Work ability determining method and work ability determining apparatus

Abstract

To accurately estimate a work 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 state of the user based on the acquired brain activity information, and the step of estimating the work state of the user is a step of estimating an arousal level of the user. The step (ST2) of estimating the arousal level of the user includes a step (ST11) of acquiring a central executive network (CEN) activity value at an active site of a CEN obtained based on the acquired brain activity information, a step (ST12) of comparing the acquired CEN activity value with a preset arousal level threshold value, and a step (ST12) of determining that the work state of the user is a suitable state when the CEN activity value is determined to be equal to or greater than the arousal level threshold value (YES in ST13).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 an auricular nerve stimulation device used to stimulate a user's vagus nerve. Specifically, the device includes at least two electrodes designed to be placed in the concha and the cavity of the concha. When a voltage difference is applied between the electrodes, the electrodes stimulate the nerve branches in the concha and the cavity of the concha, respectively. This can effectively switch from a non-focused state to a focused state. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Special Publication No. 2023-503291 Summary of the Invention [Problem to be solved by the invention]

[0004] However, stimulating the user's vagus nerve, as in the technology disclosed in the aforementioned Patent Document 1, can also have a relaxing effect, moving the user from a state of concentration to a state of disconcentration. In particular, when the user is in a state where their body is seeking rest, such as when they are sleep-deprived or fatigued, stimulating the vagus nerve puts the user in a relaxed state.

[0005] In this way, when stimulating the vagus nerve to improve the user's working condition, depending on the user's physical condition, the user may be induced into a relaxed state, i.e., a state of reduced alertness that interferes with work. Furthermore, in the invention disclosed in the above-mentioned Patent Document 1, it is difficult to accurately determine the user's condition, so stimulating the vagus nerve without understanding the user's condition may result in an effect different from the desired effect.

[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 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 a step of acquiring brain activity information of a user and a step of estimating the user's work state based on the acquired brain activity information, and the step of estimating the user's work state is a step of estimating the user's level of alertness.

[0008] The work ability determination device in the embodiment includes a brain activity detection unit that acquires brain activity information of a user, and a work state estimation unit that estimates the user's level of alertness based on the acquired brain activity information and estimates the user's work state from the level of alertness. [Effects of the Invention]

[0009] Since the present invention employs such a configuration, it is possible to accurately estimate the user's working state 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 an internal configuration of a work state estimation unit according to the embodiment of the present invention. [Figure 3] FIG. 10 is an explanatory diagram relating to the level of alertness of a user. [Figure 4] FIG. 1 is an explanatory diagram showing the relationship between a user's actions and behavior and brain processes. [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] 4 is a flowchart illustrating a method for determining a user's working ability using arousal level according to the first embodiment of the present invention. [Figure 7] 4 is a flowchart showing a flow of guidance for a task execution state in the first embodiment of the present invention. [Figure 8] FIG. 10 is an explanatory diagram showing the concept of a method for determining work capacity according to a second embodiment of the present invention. [Figure 9] FIG. 10 is an explanatory diagram for explaining estimation of task performance status using activity values ​​in the active regions of the default mode network (DMN) and the central executive network (CEN) obtained based on brain activity information in the second embodiment of the present invention, and explains a method using the time when the activity values ​​of both are the same. [Figure 10] 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. [Figure 11] 10 is a flowchart illustrating a method for determining a user's task ability using DMN and CEN in a second embodiment of the present invention. [Figure 12] 10 is a flowchart illustrating a method for determining a user's task ability using DMN and CEN in a second embodiment of the present invention. [Figure 13] 10 is a flowchart illustrating a second method using DMN and CEN for determining a user's task ability in the second embodiment of the present invention. [Figure 14]10 is a flowchart illustrating a first method using a salience network (SN) for determining a user's working ability in the second embodiment of the present invention. [Figure 15] 10 is a flowchart illustrating a second method using an SN for determining a user's working ability in the second embodiment of the present invention. [Figure 16] 10 is a flowchart illustrating a method for determining a user's working ability using a difference in brain activity values ​​according to a second embodiment of the present invention. [Figure 17] 10 is a flowchart showing the flow of guiding a work state in the second embodiment of the present invention. [Figure 18] FIG. 10 is a block diagram showing the internal configuration of a working state estimating unit in a working ability determination device according to a third embodiment of the present invention. [Figure 19] 10 is a flowchart showing a basic flow of a method for determining a user's working ability in a third 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 state of a user who is a subject of determination, and to determine whether or not the work ability is in an appropriate state. It is also used to guide the user toward further improvement of the user's work performance state based on the estimated work state of the user.

[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 state estimation unit 14, a work 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 capacity determination device 1. The brain activity information acquisition device 2 is used, for example, to acquire brain activity information of a user that is required to estimate the work state of the user performing a task.

[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 used to estimate the user's working state, which will be described later.

[0018] Furthermore, the storage unit 10 may store an application used by the work state estimation unit 14 (described later) to estimate the work state of the user, or an application used by the work state induction unit 16 to execute an 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 work state estimation unit 14 is based on the premise that each unit is controlled by the control unit 13. However, the present invention is not limited to this control mode, and may be embodied such that each unit has a function for performing calculations.

[0022] The work state estimation unit 14 estimates the work state of the user. A specific estimation method will be described separately using drawings. The work state output unit 15 outputs the work state of the user estimated by the work state estimation unit 14. The work 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 work state induction unit 16 induces the user to improve the work state based on the user's work state estimated by the work state estimation unit 14. Note that the work state induction here is based on the user's work state, and the work state induction is executed when it is determined that the user's alertness is in an appropriate state when performing work. In other words, the work state induction process executed when the alertness is determined to be an "appropriate state" aims to further improve the work state.

