Work ability determining method and work ability determining apparatus

Abstract

To accurately estimate a work performance state of a user by using brain activity information of the user.SOLUTION: The method includes a step (ST1) of acquiring brain activity information of a user, and a step (ST2) of estimating a work performance state of the user based on a frequency of switching between DMN and CEN obtained based on the acquired brain activity information, wherein the step (ST2) of estimating the work performance state of the user includes a step (ST11) of acquiring a DMN activity value, a step (ST12) of acquiring a CEN activity value, a step (ST19) of calculating a time from when the DMN activity value matches the CEN activity value to when the DMN activity value matches the CEN activity value next time, a step (ST20) of comparing the time with a preset time-threshold, and a step (ST20) of determining that the work performance state of the user is good when the time is determined to be smaller than the time-threshold (: YES). ST21.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] FIELD OF THE INVENTION The present invention relates to a method and an apparatus for determining work ability. [Background technology]

[0002] Patent Document 1 discloses a technology related to a stimulus presentation system that leads a user's psychological state to a target psychological state. Specifically, the system estimates the user's first psychological state based on the user's biological information (brain waves: alpha waves, etc.), and sets a target psychological state. Then, it presents stimulus presentation content (video, sound) to lead the user to the target psychological state, and then it is determined from the user's biological information acquired whether the second psychological state has reached the target psychological state. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-146173 Summary of the Invention [Problem to be solved by the invention]

[0004] However, although the technology disclosed in Patent Document 1 can certainly understand psychological states such as the concentration level, it cannot evaluate the quality of the work performed by the user.

[0005] For example, if a user continues to concentrate, they may lose sight of their surroundings, which may not be an appropriate state depending on the type of work they are doing.Even if it is possible to measure the level of concentration, it is not possible to evaluate how the state of concentration affects the user's work performance.

[0006] The present invention has been made to solve the above-mentioned problems, and an object of the present invention is to provide a work ability determination method and a work ability determination device that can accurately estimate a user's work performance state by using the user's brain activity information. [Means for solving the problem]

[0007] The method for determining work ability in the embodiment includes the steps of acquiring brain activity information of a user and estimating the work performance state of the user based on the frequency of switching between the default mode network (DMN) and the central executive network (CEN) obtained based on the acquired brain activity information.

[0008] The work ability discrimination device in the embodiment includes a brain activity detection unit that acquires brain activity information of a user, and a work performance state estimation unit that estimates the work performance state of the user based on the frequency of switching between the default mode network (DMN) and the central executive network (CEN) obtained based on the acquired brain activity information. [Effects of the Invention]

[0009] Since the present invention employs such a configuration, it is possible to accurately estimate the task performance state of the user using the user's brain activity information. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram showing an internal configuration of a working ability determination device according to an embodiment of the present invention. [Figure 2] FIG. 2 is a block diagram showing the internal configuration of a task performance state estimating unit in the first embodiment of the present invention. [Figure 3] FIG. 1 is an explanatory diagram showing the relationship between a user's actions and behavior and brain processes. [Figure 4]1A and 1B are explanatory diagrams for explaining estimation of task performance status using activity values ​​in the respective active regions of the default mode network (DMN) and the central executive network (CEN) obtained based on brain activity information in an embodiment of the present invention, where (A) is an explanatory diagram for explaining a method using the time when the activity values ​​of both are the same, and (B) is an explanatory diagram for explaining a method using the average value of both. [Figure 5] 1 is a flowchart showing a basic flow of a method for determining a user's working ability in the first embodiment of the present invention. [Figure 6] 1 is a flowchart illustrating a first method using DMN and CEN for determining a user's task ability in an embodiment of the present invention. [Figure 7] 1 is a flowchart illustrating a first method using DMN and CEN for determining a user's task ability in an embodiment of the present invention. [Figure 8] 10 is a flowchart illustrating a second method using DMN and CEN for determining a user's task ability in an embodiment of the present invention. [Figure 9] 1 is a flowchart illustrating a first method using a salience network (SN) for determining a user's working ability in an embodiment of the present invention. [Figure 10] 10 is a flowchart illustrating a second method using an SN for determining a user's working ability in an embodiment of the present invention. [Figure 11] 10 is a flowchart illustrating a method for determining a user's working ability using a difference in brain activity values ​​according to an embodiment of the present invention. [Figure 12] 10 is a flowchart showing a flow of guiding a task execution state in an embodiment of the present invention. [Figure 13] FIG. 10 is a block diagram showing the internal configuration of a task performance state estimating unit in a task performance determination device according to a second embodiment of the present invention. [Figure 14]10 is a flowchart showing a basic flow of a method for determining a user's working ability in a second embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Note that the drawings are schematic and may differ from the actual product. Furthermore, the embodiments of the present invention shown below are merely examples of devices and methods for embodying the technical concept of the present invention, and the technical concept of the present invention does not limit the structure, arrangement, etc. of the components to those described below. The technical concept of the present invention can be modified in various ways within the technical scope defined by the claims.

[0012] Fig. 1 is a block diagram showing the internal configuration of a work ability determination device 1 according to an embodiment of the present invention. The work ability determination device 1 is a device used to estimate the work performance state of a user who is a subject of evaluation and to determine whether the work ability is good or bad. It is also used to guide the user toward further improvement of the user's work performance state based on the estimated user's work performance state.

[0013] The functions of the work ability determination device 1 described below are realized by the control unit 13, which is made up of, for example, a processor, executing a computer program stored in the storage unit 12. The work ability determination device 1 may also be formed by dedicated hardware for executing each of the information processing operations described below. For example, the work ability determination device 1 may include a functional logic circuit set in a general-purpose semiconductor integrated circuit. The work ability determination device 1 may also include a programmable logic device (PLD) such as a field-programmable gate array (FPGA).

[0014] The work ability discrimination device 1 comprises a brain activity detection unit 11, a memory unit 12, a control unit 13, a work performance state estimation unit 14, a work performance state output unit 15, and a work state induction unit 16, and each unit exchanges information via a bus B.

[0015] Furthermore, a brain activity information acquisition device 2 can be connected to the work ability determination device 1. The brain activity information acquisition device 2 is used, for example, to acquire brain activity information of a user that is necessary to estimate the quality of the work performance state of the user who is performing the work.

[0016] The brain activity information acquired by the brain activity information acquisition device 2 is input to the brain activity detection unit 11. The brain activity detection unit 11 may passively receive the brain activity information each time it is acquired by the brain activity information acquisition device 2. Alternatively, the acquired brain activity information may be stored in the brain activity information acquisition device 2, and the brain activity detection unit 11 may actively acquire the brain activity information from the brain activity information acquisition device 2 at predetermined time intervals, for example.

[0017] The brain activity information acquired from the brain activity information acquisition device 2 via the brain activity detection unit 11 is stored in the storage unit 12. The storage unit 12 also stores various thresholds that are set in advance and are used to estimate the quality of the user's task performance, as will be described later.

[0018] Furthermore, the storage unit 10 may store an application used by the work performance state estimation unit 14 (described later) to estimate the work performance state, or an application used by the work state induction unit 16 to execute the induction process when applying a stimulus to the user using the stimulus application device 3 (described later).

[0019] The storage unit 12 is configured, for example, by a semiconductor storage device, a magnetic storage device, an optical storage device, etc. Alternatively, the storage unit 12 may include memories such as a register, a cache memory, a ROM (Read Only Memory) used as a main storage device, and a RAM (Random Access Memory).

