the second brain activity data and the second emotion data. An estimation unit for estimating the degree of
An output unit that outputs a result of estimating a degree of concentration of the second worker on the second task;
Comprising
Estimation system. The amount of brain activity in the first brain activity data and the second brain activity data is indicated by a value of a phase synchronization index.
The estimation system according to claim 1. The emotional state in the first emotion data and the second emotion data is indicated by two indicators of arousal and valence.
The estimation system according to claim 1 or 2. A first acquisition unit that acquires brain activity data indicating a brain activity amount measured from the worker while the worker is performing a predetermined task;
A second acquisition unit that acquires emotion data indicating an emotional state of the worker related to the execution of the task by receiving an input of an emotion for the execution of the task;
The correspondence data indicating the correspondence between the amount of brain activity and the emotional state and the degree of concentration is acquired, and based on the correspondence indicated by the acquired correspondence data, the brain activity data and the emotion An estimation unit that estimates a degree of concentration of the worker with respect to the task from the brain activity amount and the emotional state of the worker indicated by data;
An output unit that outputs a result of estimating the degree of concentration of the worker on the task;
Comprising
Estimating device. Computer
Obtaining brain activity data indicating the amount of brain activity measured from the worker while the worker is performing a predetermined task;
Obtaining emotion data indicating an emotional state of the worker related to the execution of the task by accepting an input of emotion for the execution of the task;
Obtaining correspondence data indicating the correspondence between the brain activity amount and the emotional state and the degree of concentration;
Based on the correspondence relationship indicated by the acquired correspondence relationship data, the concentration of the worker with respect to the task is determined from the brain activity amount and the emotional state of the worker indicated by the brain activity data and the emotion data. Estimating the degree;
Outputting a result of estimating the degree of concentration of the worker on the task;
Run the
Estimation method.