[0024] On the other hand, if it is determined that the alertness level is not appropriate (hereinafter, such a state will be referred to as an "inappropriate state"), the work state induction process is not executed. This is because, for example, when the body is in a state requiring rest, stimulation of the vagus nerve, for example, leads to a relaxed state. Therefore, if the work state induction process is executed on a user in such a state, the user may be induced to further decrease their alertness level.

[0025] When inducing the user's work state, the work state induction unit 16 uses a stimulation device 3 connected to the work ability discrimination device 1. The stimulation device 3 is a device that can perform, 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 visual, auditory, tactile, and other perceptible stimuli, making them visible and presenting them to the user. Based on this 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 capacity determination device 1.

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

[0030] The task 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 values ​​required to estimate the user's working state. The comparison unit 143, for example, compares the values ​​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 the acquired information with preset thresholds. The determination unit 144 determines whether the user's working state is appropriate based on the comparison result of the comparison unit 143.

[0032] The functions of the above-mentioned components of the task state estimation unit 14 will be explained in more detail below, as appropriate, in the description of whether or not the task state of the user is appropriate.

[0033] Next, the concept of the method for estimating the task state using the user's alertness will be explained using the drawings. Figure 3 is an explanatory diagram of the user's alertness. Here, "alertness" indicates the user's alertness state.

[0034] For example, when the user's level of alertness is low, that is, when the user's body is in a state where it seeks rest, the user becomes relaxed, and for example, drowsy is induced. Therefore, such a state can be said to be an unsuitable state for the user to perform work. Therefore, such a state will be referred to as an "inappropriate state" as appropriate. In contrast, when the user's level of alertness is high, the state can be said to be suitable for performing work. Therefore, such a state will be referred to as an "appropriate state" as appropriate.

[0035] In the explanatory diagram of FIG. 3, the vertical axis represents "alertness." The closer to the tip of the arrow, the higher the alertness. When the alertness is equal to or greater than a certain threshold, the user is determined to be in an appropriate state for performing work. On the other hand, when the alertness is lower than the threshold, the user is determined to be in an inappropriate state for performing work. Therefore, in FIG. 3, the upper side (high alertness) of the threshold is shown as an "appropriate state," and the lower side (low alertness) is shown as an "inappropriate state."

[0036] Note that various values ​​can be set for the threshold used to determine whether a user is in an appropriate state or an inappropriate state using the user's level of alertness. For example, the threshold may be set based on the relationship between an index of alertness and other factors, such as an estimated state of the task when an experimental model task is performed in advance. Alternatively, the threshold may be set based on brain processes.

[0037] In addition, in the explanatory diagram of Figure 3, an example in which one threshold is set has been described. However, it is possible to set more than one threshold, for example, it is also possible to set multiple thresholds. Furthermore, the relationship between the alertness index and task performance can be modeled and set as a "continuous scale" using machine learning techniques.

[0038] As described above, the threshold can be set in various ways. Next, a case where a threshold is set based on the user's brain process will be explained using Fig. 4. Fig. 4 is an explanatory diagram showing the relationship between the user's movements, actions, and brain processes. The explanatory diagram in Fig. 4 shows the process when the user performs a task from three perspectives: "during the task," "action," and "brain."

[0039] 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 4. 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.

[0040] 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.

[0041] 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.

[0042] In the explanatory diagram in Figure 4, 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 order in the "Task Process" and "Action Process."

[0043] In the explanatory diagram in Figure 4, 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 "Work Process" and "Action Process" that are repeated over time are not shown.

[0044] The bottom row of the explanatory diagram in Figure 4 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.

[0045] In the "brain process" section, the vertical axis indicates whether the brain activity of the above-mentioned cranial nerve network 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 first embodiment of the present invention, for example, one of these three cranial nerve networks is used as an index to estimate the user's alertness and thus the task state.

[0046] These three types of brain neural networks are the Default Mode Network (DMN), the Central Executive Network (CEN), and the Salience Network (SN).

[0047] 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.

[0048] 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.

[0049] In the diagram in Figure 4, the solid line indicates the brain activity pattern of the DMN, while the dashed line indicates the brain activity pattern of 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.

[0050] 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 4, 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 4, the area where the SN is involved in the switch between the DMN and CEN is shown circled with a dotted line.

[0051] 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 state by understanding these patterns of brain activity.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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).

[0057] In the first embodiment of the present invention, the user's level of alertness is calculated based on the above-mentioned brain activity information, and the level of alertness is compared with a threshold value to estimate the user's task state. Here, the brain activity information will be described using, for example, brain activity information on the DMN and CEN as an example. 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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).

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] To estimate the user's level of alertness, for example, the CEN activity value can be used. That is, as described above, the forehead region of the CEN is, for example, the region on the left side of the forehead as seen from the user, and by measuring this region, it is possible to grasp increases or decreases in cerebral blood flow. For example, when cerebral blood flow is increased, it is considered that noradrenaline is projected to the entire cerebral cortex as a cerebral phenomenon, and therefore it is estimated that the level of alertness is high.

[0068] That is, brain activity (CEN activity value) in the brain region on the left side of the forehead is measured, and the obtained cerebral blood flow index is treated as an index of alertness, so that the state of the worker can be estimated based on the alertness. Therefore, the information acquisition unit 141 acquires the CEN activity value from the brain activity information acquisition device 2. The acquired CEN activity value is sent to the comparison unit 143, which compares the CEN activity value with an alertness threshold.

[0069] The comparison unit 143 compares the CEN activity value with the alertness threshold and transmits the result to the determination unit 144. If the CEN activity value indicates a value equal to or greater than the alertness threshold, the determination unit 144 determines that the user's alertness is in an "appropriate state." This state corresponds to the state above the threshold in the explanatory diagram of FIG. 3. In this case, the user's working state is estimated to be good.

[0070] On the other hand, if the determination unit 144 determines that the comparison result sent from the comparison unit 143 indicates that the CEN activity value is smaller than the wakefulness threshold, the user's wakefulness is determined to be in an "inappropriate state." This state corresponds to the state below the threshold in the explanatory diagram of FIG. 3. In this case, the user's working state is estimated to be in a bad state.