[0020] The control unit 13 controls each unit constituting the work capacity determination device 1. It may also control the brain activity information acquisition device 2 and the stimulus application device 3. The control unit 13 is configured, for example, as an electronic circuit, and includes a processor (not shown). The processor may be, for example, a CPU (Central Processing Unit) or an MPU (Micro-Processing Unit).

[0021] As explained above, the task performance state estimation unit 14 is based on the premise that each unit is controlled by the control unit 13. However, the control is not limited to this, and each unit may have a function of performing calculations, for example.

[0022] The task performance state estimation unit 14 estimates whether the task performance state of the user is good or bad. A specific estimation method will be described separately using drawings. The task performance state output unit 15 outputs the task performance state of the user estimated by the task performance state estimation unit 14. The task performance state output unit 15 notifies the user by outputting to an alarm device capable of notifying the user, such as a monitor or lamp.

[0023] The task state guidance unit 16 guides the user to improve the task state based on the task state of the user estimated by the task performance state estimation unit 14. Note that the task performance state guidance here is based on the task performance state of the user, but the task performance state guidance may be performed whether the task performance state is determined to be "good" or "bad."

[0024] That is, the work performance state guidance process that is performed when the work performance state is determined to be "good" aims to further improve the work performance state. On the other hand, the work performance state guidance process that is performed when the work performance state is determined to be "bad" aims to improve the work performance state so that the work performance state is determined to be "good."

[0025] When inducing the user's task performance state, the task state induction unit 16 uses a stimulation device 3 connected to the task ability determination device 1. The stimulation device 3 is a device capable of implementing, for example, transcranial brain stimulation, stimulation of peripheral nerves, or biofeedback. Another possible method of stimulating peripheral nerves is to stimulate the auricular branch of the vagus nerve with an electric current.

[0026] Transcranial brain stimulation is a method of applying physical stimulation to the brain from outside the user's skull using direct or alternating current to induce temporary changes in movement, perception, and cognition, while peripheral nerve stimulation is a method of applying physical stimulation to motor nerves and autonomic nerves to induce temporary changes in movement, perception, and cognition.

[0027] Biofeedback converts information about an individual's physiological responses, which the individual is not aware of, into perceptible stimuli, such as visual, auditory, and tactile, and presents it to the user. Based on the presented information, the user can understand their own condition from a physiological and psychological perspective, and promote self-regulation by changing their own brain activity patterns.

[0028] Although it is assumed here that the brain activity information acquisition device 2 and the stimulation application device 3 are separate devices, they may be configured as a single integrated device. Alternatively, they may be configured as an integrated device with the work ability determination device 1.

[0029] Next, a method for determining work ability in an embodiment of the present invention will be described sequentially with reference to the drawings. First, a description will be given of each part of the work performance state estimation unit 14 in the work ability determination device 1. Fig. 2 is a block diagram showing the internal configuration of the work performance state estimation unit 14 in the first embodiment of the present invention.

[0030] The task performance state estimation unit 14 includes an information acquisition unit 141, a calculation unit 142, a comparison unit 143, and a determination unit 144. The information acquisition unit 141 acquires brain activity information from the brain activity information acquisition device 2 via the brain activity detection unit 11. In the following, it will be described that the information acquisition unit 141 acquires brain activity information from the brain activity information acquisition device 2 as appropriate.

[0031] The calculation unit 142 calculates a value required to estimate the quality of the user's task performance state. The comparison unit 143, for example, compares the value calculated by the calculation unit 142 with various preset thresholds. Alternatively, the comparison unit 143 acquires brain activity information acquired by the information acquisition unit 141 from the brain activity information acquisition device 2 and compares it with preset thresholds. The determination unit 144 determines the quality of the user's task performance state based on the comparison result of the comparison unit 143.

[0032] The functions of the above-mentioned components of the task performance state estimation unit 14 will be explained in more detail below in the description of determining whether the task performance state of the user is good or bad.

[0033] Next, the concept underlying the discrimination method will be explained using Figure 3. Figure 3 is an explanatory diagram showing the relationship between a user's movements, actions, and brain processes. The explanatory diagram in Figure 3 shows the process when a user performs a task from three perspectives: "during the task," "action," and "brain."

[0034] When a user performs a task, they first perceive the object, make a cognitive judgment, and then perform the required action. This "task" process is shown in the top part of the explanatory diagram in Figure 3. Below this, the "action" process is shown. When a user performs a task, they first observe the surrounding environment. This observation of the surrounding environment corresponds to "perception" in the "task" process described above.

[0035] Then, in order to determine what is necessary for the work, a behavioral process called important object extraction is carried out. This corresponds to recognizing and determining what is necessary for the work in the work process. After grasping the object, the behavioral process is carried out in which the actual action is performed carefully. This behavior corresponds to the "action" during the work.

[0036] In this way, the flow from when a user grasps a situation to when they take action can be explained in three parts. When a user performs a task, these parts are repeated as a group. For example, in the "Work Process," the three parts of "Perception," "Recognition and Judgment," and "Action" are grouped together and surrounded by dashed lines. Similarly, in the "Action Process," "Observation of the Surrounding Environment," "Important Object Extraction," and "Careful Action" are grouped together and surrounded by dashed lines.

[0037] In the explanatory diagram in Figure 3, there is an arrow pointing to the right at the bottom, which indicates the passage of time. Therefore, as time passes, a set of three tasks or actions is repeated in the "Task Process" and "Action Process."

[0038] In the explanatory diagram in Figure 3, the three tasks or actions that make up a single group for both the "Work Process" and the "Action Process" are shown only on the far left, and the depiction of "Work Processes" and "Action Processes" that are repeated over time is omitted.

[0039] The bottom row of the explanatory diagram in Figure 3 shows the "brain processes" that correspond to the "task processes" and "behavioral processes." The brain processes show the activity of the neural network used in the brain to understand the user's state.

[0040] In the "brain process" category, the vertical axis indicates whether the brain activity is "large (large)" or "small (small)." This indicates whether the brain activity of the above-mentioned cranial nerve network is active or not. Three types of cranial nerve networks have been identified so far, and in the embodiment of the present invention, these three cranial nerve networks are used as indicators to estimate the user's task performance state.

[0041] These three types of brain neural networks are the Default Mode Network (DMN), the Central Executive Network (CEN), and the Salience Network (SN).

[0042] The DMN is a brain activity pattern that appears when recalling past memories or when one's attention is focused on one's own internal information. On the other hand, the CEN is a brain activity pattern that appears when one's attention is focused on information external to oneself. Therefore, in a nutshell, there is a brain activity pattern (CEN) when a person (user) is concentrating on something, and a brain activity pattern (DMN) when not. If we think of this as being in the middle of a task, when one searches for important things in the surroundings while comparing the criteria one considers important with the surrounding information, a DMN brain activity pattern appears, while when one focuses on and concentrates on important things, a CEN brain activity pattern appears.

[0043] When people behave, their brain processes switch back and forth between the DMN and CEN. In other words, situations where the DMN appears and situations where the CEN appears switch depending on the task or behavior. The SN appears when switching between the DMN and CEN. In other words, the SN acts as a switch to switch between the DMN and CEN.

[0044] In the diagram in Figure 3, the solid line indicates the pattern of brain activity in the DMN, while the dashed line indicates the pattern of brain activity in the CEN. The magnitude of brain activity in the DMN and CEN corresponds to the task process and behavioral process. Specifically, in the task process, in the cases of "perception" and "cognitive judgment," the DMN appears as a brain process. On the other hand, in the case of "action," the user is more focused than in the cases of "perception" and "cognitive judgment," and the CEN appears.

[0045] In other words, in the process of moving from cognitive judgment to action, the brain process switches from the DMN to the CEN. The SN is involved in this switch. Therefore, in the explanatory diagram in Figure 3, when the DMN's brain activity changes from high to low, the area where the CEN's brain activity changes from low to high can be considered to be the area controlled by the SN. In Figure 3, the area where the SN is involved in the switch between the DMN and CEN is circled with a dotted line.

[0046] These three types of cranial neural networks are patterns of neural activity within the brain, and therefore can be understood from the exchange of electrical signals at the neural level or the subsequent secondary changes in cerebral blood flow. Therefore, the work ability determination device 1 in the embodiment of the present invention estimates the user's work performance state by understanding these brain activity patterns.

[0047] Specifically, in an embodiment of the present invention, near-infrared spectroscopy is employed as a method for understanding brain activity patterns. Near-infrared spectroscopy uses light to extract indicators of blood flow in the brain. Near-infrared light is characterized by its low absorption by biological tissue. Therefore, by using near-infrared light, it becomes easier to obtain information indicating the user's brain activity.

[0048] Specifically, when two wavelengths are used in near-infrared spectroscopy, the degree of light absorption differs between the two types of hemoglobin (oxygen-bound and non-oxygen-bound) contained in the user's bloodstream. From the difference in absorbance between oxygen-bound and non-oxygen-bound hemoglobin, the amount of change (concentration change) in oxygen-bound and non-oxygen-bound hemoglobin per brain volume can be measured.

[0049] As mentioned above, near-infrared spectroscopy uses light to extract an index of cerebral blood flow, and measures changes in hemoglobin concentration. These changes in concentration are considered to reflect changes in cerebral blood flow. Therefore, while near-infrared spectroscopy does not measure cerebral blood flow itself, it can be understood to represent changes in cerebral blood flow.

[0050] The above-described brain activity information acquisition device 2 acquires the user's brain activity information using near-infrared spectroscopy. However, methods for grasping the brain activity pattern include, in addition to near-infrared spectroscopy, methods for capturing the electrical neural activity of the brain, such as electroencephalography and magnetoencephalography. In other words, any method may be employed as long as it can measure the above-described brain activity pattern.

[0051] Furthermore, the brain activity information acquisition device 2 may be a device that acquires brain activity information by direct contact with the user, or may be a device that can acquire brain activity information without contacting the user, such as functional Magnetic Resonance Imaging (fMRI).

[0052] As mentioned above, when a person acts, they repeat the processes of "perception (observing the surrounding environment)," "cognitive judgment (extracting important objects)," and "action (taking action while paying attention)," and as a result, a pattern of brain activity appears in the brain in which the DMN and CEN switch via the SN. Frequent switching between the DMN and CEN means that multiple tasks (actions) are being performed one after another, and it can be determined that the user's task performance is good.

[0053] On the other hand, if switching between the DMN and CEN does not occur often, it can be assumed that multiple tasks (actions) are being performed slowly, and as a result, it can be determined that the user's task performance is poor.

[0054] Therefore, in an embodiment of the present invention, the user's task performance state is estimated by focusing on the frequency of switching between the DMN and the CEN. This method (method for determining task ability) will be described below.

[0055] First, a method using the DMN and CEN will be described. Figure 4 is an explanatory diagram for explaining estimation of task performance status using activity values ​​in the active regions of the DMN and CEN obtained based on brain activity information in an embodiment of the present invention. The explanatory diagram in Figure 4 uses the diagram of switching between the DMN and CEN in the brain process shown in the lower part of the explanatory diagram in Figure 3.

[0056] There are two other methods for determining work ability using the DMN and CEN. The first method will be explained using Figure 4(A). Figure 4(A) is an explanatory diagram that explains a method that uses the time when the activity values ​​of the DMN and CEN match.

[0057] As mentioned above, the SN is involved in the switching between the DMN and the CEN. Therefore, by checking the frequency with which the SN appears, it is possible to judge the quality of the user's task performance. The shorter the interval between SN appearances, the better the user's task performance is judged to be. Note that in Figure 4(A), as in Figure 3, the part where the SN is involved and the switching between the DMN and the CEN occurs is circled with a dashed line.

[0058] That is, the information acquisition unit 141 acquires a DMN activity value and a CEN activity value based on the brain activity information acquired from the brain activity information acquisition device 2. Here, the DMN activity value is a value indicating the amount of activity of the DMN, and the CEN activity value is a value indicating the amount of activity of the CEN.

[0059] The activity of the DMN and CEN can be detected in certain areas of the user's brain. The DMN activity areas include the posterior cingulate cortex, medial frontal gyrus, and inferior parietal lobule. The CEN activity areas include the dorsolateral prefrontal cortex and superior parietal lobule.

[0060] As such, multiple regions can be cited as active regions of the DMN and active regions of the CEN. Therefore, in order to accurately monitor the activity of each cranial neural network, it is preferable to measure all regions of the user's brain. However, since it is difficult to measure all regions, the activity of the cranial neural network is understood based on information obtained from any of the above-mentioned regions.

[0061] As explained above, the activity of cranial neural networks can be understood by measuring brain activity in active areas of the brain, such as the posterior cingulate cortex. These active areas are, so to speak, pinpoint areas in the brain where cranial neural networks function.

[0062] However, there are individual differences in brain size and brain activity areas. Therefore, pinpointing the activity areas for each user may result in inaccurate brain activity information that reflects individual differences. Therefore, as explained below, it is possible to understand the activity of the cranial neural network by measuring brain activity over a wider area rather than pinpointing the activity area of ​​the brain.

[0063] In other words, when actually using the brain activity information acquisition device 2 to acquire brain activity information in the DMN and CEN of a user, it is also possible to identify, for example, the user's forehead as an area of ​​brain activity (hereinafter, such an area will be referred to as the "forehead area" as appropriate).

[0064] Here, the forehead region of the DMN is the central part of the forehead. Here, the "central part of the forehead" is, for example, the central area when the distance between the left and right temples is divided into thirds, and can be defined as a range of about 10 cm from the space between the eyebrows toward the top of the head. On the other hand, the forehead region of the CEN is, for example, the area on the left side of the forehead as seen from the user. In other words, the "left side of the forehead" is the area on the left side of the forehead as seen from the user when the distance between the left and right temples is divided into thirds, and can be defined as a range of about 10 cm from the space between the eyebrows toward the top of the head.

[0065] Therefore, if the amount of brain activity in the center of the forehead is greater than the amount of brain activity on the left and right sides of the forehead, it can be understood as DMN activity. On the other hand, if the amount of brain activity on the left and right sides is greater than the amount of brain activity in the center of the forehead, it can be understood as CEN activity. As mentioned above, the amount of brain activity can be understood by measuring, for example, cerebral blood flow, electroencephalograms, and magnetoencephalography, which can identify patterns of brain activity.

[0066] The DMN activity value and the CEN activity value may be calculated, for example, by the calculation unit 142 described above based on the brain activity information acquired from the brain activity information acquisition device 2. Alternatively, the DMN activity value and the CEN activity value may be acquired as brain activity information from the brain activity information acquisition device 2 by the information acquisition unit 141.