[0071] If the determination unit 144 determines that the level of alertness is in an "appropriate state" or an "inappropriate state," the result is notified to the user via the work status output unit 15.

[0072] Furthermore, the work state induction unit 16 acquires the estimation result of the user's alertness estimated by the work state estimation unit 14. Then, the work state induction unit 16 confirms whether the estimation result is an "appropriate state" or an "inappropriate state" as the user's work state. Note that the work 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.

[0073] When the work state induction unit 16 confirms that the work state of the user is determined to be an "appropriate state," it determines, for example, that input of stimulation to the vagus nerve is possible, and then a process of inputting stimulation to the user's vagus nerve is executed.

[0074] Specifically, the work state induction unit 16 instructs the stimulus application device 3 to apply a stimulus to the user. Based on the instruction from the work state induction unit 16, the stimulus application device 3 applies a stimulus to the user.

[0075] On the other hand, if the task state induction unit 16 determines that the user's task state is determined to be in an "inappropriate state," it determines, for example, that input of stimulation to the vagus nerve is not permitted. This is because, as described above, input of stimulation may induce the user to decrease their level of alertness. Therefore, in this case, no induction process is performed for the user.

[0076] Furthermore, when the user's work state is determined to be an "inappropriate state" in this way, the guidance process may not be performed, or an alert may be issued to the user to urge the user to stop the work. The alert is issued to the user via the work state output unit 15, for example.

[0077] [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 and Fig. 6. 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.

[0078] The process of determining the user's working ability begins when the brain activity detection unit 11 of the working 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 working state estimation unit 14 executes a process of estimating the alertness level (ST2).

[0079] After the work state estimation unit 14 estimates the user's level of alertness, the result is notified to the user via the work state output unit 15 (ST3). Then, a work state guidance process is executed to guide the user to a better work state (ST4).

[0080] Next, the flow of the process of estimating the user's alertness will be described. Fig. 6 is a flowchart illustrating a method using alertness as a method for determining the user's working ability in the first embodiment of the present invention.

[0081] The information acquiring unit 141 acquires a brain activity value (CEN activity value) Scen at an active site of the CEN from the brain activity information acquiring device 2 (ST11). The acquired CEN activity value Scen is compared with the wakefulness threshold Sth in the comparing unit 143 (ST12).

[0082] As a result of the comparison by the comparison unit 143, if the CEN activity value Scen is equal to or greater than the alertness threshold Sth (YES in ST12), the judgment unit 144 judges that the user's alertness is in an "appropriate state", and the work state output unit 15 outputs the user's work state as an "appropriate state" (ST13).

[0083] On the other hand, if the comparison by the comparison unit 143 shows that the CEN activity value Scen is smaller than the alertness threshold Sth (NO in ST12), the judgment unit 144 judges that the user's alertness is in an "inappropriate state", and the work state output unit 15 outputs the user's work state as an "inappropriate state" (ST14).

[0084] Furthermore, a task state guidance process for improving the task state based on the task state estimated using the user's alertness will be described below. Fig. 7 is a flowchart showing the flow of task performance state guidance in the first embodiment of the present invention.

[0085] The work state induction unit 16 acquires the estimation result of the user's alertness estimated by the work state estimation unit 14 (ST21). Then, the work state induction unit 16 checks whether the estimation result is an "appropriate state" or an "inappropriate state" as the user's work state (ST22).

[0086] When the work state induction unit 16 confirms that the work state of the user is determined to be "appropriate" (YES in ST22), it determines that, for example, input of stimulation to the vagus nerve is possible (ST23), and then a process of inputting stimulation to the user's vagus nerve is executed (ST24).

[0087] Specifically, as described above, the work state induction unit 16 instructs the stimulus application device 3 to apply a stimulus to the user. The stimulus application device 3 applies a stimulus to the user based on the instruction from the work state induction unit 16.

[0088] On the other hand, if the task state induction unit 16 determines that the user's task state is determined to be in an "inappropriate state" (NO in ST22), it determines, for example, that input of stimulation to the vagus nerve is not permitted (ST25). This is because, as described above, input of stimulation may induce the user to decrease their level of alertness. Therefore, in this case, no induction process is performed for the user.

[0089] 7, for the sake of convenience, the "appropriate state" and "inappropriate state" as results confirmed by the work state induction unit 16 are indicated by dashed lines because they are not processes performed by the work state induction unit 16.

[0090] (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.

[0091] In the first embodiment described above, the user's alertness is used to estimate the user's work state. However, when estimating the user's work state, it is also possible to take into account the user's work performance state in addition to the alertness.

[0092] That is, when a person takes action, they repeat the process of "perception (observing the surrounding environment)," "cognitive judgment (extracting important objects)," and "action (taking action while paying attention)," as explained using Figure 4. Along with this, a pattern of brain activity appears in the brain in which the DMN and CEN switch via the SN. Furthermore, 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.

[0093] 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.

[0094] Here, "task performance state" refers to the ease of switching between the DMN and CEN in the three cranial neural networks mentioned above via the SN.

[0095] In view of the above, in the second embodiment of the present invention, the user's working state is estimated by focusing not only on the level of alertness but also on the frequency of switching between the DMN and the CEN.

[0096] This is conceptually shown in Figure 8. Figure 8 is an explanatory diagram showing the concept of the method for determining work ability according to the second embodiment of the present invention. The vertical axis shows "arousal level" and the horizontal axis shows "work performance state."

[0097] For "alertness," the level of alertness increases toward the tip of the arrow, and decreases toward the opposite side of the arrow. In contrast, for "task performance," the level of task performance improves toward the tip of the arrow. On the other hand, the level of task performance worsens toward the opposite side of the arrow.

[0098] Then, four regions appear, separated by the vertical axis of alertness and the horizontal axis of task performance. These four regions are appropriately referred to as the first to fourth quadrants. The region that falls into the so-called first quadrant is the region where the user's task status is "appropriate." In other words, the alertness is above the alertness threshold, and the task performance status is also good.

[0099] On the other hand, when the arousal level is lower than the arousal level threshold and the task performance state is poor, the user's task state is in an "inappropriate state." The region indicating this inappropriate state is the region that corresponds to the so-called third quadrant.

[0100] The other areas, which are called quadrants 2 and 4, are neither appropriate nor inappropriate. The user's working state in these areas will be referred to as a "distracted state."

[0101] However, although both are referred to as a state of distraction, the state of distraction differs between the second and fourth quadrants. In other words, the second quadrant is a state in which arousal is high but task performance is poor. Poor task performance is, for example, a state in which switching between the DMN and CEN does not occur often, and so is thought to be a state in which one is overly focused but fails to pay attention to important things.

[0102] On the other hand, in the case of a distracted state in the fourth quadrant, task performance is good but arousal level is low. Therefore, this is a state of distraction in which the person is unable to concentrate on the target object due to low arousal level.

[0103] In this way, in the second embodiment of the present invention, the user's task state is estimated using both the alertness level and the task performance state. Of these, the method for estimating the user's task state using the alertness level has been described above. Therefore, the following describes the method for estimating the task state using the task performance state.

[0104] Here, a method using the DMN and CEN will be described. Fig. 9 is an explanatory diagram for explaining estimation of task performance state using activity values ​​in the active regions of the default mode network (DMN) and the central executive network (CEN) obtained based on brain activity information in the second embodiment of the present invention, and explains a method using the time when the activity values ​​of both are the same.

[0105] As mentioned above, the SN is involved in 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.

[0106] That is, the information acquisition unit 141 acquires the DMN activity value and the CEN activity value based on the brain activity information acquired from the brain activity information acquisition device 2. Note that the acquisition of each activity value may involve the normalization process described above. 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.

[0107] As mentioned above, the DMN and CEN in the brain process are switched depending on the user's actions. The timing of the switch between the two is as shown in Figure 4, when the user performs perception and cognitive judgment and moves on to the action, and when the user finishes the action and moves on to perception and cognitive judgment again.

[0108] When a user begins a task, perception and cognitive judgment are carried out first, as explained using the explanatory diagram in Figure 4. 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.

[0109] Therefore, during this transitional period, there is a time when the DMN activity and the CEN activity coincide when the DMN and CEN switch. 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 FIG.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] On the other hand, when the comparison result between the time difference dt and the time threshold value Tth by the comparison unit 143 shows 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.

[0115] In the second embodiment, the user's task state is estimated by combining the above-described task performance state estimation method with the alertness estimation method. That is, a case where the alertness level is appropriate and the task performance state is good corresponds to the "appropriate state" shown in the first quadrant. On the other hand, a case where the alertness level is inappropriate and the task performance state is poor corresponds to the "inappropriate state" shown in the third quadrant.

[0116] In addition, when the alertness level is appropriate but the task performance state is poor, or when the alertness level is inappropriate but the task performance state is good, both of these correspond to the distracted state. The determination unit 144 makes the above-mentioned determination based on the estimated alertness level and task performance state.

[0117] Then, the user's work state is estimated using the alertness and work performance state, and the estimated work state is output via the work state output unit 15. At the same time, the work state guidance unit 16 executes guidance processing for the user.

[0118] The work state induction unit 16 first performs the induction process when the user's alertness is high and the work performance state is good, that is, when the user's work state is in an "appropriate state." The induction process is performed, for example, by stimulating the user's vagus nerve via the stimulation device 3.

[0119] When the user's working state is "appropriate," it is included in the first quadrant in the explanatory diagram of FIG. 8. For example, the user may have a very high level of alertness and a very good working state. However, this is not necessarily the only possible state; it may also be that the user is judged to be in an "appropriate" state, but the level of alertness is not so high and the working state is not so good. In other words, this is the case when the user's working state is shown in a position close to the origin in the first quadrant in the explanatory diagram of FIG.

[0120] As described above, even if a user is included in the same quadrant, the user's work state may vary. Therefore, for example, the lower the level of the user's work state, the stronger the stimulus applied to the user in the user guidance process. In other words, the work state guidance unit 16 controls the stimulus application device 3 so that when the level of the user's work state is low, the stimulus applied to the user is stronger, and conversely, when the level of the user's work state is high, the stimulus applied to the user is weaker.

[0121] By performing such guidance processing, it is possible to provide the user with stimuli that are appropriate for their working state, and therefore the guidance processing can be utilized effectively, thereby improving the working state more efficiently.

[0122] 8, when the user's work state is appropriate, the user's alertness is low and the work performance state is poor, i.e., the user's work state on the side closer to the origin is expressed as "the user's work state has a low level" for the sake of convenience. On the other hand, the user's alertness is higher and the work performance state is better, i.e., the user's work state on the side farther from the origin is expressed as "the user's work state has a high level" for the sake of convenience.

[0123] In the second embodiment of the present invention, the user's task state is estimated using the alertness level and task performance state, and the task state is divided into four states, including an "appropriate state," an "inappropriate state," and two "distracted states." Of these, for a user in an inappropriate state, no stimulus is input to induce a task state, as this may lead to a decrease in alertness, as described above.

[0124] In contrast, in the case of a "distracted state," a process is executed to guide the user toward improving their work state. This is because, even in a distracted state, if the level of arousal is high, there is no risk of the user being guided toward lowering the level of arousal. On the other hand, if the level of arousal is low but the work performance state is good, although the level of arousal is indeed low, it is thought that by adding a stimulus it is possible to guide the user toward further improving the work performance state.