[0067] Furthermore, the brain activity information used as the basis for calculating the DMN activity value and the CEN activity value is, for example, cerebral blood flow, but the maximum value of the cerebral blood flow may vary depending on the region where the DMN or CEN activity is manifested. Therefore, for example, by storing the maximum cerebral blood flow value corresponding to each brain region in advance in the storage unit 12, and performing a normalization process to divide the measured cerebral blood flow value by the stored value and obtain a numerical value that takes the maximum value 1 from the cerebral blood flow value, a more accurate activity value can be determined.

[0068] The calculation unit 142 then calculates the time t from when the DMN activity value and the CEN activity value match until the next time the DMN activity value and the CEN activity value match. As described above, the DMN and CEN in the brain process are switched depending on the user's behavior. The timing of the switch between the two can be as shown in Figure 3, when the user performs perception and cognitive judgment and moves on to an action, or when the user finishes an action and moves on to a new perception and cognitive judgment action.

[0069] When a user begins a task, perception and cognitive judgment are carried out first, as explained using the explanatory diagram in Figure 3. Therefore, during this period, the DMN has greater brain activity than the CEN. However, during the transitional period when the user moves from perception and cognitive judgment to action, as the user's concentration gradually increases, the activity of the DMN gradually decreases and the activity of the CEN increases.

[0070] Therefore, during this transitional period, when the DMN and CEN switch, there is a time when the DMN activity value and the CEN activity value match. The time when the DMN and CEN first switch (when the solid line and the dashed line intersect) is designated as "t1" as shown in the explanatory diagram of Figure 4.

[0071] That is, the calculation unit 142 recognizes the time point at which the DMN and CEN switch during the transitional period from perception and cognitive judgment to action as "time t1." Similarly, the calculation unit 142 recognizes the time point at which the CEN and DMN switch during the next transitional period from action to perception and cognitive judgment as "time t2." Then, the calculation unit 142 calculates the time difference dt between time t1 and time t2.

[0072] The comparison unit 143 then compares the time difference dt calculated by the calculation unit 142 with a preset time threshold Tth. The comparison result is sent to the determination unit 144, which determines that the user's task performance state is good if the time difference dt is smaller than the time threshold Tth.

[0073] As described above, the time t indicates the time when brain activity in the DMN and CEN switches, and the SN is involved in this switching. The more frequently the SN appears, the more frequently the brain activity in the DMN and CEN switches. The more frequently the SN appears, the shorter the time difference dt between time t1 and time t2 calculated by the calculation unit 142.

[0074] Thus, the shorter the time difference dt, the higher the frequency of SN appearance, indicating that the user is switching between DMN and CEN brain activity. Therefore, if the time difference dt is smaller than the time threshold Tth, it can be determined that the user's task performance is good.

[0075] On the other hand, when the comparison result between the time difference dt and the time threshold value Tth by the comparison unit 143 indicates that the time difference dt is greater than the time threshold value Tth, the determination unit 144 determines that the task performance state of the user is poor.

[0076] Next, we will use Figure 4(B) to explain a second method that uses the DMN and CEN to estimate a user's task performance state based on the frequency of switching between the DMN and CEN. Figure 4(B) is an explanatory diagram that explains a method that uses the average value of the two. Like Figure 4(A), Figure 4(B) also shows the increase and decrease in activity of the DMN and CEN depending on the user's task, with the horizontal axis representing time and the vertical axis representing brain activity.

[0077] The method for determining work capacity here uses the average DMN activity value measured over a predetermined time period and the average CEN activity value measured over a predetermined time period. Note that the calculation of the average values ​​may involve normalization as described above.

[0078] Here, the "predetermined time" can be set arbitrarily, but is a time long enough to average the DMN activity value and the CEN activity value. Therefore, a sufficient time is required for multiple switching of brain activity between the DMN and the CEN. The set predetermined time is stored in, for example, the storage unit 12.

[0079] For example, the calculation unit 142 calculates a DMN activity value and a CEN activity value based on the brain activity information acquired by the information acquisition unit 141 from the brain activity information acquisition device 2. From these multiple DMN activity values ​​and CEN activity values, the calculation unit 142 calculates the average values ​​of the DMN activity value and the CEN activity value.

[0080] The calculation unit 142 calculates the average values ​​of the DMN activity value and the CEN activity value for the following reason: When a change in brain activity pattern is detected using cerebral blood flow as described above, it takes more time to grasp the change than when it is detected as neural activity, for example.

[0081] Therefore, by using the average values ​​of the DMN activity value and the CEN activity value instead of the activity value itself, it is possible to shorten the time required to determine the work capacity without missing any brain activity information.Furthermore, the calculation unit 142 calculates the difference dA between the respective average values.

[0082] The comparison unit 143 compares the difference dA between the average values ​​calculated by the calculation unit 142 with a preset average threshold Dth1. It is more preferable that the comparison unit 143 compares the absolute value of the difference dA between the average values ​​of the DMN activity value and the CEN activity value with the average threshold Dth1.

[0083] If the comparison by the comparison unit 143 determines that the absolute value of the difference dA between the average values ​​is smaller than the average threshold Dth1, the determination unit 144 determines that the user's task performance state is good. If the difference dA between the average values ​​is smaller than the average threshold Dth1, this means that the difference between the DMN activity value and the CEN activity value is small.

[0084] In other words, the pattern of brain activity over a given time period is such that the DMN and CEN are roughly equivalent in activity, and therefore, in such cases, switching between the DMN and CEN is likely to be smooth and frequent.

[0085] On the other hand, if the difference dA between the average values ​​is equal to or greater than the average threshold Dth1, it indicates that one of the DMN and CEN has a larger value than the other. That is, as a pattern of brain activity over a given time, there is a bias in brain activity between the DMN and CEN. Therefore, in such a case, switching between the DMN and CEN is not performed smoothly, and the frequency of switching is considered to be low. Therefore, in such a case, the determination unit 144 determines that the user's task performance state is poor.

[0086] The method for estimating a user's task performance status based on the frequency of switching between the DMN and CEN (method for determining task ability) described above uses brain activity of the DMN and CEN. However, as mentioned above, the switching between the DMN and CEN requires the involvement of the SN. Therefore, the following describes a method for determining the quality of a user's task performance status using the SN.

[0087] Here, examples of active areas of the SN include the anterior cingulate gyrus, middle frontal gyrus, and insular cortex. Meanwhile, the forehead region of the SN is, for example, the region on the right side of the forehead as seen from the user, or the right side of the forehead and the central region of the forehead. Here, the "right side of the forehead" refers to the region on the right side of the forehead as seen from the user when the distance between the left and right temples is divided into three equal parts, and can be defined as a range of approximately 10 cm from the space between the eyebrows toward the top of the head.

[0088] Therefore, if the brain activity in the right area of ​​the forehead, or in the right and central areas of the forehead, is greater than the brain activity in the left area of ​​the forehead, it can be determined to be SN activity. The brain activity can be determined by measuring, for example, cerebral blood flow, electroencephalograms, and magnetoencephalography, which can determine the pattern of brain activity, as described above.

[0089] As mentioned above, the SN activity value may be calculated, for example, by the calculation unit 142 based on the brain activity information acquired from the brain activity information acquisition device 2, or may be acquired by the information acquisition unit 141 as brain activity information in the form of an SN activity value from the brain activity information acquisition device 2.

[0090] The comparison unit 143 compares the SN activity value Ss acquired by the information acquisition unit 141 with a preset SN threshold value Sth. Based on the comparison result by the comparison unit 143, if the determination unit 144 determines that the SN activity value Ss indicates a value equal to or greater than the SN threshold value Sth, it determines that the user's task performance state is good.

[0091] The fact that the SN activity value Ss is equal to or greater than the SN threshold Sth indicates that the brain activity patterns in the DMN and CEN are frequently switched, and therefore the determination unit 144 determines that the user's task performance state is good.

[0092] On the other hand, if it is determined that the SN activity value Ss is smaller than the SN threshold Sth, the determination unit 144 determines that the user's task performance state is poor, because this indicates that the brain activity patterns in the DMN and CEN are not switching very often.

[0093] Another method for determining the quality of a user's task performance using the SN is to use the brain activity value of the SN for a predetermined time, as explained with reference to the explanatory diagram in Fig. 4(B). The information acquisition unit 141 acquires the brain activity value SS, which is the result of measuring the activity of the SN for a predetermined time, from the brain activity information acquisition device 2.

[0094] The calculation unit 142 calculates the average value As of the brain activity value SS. The larger the average value As, the more frequently the DMN and CEN are switched. The comparison unit 143 compares the average value As with an average threshold Dth2.

[0095] If the determination unit 144 determines that the average value As is equal to or greater than the average threshold value Dth2, it determines that the user's task performance state is good. On the other hand, if the determination unit 144 determines that the average value As is smaller than the average threshold value Dth2, it determines that the user's task performance state is bad.

[0096] In the above explanation, as a method for estimating a user's task performance state based on the frequency of switching between the DMN and CEN, a case where brain activity information showing the pattern of brain activity in the DMN and CEN is used, and a case where brain activity information showing the pattern of brain activity in the SN is used, have been explained. Furthermore, in these methods, when DMN, CEN, or SN is obtained as brain activity information from the user, no stimulation is applied to the user.

[0097] In contrast, in the method for estimating a user's task performance state described below, a stimulus is applied to the user when acquiring brain activity information. The task performance state is then judged based on the difference between when the stimulus is applied and when it is not applied. Note that the target to which the stimulus is applied here is the user's vagus nerve.

[0098] Specifically, while the user is performing a task, a stimulus adding unit (not shown as a component of the task ability determination device 1 in Fig. 1) controls the stimulus adding device 3 to add a stimulus to the user. Alternatively, the task state induction unit 16 may be used to control the stimulus adding device 3.

[0099] The brain activity information acquisition device 2 acquires a brain activity value N when a stimulus is applied to the user. After that, the stimulus application device 3 stops applying the stimulus to the user. This creates a state in which no stimulus is applied to the user. Therefore, the brain activity information acquisition device 2 acquires a brain activity value F when no stimulus is applied to the user.

[0100] The information acquiring unit 141 acquires, as brain activity information, information on the brain activity value N when a stimulus is applied to the user and the brain activity value F when no stimulus is applied to the user, which information has been acquired by the brain activity information acquiring device 2. The calculating unit 142 calculates the difference (activity difference dV) between the acquired brain activity value N and brain activity value F.

[0101] The comparison unit 143 compares the activity difference dV calculated by the calculation unit 142 with a preset activity difference threshold Vth. If the determination unit 144 determines as a result of the comparison that the activity difference dV indicates a value equal to or less than the activity difference threshold Vth, it determines that the user's task performance state is good.

[0102] This is based on the premise that adding stimuli to the user can improve the state of task performance, as will be described later. For example, when looking at the amount of activity of the SN, if the amount of activity without stimuli is comparable to the amount of activity with stimuli, it can be considered that the brain activity pattern is smoothly switched regardless of the presence or absence of stimuli.

[0103] Therefore, the smaller the difference between the activity values ​​of the brain activity when a stimulus is applied to the user and when it is not, the more frequently the brain activity patterns are switched, and the better the user's task performance state. Therefore, as described above, when the activity difference dV is equal to or less than the activity difference threshold Vth, the user's task performance state is determined to be good.

[0104] Conversely, if there is a large difference in the activity values ​​of brain activity depending on whether or not the user is stimulated (if the activity difference dV is greater than the activity difference threshold Vth), the switching of brain activity patterns is not smooth, and the user's task performance is judged to be poor.

[0105] The estimation process of the user's task performance state has been described above. Next, the task performance state guidance process for improving the task performance state will be described. The guidance process performed here is for improving the task performance state, and includes two cases. That is, when the user's task performance state is originally good, guidance is provided in a direction that can further improve the task performance state, and when the user's task performance state is determined to be poor, guidance is provided to a better task performance state.

[0106] The task state induction unit 16 acquires the estimation result of the task performance state of the user estimated by the task performance state estimation unit 14. Then, it determines whether or not induction of the task performance state is required. Note that the task performance state induction process is not a required process, but may be a process that is executed as needed, or may be a required process.

[0107] Therefore, if the task state induction unit 16 determines that induction of a task performance state is required, a process for guiding the task performance state of the user in a positive direction is executed. Specifically, the task state induction unit 16 instructs the stimulus application device 3 to apply a stimulus to the user.

[0108] The stimulation device 3 stimulates the user using the above-mentioned methods, such as transcranial brain stimulation, a method of stimulating peripheral nerves, or biofeedback, based on instructions from the task state induction unit 16. On the other hand, if it is determined that no induction process is required, no induction process is performed on the user.

[0109] [Operation] Next, the flow of processing for determining the working ability of a person to be determined by the working ability determination device 1 will be described appropriately using the flowcharts shown in Fig. 5 to Fig. 12. Fig. 5 is a flowchart showing the basic flow of the method for determining the working ability of a user in the first embodiment of the present invention.

[0110] The process of determining the user's work ability begins when the brain activity detection unit 11 of the work ability determination device 1 acquires brain activity information from the brain activity information acquisition device 2 (ST1). Then, based on the acquired brain activity information, the work performance state estimation unit 14 determines whether the work performance state is good or bad (ST2).

[0111] After the task performance state estimation unit 14 determines whether the task performance state of the user is good or bad, the result is notified to the user via the task performance state output unit 15 (ST3). Then, task performance state guidance processing is executed to guide the user to a better task performance state as needed (ST4).

[0112] As mentioned above, various methods are possible for estimating the quality of a user's task performance. The flow of each estimation method explained so far will now be explained in order. Figures 6 and 7 are flowcharts explaining a first method using DMN and CEN for determining a user's task ability in an embodiment of the present invention.

[0113] The estimation method described using Figures 6 and 7 is a method of estimating a user's work performance state based on the frequency of switching between DMN and CEN (a method of determining work ability), and is a technique that uses the time when the activity values ​​of DMN and CEN match.

[0114] The information acquisition unit 141 of the task performance state estimation unit 14 first acquires information on brain activity in the DMN from the brain activity information acquisition device 2 via the brain activity detection unit 11 (ST11). Hereinafter, the information on brain activity in the activated areas of the DMN, which corresponds to the brain activity information here, will be referred to as the DMN brain activity value. Therefore, the information acquisition unit 141 first acquires the DMN activity value Sd1.

[0115] Additionally, the information acquiring unit 141 acquires information on brain activity in the CEN from the brain activity information acquiring device 2 (ST12). Hereinafter, the information on brain activity in the active area of ​​the CEN, which corresponds to the brain activity information here, will be referred to as a CEN brain activity value. Therefore, the information acquiring unit 141 first acquires a CEN activity value Sc1.