[0125] Note that this guidance process is also performed for the user when the user's work state is in a distracted state. Therefore, the intensity of the stimulus applied to the user can be changed according to the level of the user's work state. As described above, the user's work state when determined to be in a distracted state can be considered to be in two patterns: when the user is in the second quadrant or the fourth quadrant in the explanatory diagram of FIG. 8.

[0126] Therefore, for the sake of convenience, the user's working state in the direction away from the origin will be referred to as "high level of distraction" whether in the second or fourth quadrant. Conversely, the user's working state closer to the origin will be referred to as "low level of distraction" whether in the second or fourth quadrant.

[0127] When a user is in a distracted state during work and a guidance process is performed on the user, the higher the level of distraction, the stronger the stimulus applied to the user, whereas the lower the level of distraction, the weaker the stimulus applied to the user.

[0128] By performing such guidance processing, it is possible to provide the user with stimuli that are appropriate for their working state, and therefore the guidance processing can be utilized effectively, thereby improving the working state more efficiently.

[0129] As described above, if the user's working state is determined to be inappropriate and the above-mentioned guidance process is not executed, an alert may also be sent to the user urging them to stop working.

[0130] The alert is notified to the user, for example, via the work status output unit 15. The type of alert may be any type as long as it appeals to the user's five senses, such as hearing or sight.

[0131] Up to now, the method using the coincidence of activity values ​​between the DMN and the CEN has been exemplified as a method used in the estimation process of the user's task performance state. However, the method of estimating the task performance state using the brain neural network is not limited to this method. Therefore, other methods will be described below.

[0132] First, we use the DMN and CEN to estimate the user's task performance state based on the frequency of switching between the DMN and CEN. However, unlike the above-mentioned method, this method uses the average activity values ​​of the DMN and CEN.

[0133] That is, the method for determining work capacity here uses the average DMN activity value obtained by measuring DMN activity for a predetermined period of time and the average CEN activity value obtained by measuring CEN activity for a predetermined period of time. Here, the "predetermined period" can be set arbitrarily, but is a period long enough to take the average of the DMN activity value and the CEN activity value. Therefore, a period long enough for brain activity in the DMN and CEN to be switched multiple times is required. The set predetermined period of time is stored, for example, in the memory unit 12.

[0134] Based on the brain activity information acquired by the information acquisition unit 141 from the brain activity information acquisition device 2, the calculation unit 142, for example, calculates a DMN activity value and a CEN activity value. Note that the calculation of each activity value may involve normalization processing as described above. From these multiple DMN activity values ​​and CEN activity values, the calculation unit 142 calculates the average value of the DMN activity value and the CEN activity value.

[0135] 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.

[0136] 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 between the respective average values.

[0137] The comparison unit 143 compares the difference in the average values ​​calculated by the calculation unit 142 with a preset average threshold. It is more preferable that the comparison unit 143 compares the absolute value of the difference in the average values ​​of the DMN activity value and the CEN activity value with the average threshold.

[0138] If the comparison by the comparison unit 143 determines that the absolute value of the difference in the average values ​​is smaller than the average threshold, the determination unit 144 determines that the user's task performance state is good. If the difference in the average values ​​is smaller than the average threshold, this means that the difference between the DMN activity value and the CEN activity value is small.

[0139] 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.

[0140] On the other hand, if the difference between the average values ​​is equal to or greater than the average threshold, 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The comparison unit 143 compares the SN activity value acquired by the information acquisition unit 141 with a preset SN threshold. Based on the comparison result by the comparison unit 143, if the determination unit 144 determines that the SN activity value indicates a value equal to or greater than the SN threshold, it determines that the user's task performance state is good.

[0146] The fact that the SN activity value is equal to or greater than the SN threshold proves 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.

[0147] On the other hand, if it is determined that the SN activity value is smaller than the SN threshold, 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.

[0148] 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. 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.

[0149] The calculation unit 142 calculates the average value of the brain activity values. The larger the average value, the more frequently the DMN and CEN are switched. The comparison unit 143 compares the average value with an average threshold.

[0150] If the determination unit 144 determines that the average value is equal to or greater than the average threshold, 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 is smaller than the average threshold, it determines that the user's task performance state is bad.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The brain activity information acquisition device 2 acquires brain activity values ​​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 brain activity values ​​when no stimulus is applied to the user.

[0155] The information acquiring unit 141 acquires, as brain activity information, information on brain activity values ​​when a stimulus is applied to the user and when no stimulus is applied to the user, which information is acquired by the brain activity information acquiring device 2. The calculating unit 142 calculates the difference (activity difference) between the acquired brain activity value and the brain activity value.

[0156] The comparison unit 143 compares the activity difference calculated by the calculation unit 142 with a preset activity difference threshold. If the determination unit 144 determines as a result of the comparison that the activity difference indicates a value equal to or less than the activity difference threshold, it determines that the user's task performance state is good.

[0157] 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.

[0158] Therefore, the smaller the difference in activity values ​​between 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 can be said to be. Therefore, as described above, if the activity difference is a value equal to or less than the activity difference threshold, the user's task performance state is determined to be good.

[0159] Conversely, if there is a large difference in brain activity values ​​depending on whether or not the user is stimulated (if the activity difference is greater than the activity difference threshold), the switching of brain activity patterns is not smooth, and the user's task performance is determined to be poor.

[0160] [Operation] Next, the processing flow of determining the working ability of a person to be determined by the working ability determination device 1 in the second embodiment will be described appropriately using the flowcharts shown in Figures 10 to 13. Figure 10 is a flowchart showing the basic flow of the method for determining the working ability of a user in the second embodiment of the present invention.

[0161] The process of determining the user's working ability begins when the brain activity detection unit 11 of the working ability determination device 1 acquires brain activity information from the brain activity information acquisition device 2 (ST1). Then, the working state estimation unit 14 executes the wakefulness estimation process described in the first embodiment (ST2). Then, based on the acquired brain activity information, the working state estimation unit 14 determines whether the working performance state is good or bad (ST5).