[0116] For convenience of explanation, the information acquisition unit 141 acquires the DMN activity value Sd1 first, and then acquires the CEN activity value Sc1 to acquire each piece of brain activity information. However, either the DMN activity value Sd1 or the CEN activity value Sc1 may be acquired first, or both may be acquired simultaneously.

[0117] The comparison unit 143 compares the acquired DMN activity value Sd1 and CEN activity value Sc1 to determine whether or not they match (ST13). If the determination unit 144 determines that the DMN activity value Sd1 and the CEN activity value Sc1 do not match (NO in ST13), the process returns to step ST11 to acquire the DMN activity value Sd1 and the CEN activity value Sc1 again.

[0118] On the other hand, if the judgment unit 144 determines that the DMN activity value Sd1 and the CEN activity value Sc1 match (YES in ST13), the time t1 when the DMN activity value Sd1 and the CEN activity value Sc1 match is obtained (ST14).

[0119] Subsequently, the information acquiring unit 141 acquires information on brain activity in the DMN as a brain activity value Sd2 from the brain activity information acquiring device 2 (ST15).The information acquiring unit 141 also acquires information on brain activity in the CEN as a brain activity value Sc2 (ST16).

[0120] The comparison unit 143 compares the acquired DMN activity value Sd2 and CEN activity value Sc2 to determine whether or not they match (ST17). If the determination unit 144 determines that the DMN activity value Sd2 and the CEN activity value Sc2 do not match (NO in ST17), the process returns to step ST15 to acquire the DMN activity value Sd2 and the CEN activity value Sc2 again.

[0121] On the other hand, if the judgment unit 144 determines that the DMN activity value Sd2 and the CEN activity value Sc2 match (YES in ST17), the time t2 when the DMN activity value Sd2 and the CEN activity value Sc2 match is obtained (ST18).

[0122] Through the above process, the determination unit 144 has acquired two times t1 and t2 at which the DMN activity value and the CEN activity value match. The determination unit 144 then calculates the time difference dt between times t1 and t2 (ST19 in FIG. 7). The determination unit 144 then compares the time difference dt with a preset time threshold Tth (ST20).

[0123] If the determination unit 144 determines that the time difference dt is smaller than the time threshold Tth (YES in ST20), it outputs that the user's task performance state is good via the task performance state output unit 15 (ST21).

[0124] On the other hand, if the determination unit 144 determines that the time difference dt is equal to or greater than the time threshold value Tth (NO in ST20), it outputs via the task performance status output unit 15 that the task performance status of the user is poor (ST22).

[0125] Next, we will explain a method for estimating a user's work performance state based on the frequency of switching between the DMN and CEN (method for determining work ability), which uses the average activity values ​​of the DMN and CEN over a predetermined time period. Figure 8 is a flowchart explaining a second method for determining a user's work ability using the DMN and CEN in an embodiment of the present invention.

[0126] The information acquiring unit 141 acquires a plurality of DMN brain activity values ​​SD when the brain activity of the DMN is measured for a predetermined time from the brain activity information acquiring device 2 (ST31). In addition, the information acquiring unit 141 also acquires a plurality of CEN brain activity values ​​SC when the brain activity of the CEN is measured for a predetermined time from the brain activity information acquiring device 2 (ST32).

[0127] The calculation unit 142 calculates the average value Ad of the brain activity values ​​SD of the multiple DMNs acquired by the information acquisition unit 141 (ST33), and also calculates the average value Ac of the brain activity values ​​SC of the multiple CENs (ST34).

[0128] The order in which the information acquiring unit 141 acquires the brain activity values ​​and the order in which the calculating unit 142 calculates the average values ​​do not necessarily have to be as described above. For example, the brain activity value SC of the CEN may be acquired before the activity value SD of the DMN, or they may be acquired in parallel.

[0129] The calculation unit 142 further calculates the difference dA between the calculated average values ​​Ad and Ac (ST35). The comparison unit 143 compares the difference dA between the average values ​​with an average threshold Dth1 that is set in advance and stored in the storage unit 12 (ST36). As described above, it is more preferable to use an absolute value for the difference dA.

[0130] If the comparison by the comparison unit 143 determines that the absolute value of the difference dA in the average values ​​is smaller than the average threshold Dth1 (YES in ST36), the determination unit 144 determines that the user's task performance state is good (ST37). On the other hand, if the difference dA in the average values ​​is equal to or greater than the average threshold Dth1 (NO in ST36), the determination unit 144 determines that the user's task performance state is bad (ST38).

[0131] Next, a method for determining the quality of a user's task performance using an SN will be described with reference to Fig. 9. Fig. 9 is a flowchart illustrating a first method using a salience network (SN) for determining a user's task performance in an embodiment of the present invention.

[0132] First, the information acquiring unit 141 acquires a brain activity value Ss in the active area of ​​the SN from the brain activity information acquiring device 2 (ST41). The acquired brain activity value Ss is transmitted to the comparing unit 143, which then compares the SN activity value Ss with an SN threshold value Sth that is preset and stored in the storage unit 12 (ST42).

[0133] Then, based on the comparison result in the comparison unit 143, if the judgment unit 144 determines that the SN activity value Ss indicates a value greater than or equal to the SN threshold value Sth (YES in ST42), it judges that the user's task performance status is good (ST43).

[0134] On the other hand, if the determination unit 144 determines that the SN activity value Ss indicates a value smaller than the SN threshold value Sth (NO in ST42), it determines that the user's task performance state is poor (ST44).

[0135] Next, a method of using the average value As of the brain activity value SS as a method for estimating the quality of a user's task performance by focusing on the activity of the SN as a brain activity pattern will be described with reference to Fig. 10. Fig. 10 is a flowchart illustrating a second method using the SN as a method for determining a user's task ability in an embodiment of the present invention.

[0136] First, the information acquiring unit 141 acquires a plurality of brain activity values ​​SS, which are the results of measuring the activity of the SN for a predetermined time, from the brain activity information acquiring device 2 (ST51). Then, the calculating unit 142 calculates the average value As of the plurality of brain activity values ​​SS (ST52). The comparing unit 143 compares the average value As with an average threshold Dth2 that is preset and stored in the storage unit 12 (ST53).

[0137] If the determination unit 144 determines that the average value As is equal to or greater than the average threshold value Dth2 (YES in ST53), it determines that the user's task performance state is good (ST54). On the other hand, if the determination unit 144 determines that the average value As is smaller than the average threshold value Dth2 (NO in ST53), it determines that the user's task performance state is bad (ST55).

[0138] Finally, the flow of a method for estimating a user's task performance state, which determines the quality of task performance state based on the difference between when a stimulus is applied and when no stimulus is applied, will be described with reference to Fig. 11. Fig. 11 is a flowchart illustrating a method for determining a user's task ability using the difference in brain activity values ​​in an embodiment of the present invention.

[0139] First, while the user is performing a task, the stimulus applying device 3 applies a stimulus to the user (ST61). Then, the brain activity information acquiring device 2 acquires a brain activity value N when the stimulus is applied to the user (ST62).

[0140] Thereafter, the stimulation device 3 stops providing stimulation to the user (ST63). The brain activity information acquisition device 2 acquires the brain activity value F when no stimulation is provided to the user (ST64).

[0141] Although the acquisition of brain activity values ​​N and F is performed in the order described above, the order of first applying a stimulus to the user to obtain brain activity value N, or first obtaining brain activity value F without applying a stimulus to the user, can be any order.

[0142] The information acquiring unit 141 acquires, as brain activity information, information on the brain activity value N and the brain activity value F acquired by the brain activity information acquiring device 2. The calculating unit 142 calculates the difference (activity difference dV) between the acquired brain activity value N and brain activity value F (ST65).

[0143] The comparison unit 143 compares the activity difference dV calculated by the calculation unit 142 with the activity difference threshold Vth that is set in advance and stored in the storage unit 12 (ST66). If the determination unit 144 determines as a result of the comparison that the activity difference dV indicates a value equal to or less than the activity difference threshold Vth (YES in ST66), it determines that the user's task performance state is good (ST67).

[0144] Conversely, if the judgment unit 144 determines that the activity difference dV is greater than the activity difference threshold Vth due to the presence or absence of stimulation to the user (NO in ST66), the switching of brain activity patterns is not smooth, and therefore the user's task performance state is judged to be poor (ST68).

[0145] The above has described the flow of determining whether the user's task performance state is good or bad by the task performance state estimation unit 14. After the determination process is performed, the task performance state induction unit 16 executes a process to further improve the user's task performance state, if necessary.

[0146] Next, a process flow for further improving the task performance state of the user will be described below. Fig. 12 is a flowchart showing the process flow for guiding the task performance state in an embodiment of the present invention.

[0147] The task state induction unit 16 acquires the estimation result of the task performance state of the user estimated by the task performance state estimation unit 14 (ST71). Furthermore, the task state induction unit 16 determines whether or not induction of the task performance state is required (ST72).

[0148] Therefore, if the task state induction unit 16 determines that induction of a task performance state is required (YES in ST72), a process is executed to induce the task performance state of the user in a good direction (ST73). Specifically, the task state induction unit 16 instructs the stimulus application device 3 to apply a stimulus to the user.

[0149] On the other hand, if the task state guidance unit 16 determines that guidance of the task performance state is not required (NO in ST72), the process of guiding the task performance state of the user in a good direction is not executed.

[0150] (Second embodiment) Next, a second embodiment of the present invention will be described. In the second embodiment, the same components as those described in the first embodiment are denoted by the same reference numerals, and redundant descriptions of the same components will be omitted.

[0151] The method for estimating the user's task performance status explained so far can be applied to users of all ages and genders. As explained using the explanatory diagram in Figure 3, the method for estimating the user's task performance status focuses on the frequency of switching between the DMN and the CEN.

[0152] However, as people get older, the switching between brain activity in the DMN and CEN does not occur smoothly, which means that they tend to remain in a state of distraction for a long time. Here, "a state of distraction" refers to a state in which people focus their attention on unimportant things, and may miss something that is truly important.

[0153] As explained using the diagram in Figure 3, during the task of perception and cognitive judgment, the brain processes are in a state where the DMN, a neural network that works when searching for important things in the surrounding environment, is active. Then, as the task progresses, the brain processes gradually shift from the DMN to the CEN, a neural network that works when focusing attention on important things.

[0154] On the other hand, when we are distracted, the proportion of the DMN is large when we first perceive something, but because we focus our attention on something unimportant, the DMN and CEN switch over at the time of the cognitive judgment process, and the CEN state continues for a long time afterwards.

[0155] Therefore, it is thought that by allowing brain processes that tend to be fixed in the CEN state to switch frequently between the DMN and CEN, the switching between the DMN and CEN will be smoother.

[0156] Therefore, in the second embodiment of the present invention, before executing the process of estimating the quality of the user's task performance, it is first determined whether the brain function is aging. If it is determined that the brain function is aging, the task performance of the elderly person is guided to a better state by frequently switching between the DMN and the CEN.

[0157] 13 is a block diagram showing the internal configuration of a work performance state estimating unit 14A in the work ability discrimination device 1 according to the second embodiment of the present invention. The difference from the work ability discrimination device 1 according to the first embodiment is that an aging state estimating unit 145 is provided between the information acquiring unit 141 and the calculating unit 142.

[0158] The aging state estimation unit 145 estimates whether the brain function of a user who is the subject of estimation of the task performance state is aging or not. There are various possible methods for this estimation, such as a method using the Hasegawa Dementia Scale or a method using the results of using a brain training application.

[0159] Taking the case of using the Hasegawa Dementia Scale as an example, before estimating the quality of the task performance state, the user answers questions using the Hasegawa Dementia Scale in advance, and the answers are stored in, for example, the storage unit 12. Triggered by the information acquisition unit 141 acquiring brain activity information from the brain activity information acquisition device 2, the aging state estimation unit 145 acquires, from the storage unit 12, answers related to the user who is the target of estimation of the quality of the task performance state.

[0160] The aging state estimation unit 145 estimates the aging state of the user from the obtained answers. As a method for estimating the aging state, for example, a method of converting the obtained answers into numerical values ​​and comparing them with a preset threshold value can be adopted.

[0161] The estimation result from the aging state estimation unit 145 is transmitted to the calculation unit 142 or the comparison unit 143, and the estimation of the quality of the task performance state described above is executed. Note that, for example, the estimation of the quality of the task performance state described above may be executed only for users whose brain function is estimated to be aging by the aging state estimation unit 145.

[0162] 14 is a flowchart showing the basic flow of the method for determining a user's task ability in the second embodiment of the present invention. As described above, after the information acquisition unit 141 acquires brain activity information from the brain activity information acquisition device 2 (ST1), the aging state estimation unit 145 estimates the aging state (ST5). After the aging state estimation is performed, various processes, such as the judgment of the user's task performance state by the task performance state estimation unit 14 described above, are performed (ST3, ST4).

[0163] [Effects of the Example] (1) A method for determining work ability includes a step of acquiring brain activity information of a user, and a step of estimating the user's work performance state based on the frequency of switching between the default mode network (DMN) and the central executive network (CEN) obtained based on the acquired brain activity information.

[0164] By employing such a determination method, it is possible to accurately estimate the task performance state of the user using the user's brain activity information.

[0165] (2) The step of estimating the user's work performance state in the work ability determination method described in (1) above includes the steps of acquiring DMN activity values ​​in the active areas of the DMN obtained based on the acquired brain activity information, acquiring CEN activity values ​​in the active areas of the CEN obtained based on the acquired brain activity information, calculating the time from when the DMN activity value and the CEN activity value match until the next time when the DMN activity value and the CEN activity value match, comparing the time with a predetermined time threshold, and determining that the user's work performance state is good if the time is determined to be a value smaller than the time threshold.

[0166] By adopting a method for estimating a user's task performance state based on the frequency of switching between DMN and CEN in this way, it is possible to more accurately estimate the quality of a user's task performance state.

[0167] (3) The step of estimating the user's work performance state in the work ability determination method described in (1) above includes the steps of acquiring DMN activity values ​​measured for a predetermined period of time from the DMN obtained based on the acquired brain activity information, acquiring CEN activity values ​​measured for a predetermined period of time from the CEN obtained based on the acquired brain activity information, calculating the average DMN activity values ​​and the average CEN activity values, comparing the difference between the average DMN activity values ​​and the average CEN activity values ​​with a predetermined average threshold, and determining that the user's work performance state is good if the difference is determined to be smaller than the average threshold.

[0168] In this method of estimating a user's task performance status based on the frequency of switching between the DMN and CEN, the average values ​​of the DMN activity value and the CEN activity value are used, thereby shortening the time required to determine task ability without missing any brain activity information.