[0162] Although the wakefulness estimation process and the task performance state estimation process have been described in the order of the former and the latter, either of these processes may be performed first. Furthermore, these processes may be performed in parallel.

[0163] After the task 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 state output unit 15 (ST3). Then, in order to guide the user to a better task performance state, the task performance state guidance unit 16 executes a task performance state guidance process (ST4).

[0164] The above is a rough outline of the method for estimating a task state using a user's alertness and task performance state. The flow of the alertness estimation process has been explained in the first embodiment, so here we will explain the flow of the task performance estimation process. Figures 11 and 12 are flowcharts explaining a method using DMN and CEN for determining a user's task ability in an embodiment of the present invention.

[0165] The estimation method described using Figures 11 and 12 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.

[0166] The information acquisition unit 141 of the task 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 (ST31). 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.

[0167] Additionally, the information acquiring unit 141 acquires information on brain activity in the CEN from the brain activity information acquiring device 2 (ST32). 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.

[0168] 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.

[0169] The comparison unit 143 compares the acquired DMN activity value Sd1 and CEN activity value Sc1 to determine whether or not they match (ST33). If the determination unit 144 determines that the DMN activity value Sd1 and the CEN activity value Sc1 do not match (NO in ST33), the process returns to step ST31 to acquire the DMN activity value Sd1 and the CEN activity value Sc1 again.

[0170] 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 ST33), the time t1 when the DMN activity value Sd1 and the CEN activity value Sc1 match is obtained (ST34).

[0171] 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 (ST35).The information acquiring unit 141 also acquires information on brain activity in the CEN as a brain activity value Sc2 (ST36).

[0172] The comparison unit 143 compares the acquired DMN activity value Sd2 and CEN activity value Sc2 to determine whether or not they match (ST37). If the determination unit 144 determines that the DMN activity value Sd2 and the CEN activity value Sc2 do not match (NO in ST37), the process returns to step ST35 to acquire the DMN activity value Sd2 and the CEN activity value Sc2 again.

[0173] 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 ST37), the time t2 when the DMN activity value Sd2 and the CEN activity value Sc2 match is obtained (ST38).

[0174] 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 (ST39 in FIG. 12). The determination unit 144 then compares the time difference dt with a preset time threshold Tth (ST40).

[0175] If the determination unit 144 determines that the time difference dt is smaller than the time threshold Tth (YES in ST40), it outputs that the user's task performance status is good via the task status output unit 15 (ST41).

[0176] 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 ST40), it outputs via the task status output unit 15 that the task performance status of the user is poor (ST42).

[0177] 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 13 is a flowchart explaining a second method for determining a user's work ability using the DMN and CEN in the second embodiment of the present invention.

[0178] 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 (ST51). 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 (ST52).

[0179] 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 (ST53), and also calculates the average value Ac of the brain activity values ​​SC of the multiple CENs (ST54).

[0180] 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.

[0181] The calculation unit 142 further calculates the difference dA between the calculated average values ​​Ad and Ac (ST55). 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 (ST56). As described above, it is more preferable to use an absolute value for the difference dA.

[0182] 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 ST56), the determination unit 144 determines that the user's task performance state is good (ST57). On the other hand, if the difference dA in the average values ​​is equal to or greater than the average threshold Dth1 (NO in ST56), the determination unit 144 determines that the user's task performance state is bad (ST58).

[0183] Next, a method for determining the quality of a user's task performance using an SN will be described with reference to Fig. 14. Fig. 14 is a flowchart illustrating a first method using a salience network (SN) for determining a user's task performance in the second embodiment of the present invention.

[0184] 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 (ST61). 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 (ST62).

[0185] 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 ST62), it judges that the user's task performance status is good (ST63).

[0186] 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 ST62), it determines that the user's task performance state is poor (ST64).

[0187] 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 state by focusing on the activity of the SN as a brain activity pattern will be described with reference to Fig. 15. Fig. 15 is a flowchart illustrating a second method using the SN as a method for determining a user's task ability in the second embodiment of the present invention.

[0188] 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 (ST71). Then, the calculating unit 142 calculates the average value As of the plurality of brain activity values ​​SS (ST72). The comparing unit 143 compares the average value As with an average threshold Dth2 that is preset and stored in the storage unit 12 (ST73).

[0189] If the determination unit 144 determines that the average value As is equal to or greater than the average threshold value Dth2 (YES in ST73), it determines that the user's task performance state is good (ST74). 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 ST73), it determines that the user's task performance state is bad (ST75).

[0190] 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. 16. Fig. 16 is a flowchart illustrating a method for determining a user's task ability using the difference in brain activity values ​​in the second embodiment of the present invention.

[0191] First, while the user is performing a task, the stimulus applying device 3 applies a stimulus to the user (ST81). Then, the brain activity information acquiring device 2 acquires a brain activity value N when the stimulus is applied to the user (ST82).

[0192] Thereafter, the stimulation application device 3 stops applying stimulation to the user (ST83). The brain activity information acquisition device 2 acquires the brain activity value F when no stimulation is applied to the user (ST84).

[0193] 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.

[0194] 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 (ST85).

[0195] 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 (ST86). 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 ST86), it determines that the user's task performance state is good (ST87).

[0196] 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 ST86), the switching of brain activity patterns is not smooth, and therefore the user's task performance state is judged to be poor (ST88).

[0197] Now that the user's alertness and task performance state have been obtained, the determination unit 144 determines what state the user's task performance state is in. How the task performance state is determined depends on how thresholds for the alertness and task performance state are set, but for example, the task performance state can be set to correspond to one of the quadrants in the explanatory diagram shown in Fig. 8. The determination result may then be notified to the user via the task performance output unit 15, for example.

[0198] Next, the flow of the guidance process based on the user's work state will be described with reference to Fig. 17. Fig. 17 is a flowchart showing the flow of guidance of the work state in the second embodiment of the present invention.