[0169] (4) The step of estimating the user's work performance state in the work ability determination method described in (1) above includes the steps of acquiring an SN activity value in an active area of ​​the salience network (SN) obtained based on the acquired brain activity information, comparing the acquired SN activity value with a predetermined SN threshold, and determining that the user's work performance state is good if the SN activity value is determined to be equal to or greater than the SN threshold.

[0170] In a method for estimating a user's task performance state based on the frequency of switching between the DMN and CEN, the activity value of the SN, which controls the switching between the DMN and CEN, can be used to more easily estimate the user's task performance state.

[0171] (5) The step of estimating the user's work performance state in the method for determining work ability described in (1) above includes the steps of acquiring SN activity values ​​in active areas of the salience network (SN) obtained based on the acquired brain activity information, calculating the average value of the SN activity values, comparing the average value with a predetermined average threshold, and judging the user's work performance state to be good if the average value is determined to be smaller than the average threshold.

[0172] In a method for estimating a user's task performance state based on the frequency of switching between DMN and CEN, by using the activity value of the SN, which controls switching between DMN and CEN, it is possible to shorten the time required to determine task ability without missing any brain activity information, and to more easily estimate the user's task performance state.

[0173] (6) The step of estimating the user's work performance state in the method for determining work ability described in (1) above includes the steps of acquiring brain activity information when a stimulus is applied to the user, acquiring brain activity information when no stimulus is applied to the user, calculating the activity difference between the brain activity information when the stimulus is applied and the brain activity information when no stimulus is applied, comparing the activity difference with a predetermined activity difference threshold, and determining that the user's work performance state is good if it is determined that the activity difference is equal to or less than the activity difference threshold.

[0174] When estimating the user's task performance status, by using the difference in activity of brain activity information depending on whether or not a stimulus is applied, the location that reacts to the application of a stimulus is limited to a specific area, such as the SN region, and therefore the user's task performance status can be estimated more accurately.

[0175] (7) In the method for determining work ability described in any of (1) to (6) above, a step of determining whether brain function is aging or not is included before the step of estimating the user's work performance state, and if it is determined that the brain function is aging, an estimation process of the user's work performance state is executed.

[0176] By estimating the aging state in this way, it is possible to quantify and understand the state of distraction in elderly users. Furthermore, by subsequently estimating the quality of the user's task performance, it is possible to guide the user to improve their task performance.

[0177] (8) In the method for determining work ability described in any of (1) to (7) above, after the step of estimating the user's work performance state, there is further provided a step of guiding the user's work performance state, wherein the step of guiding the work performance state includes a step of acquiring information on the estimated user's work performance state, a step of determining whether or not it is necessary to guide the user to a work performance state, and a step of guiding the user to a work performance state when it is determined that guidance is necessary.

[0178] After estimating the quality of the user's task performance state in this way, the task performance state can be further improved by guiding the user's task performance state in a better direction.

[0179] (9) The work ability discrimination device includes a brain activity detection unit that acquires the user's brain activity information, and a work performance state estimation unit that estimates the user's work performance state based on the frequency of switching between the default mode network (DMN) and the central executive network (CEN) obtained based on the acquired brain activity information.

[0180] By employing such a discrimination device, it is possible to accurately estimate and provide the user's task performance state using the user's brain activity information.

[0181] (10) The work ability discrimination device described in (9) above is provided with a work state induction unit that induces the user to enter a work performance state after the user's work performance state is estimated by the work performance state estimation unit.

[0182] After estimating the quality of the user's task performance state in this way, the task performance state can be further improved by guiding the user's task performance state in a better direction. [Explanation of symbols]

[0183] 1. Work ability discrimination device, 11. Brain activity detection unit, 12. Memory unit, 13. Control unit, 14. Work performance state estimation unit, 141. Information acquisition unit, 142. Calculation unit, 143. Comparison unit, 144. Determination unit, 145. Aging state estimation unit, 15. Work performance state output unit, 16. Work state induction unit, 2. Brain activity information acquisition device, 3. Stimulation application device

Claims

1. acquiring brain activity information of a user; A step of estimating the user's task performance state based on the frequency of switching between the default mode network (DMN) and the central executive network (CEN) obtained based on the acquired brain activity information; A method for determining work ability, comprising:

2. The step of estimating the task performance state of the user includes: A step of acquiring a DMN activity value in an active region of the DMN obtained based on the acquired brain activity information; acquiring a CEN activity value at an active site of the CEN based on the acquired brain activity information; Calculating the time from when the DMN activity value and the CEN activity value match until the next time when the DMN activity value and the CEN activity value match; comparing the time to a preset time threshold; determining that the user's task performance status is good when the time is determined to be smaller than the time threshold; 2. The method for determining working ability according to claim 1, further comprising:

3. The step of estimating the task performance state of the user includes: A step of acquiring a DMN activity value measured for a predetermined time period based on the acquired brain activity information; acquiring a CEN activity value measured for a predetermined period of time based on the acquired brain activity information; Calculating an average value of the DMN activity value and an average value of the CEN activity value; comparing a difference between the average value of the DMN activity value and the average value of the CEN activity value with a preset average threshold; determining that the task performance state of the user is good when the difference is determined to be smaller than the average threshold value; 2. The method for determining working ability according to claim 1, further comprising:

4. The step of estimating the task performance state of the user includes: A step of acquiring an SN activity value in an active region of the salience network (SN) obtained based on the acquired brain activity information; comparing the obtained SN activity value with a preset SN threshold; determining that the user's task performance state is good when the SN activity value is determined to be equal to or greater than the SN threshold; 2. The method for determining working ability according to claim 1, further comprising:

5. The step of estimating the task performance state of the user includes: A step of acquiring an SN activity value in an active region of the salience network (SN) obtained based on the acquired brain activity information; calculating an average value of the SN activity values; comparing the average value with a preset average threshold value; determining that the task performance state of the user is good when it is determined that the average value is equal to or greater than the average threshold value; 2. The method for determining working ability according to claim 1, further comprising:

6. The step of estimating the task performance state of the user includes: acquiring the brain activity information when a stimulus is applied to the user; acquiring the brain activity information when no stimulus is applied to the user; calculating an activity difference between the brain activity information when a stimulus is applied and the brain activity information when no stimulus is applied; comparing the activity difference with a preset activity difference threshold; determining that the task performance state of the user is good when it is determined that the activity difference is equal to or less than the activity difference threshold; 2. The method for determining working ability according to claim 1, further comprising:

7. The method for determining task ability includes, before the step of estimating the task performance state of the user, a step of determining whether brain function is aging, 2. The method for determining working ability according to claim 1, further comprising the step of: executing a process for estimating the user's working performance state when it is determined that the brain function has aged.

8. The method for determining work ability further includes, after the step of estimating the work performance state of the user, a step of guiding the work performance state of the user, The step of inducing a work performance state includes: acquiring information on the estimated task performance state of the user; determining whether it is necessary to guide the user to the task performance state; When it is determined that guidance is necessary, a step of performing guidance on the task performance state for the user; 8. The method for determining working ability according to claim 1, further comprising:

9. a brain activity detection unit that acquires brain activity information of a user; a task performance state estimation unit that estimates the task performance state of the user based on the frequency of switching between a default mode network (DMN) and a central executive network (CEN) obtained based on the acquired brain activity information; A working ability determination device comprising:

10. 10. The work ability determination device according to claim 9, further comprising a work state induction unit that induces the user to enter the work performance state after the work performance state estimation unit estimates the user's work performance state.

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