[0199] The task state guidance unit 16 acquires information on the user's alertness and task performance state estimated by the task state estimation unit 14 (ST91, ST92). Note that while Fig. 17 shows that the alertness is acquired first and then the task performance state is acquired, either the alertness or the task performance state may be acquired first, or they may be acquired in parallel.

[0200] The work state induction unit 16 determines whether the user's work state is determined to be appropriate based on the estimation result of the alertness level (ST93). If the user's work state is determined to be appropriate (YES in ST94), the work state induction unit 16 further determines whether the user's work state is determined to be good based on the estimation result of the work performance state (ST94).

[0201] If the determination is that the work performance state is good (YES in ST94), the user's work state is appropriate, that is, it is in the state shown in the first quadrant area in FIG.

[0202] Since the user's working state is in this state, the working state induction unit 16 determines that it is acceptable to input a stimulus to the vagus nerve (ST95). Therefore, a process is executed to input a stimulus to the user's vagus nerve via the stimulus application device 3 (ST96). By executing this induction process, the user's working state can be further improved.

[0203] When the task state induction unit 16 applies a stimulus to the user via the stimulus application device 3, the strength of the stimulus may be adjusted according to the level of the user's task state, as described above. That is, when the level of the user's task state is low, the stimulus can be made strong, and when the level of the user's task state is high, the stimulus can be made weak.

[0204] On the other hand, if the work state induction unit 16 determines that the alertness level is inappropriate (NO in ST93), the work state induction unit 16 further determines whether the user's work state is determined to be good based on the estimation result of the work performance state (ST97).

[0205] If the work state induction unit 16 determines that the user's work performance state is good (YES in ST97), the user's work state is determined to be a distracted state. Specifically, the user's work state corresponds to the state shown in the fourth quadrant area in FIG. 8.

[0206] Furthermore, even if the work state induction unit 16 confirms that the user's work state is determined to be appropriate based on the wakefulness estimation processing result (YES in ST93), and determines that the work performance state is determined to be poor (NO in ST94), the user's work state is also determined to be a distracted state. However, the distracted state in this case corresponds to the distracted state shown in the second quadrant area in FIG. 8.

[0207] When the user's work state is in a distracted state, the work state induction unit 16 executes an induction process for the user. That is, the work state induction unit 16 determines that it is acceptable to input a stimulus to the vagus nerve, and executes a process of inputting a stimulus to the vagus nerve of the user via the stimulus application device 3 (ST95, ST96).

[0208] When the user's work state is in a distracted state, the user's alertness is high but the work performance state is poor. Alternatively, the user's alertness is low but the work performance state is good. Therefore, in such cases, unlike when the user's work state is in an inappropriate state as described below, the user's alertness is not necessarily guided in a direction that further worsens, and it is thought that the work state can be improved. Therefore, when the user's work state is in a distracted state, a guidance process is executed.

[0209] When the user is in a distracted state during work, the strength of the stimulus to be applied to the user can be adjusted according to the level of distraction. That is, the work state induction unit 16 can control the stimulus application device 3 so that the stimulus is strengthened when the level of distraction is high, and weakened when the level of distraction is low.

[0210] On the other hand, if the work state induction unit 16 determines that the user has a low level of alertness and a poor work performance state (NO in ST93, NO in ST97), the user's work state is inappropriate.

[0211] When the user's working state is inappropriate, executing a guidance process for the user may actually lead to a decrease in the user's alertness. Therefore, as described above, the working state guidance unit 16 determines that input of stimulation to the user's vagus nerve is not permitted so as not to input stimulation for inducing a working state (ST98).

[0212] Therefore, no guidance process is performed on the user via the stimulus adding device 3. In addition, an alert to stop the work is notified to the user via the work status output unit 15 (ST99).

[0213] (Third embodiment) Next, a third embodiment of the present invention will be described. In the third embodiment, the same components as those described in the first or second embodiment are denoted by the same reference numerals, and redundant descriptions of the same components will be omitted.

[0214] 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 4, the method for estimating the user's task performance status focuses on the frequency of switching between the DMN and the CEN.

[0215] 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.

[0216] As explained using the diagram in Figure 4, 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 gradually shifts from the DMN to the CEN, a neural network that works when focusing attention on important things.

[0217] 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.

[0218] 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.

[0219] Therefore, in the third 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.

[0220] 18 is a block diagram showing the internal configuration of a working state estimating unit 14A in a working ability discrimination device 1 according to the third embodiment of the present invention. The difference from the working ability discrimination devices 1 according to the first and second embodiments is that an aging state estimating unit 145 is provided between an information acquiring unit 141 and a calculating unit 142.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 19 is a flowchart showing the basic flow of a method for determining a user's task ability in the third 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 (ST6). After the aging state estimation is performed, various processes are performed, such as the wakefulness estimation process (ST2) by the task state estimation unit 14 and the determination of the user's task performance state (ST3, ST4), as described above.

[0226] 19 does not include the task performance state estimation process (ST5) described in the second embodiment. However, the aging state estimation process can be executed when the process in the second embodiment is performed.

[0227] [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 state based on the acquired brain activity information, and the step of estimating the user's work state is a step of estimating the user's level of alertness.

[0228] By employing such a determination method, it is possible to accurately estimate the user's task state using the user's brain activity information.

[0229] (2) The step of estimating the user's level of alertness in the method for determining work ability described in (1) above includes the steps of acquiring a CEN activity value in the active area of ​​the central executive network (CEN) obtained based on the acquired brain activity information, comparing the acquired CEN activity value with a predetermined alertness threshold, and determining that the user's work state is appropriate if the CEN activity value is determined to be equal to or greater than the alertness threshold.

[0230] By using the CEN activity value in the process of estimating the level of alertness in this way, the level of alertness can be estimated with higher accuracy, and therefore the working state of the user can be estimated more accurately.

[0231] (3) In the method for determining work ability described in (1) or (2) above, the method for determining work ability further includes, after the step of estimating the user's work state, a step of guiding the user's work state, and the step of guiding the work state includes a step of acquiring information about the estimated user's alertness, a step of determining whether the user's work state is determined to be appropriate from the information about the alertness, and a step of guiding the user's work state if it is determined that the user's work state is appropriate.

[0232] By performing a guidance process for the user when the user's working state is appropriate, the user's working state can be further improved.

[0233] (4) In the method for determining work ability described in any one of (1) to (3) above, the step of estimating the user's work state further includes a step of estimating the work performance state, and the estimation of the work performance state is performed 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.

[0234] When estimating the user's task performance state, the task performance state can be estimated with higher accuracy by estimating the user's task performance state based on the frequency of switching between the DMN and the CEN.

[0235] (5) The step of estimating the user's work performance state in the work ability determination method described in (4) 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.

[0236] In this way, by estimating the user's task performance state based on the frequency of switching between the DMN and the CEN, it is possible to accurately estimate the quality of the user's task performance state.

[0237] (6) The step of estimating the user's work performance state in the work ability determination method described in (4) 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.

[0238] In this method of estimating a user's task performance status based on the frequency of switching between DMN and CEN, the average values ​​of the DMN activity value and CEN activity value are used, thereby shortening the time required to determine task ability without missing any brain activity information.

[0239] (7) The step of estimating the user's work performance state in the work ability determination method described in (4) 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.

[0240] 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.

[0241] (8) The step of estimating the user's work performance state in the method for determining work ability described in (4) 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.

[0242] 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.

[0243] (9) The step of estimating the user's work performance state in the method for determining work ability described in (4) above includes a step of acquiring brain activity information when a stimulus is applied to the user, a step of acquiring brain activity information when no stimulus is applied to the user, a step of calculating the activity difference between the brain activity information when a stimulus is applied and the brain activity information when no stimulus is applied, a step of comparing the activity difference with a predetermined activity difference threshold, and a step of 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.

[0244] 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.

[0245] (10) In the method for determining work ability described in any of (1) to (9) above, a step of determining whether brain function is aging or not is included before the step of estimating the user's work state, and if it is determined that the brain function is aging, an estimation process of the user's work performance state is performed.

[0246] 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.

[0247] (11) In the method for determining work ability described in any of (4) to (10) above, the step of estimating the user's work state determines that the user's work state is appropriate when the user's level of alertness is high and the user's work performance state is good, determines that the user's work state is inappropriate when the user's level of alertness is low and the user's work performance state is poor, and determines that the user's work state other than the appropriate state and the inappropriate state is a distracted state.

[0248] In particular, by taking into account not only the level of alertness but also the state of task performance when estimating the user's task state, it becomes possible to estimate the task state more accurately. Then, by performing guidance processing based on the estimated task state, it becomes possible to improve the controllability of guiding the task state of the user.

[0249] (12) In the method for determining work ability described in any of (4) to (11) above, the step of inducing a work performance state includes a step of acquiring information regarding the estimated user's alertness and information regarding the user's work performance state, and a step of inducing the user to a work state when the user's work state is determined to be appropriate and when the user is determined to be in a distracted state.

[0250] By executing the work state guidance process not only when the user's work state is appropriate but also when the user's attention is distracted, the user's work state can be further improved.

[0251] (13) In the method for determining work ability described in (12) above, when guiding a user to a work state, if it is determined that the user's work state is appropriate, the guidance is performed with greater intensity the lower the level of the work state.

[0252] By performing such guidance processing, it is possible to provide the user with stimuli that are appropriate for their working state, and therefore the guidance processing can be utilized effectively, thereby improving the working state more efficiently.

[0253] (14) In the method for determining work ability described in (12) above, when inducing a work state for a user, if it is determined that the user's work state is a distracted state, the guidance is performed with greater intensity the higher the level of the distracted state.

[0254] By performing the process of guiding the user to improve their work state in this way, the user's work state can be further improved.

[0255] (15) In the step of estimating the user's work state in the method for determining work ability described in any of (4) to (14) above, if the information regarding the alertness level determines that the user's work state is inappropriate and the information regarding the work performance state determines that the user's work state is poor, no guidance is given to the user's work state.

[0256] If the user's level of alertness is low and the task performance is poor, executing the task state induction process may actually lead to a decrease in the level of alertness. Therefore, by not inputting a stimulus to induce a task state, it is possible to prevent a further decrease in the level of alertness.

[0257] (16) In the method for determining work ability described in (15) above, if the user's work state is not induced, the user is notified to stop the work. By notifying the user in this manner, the user can be prompted to stop the work if the user's work state is inappropriate, thereby protecting the user. Furthermore, the execution of unnecessary work state induction processing can be avoided.

[0258] (17) A work ability discrimination device includes a brain activity detection unit that acquires brain activity information of a user, and a work state estimation unit that estimates the user's level of alertness based on the acquired brain activity information and estimates the user's work state from the level of alertness.

[0259] By employing such a discrimination device, it is possible to accurately estimate and provide the user's working state using the user's brain activity information.

[0260] (18) In the work ability discrimination device described in (17) above, the work state estimation unit estimates the user's work performance state based on the acquired brain activity information, and estimates the user's work state from the work performance state.

[0261] When estimating the user's working state, the user's working state can be more accurately estimated by taking into consideration not only the user's level of alertness but also the state of work performance.

[0262] (19) The work ability determination device described in (17) or (18) above is provided with a work state guidance unit that guides the user to change the work state after the work state estimation unit estimates the user's work state.

[0263] After estimating the user's work state in this way, the work state can be further improved by guiding the user's work performance state in a better direction. [Explanation of symbols]

[0264] 1. Work ability discrimination device, 11. Brain activity detection unit, 12. Memory unit, 13. Control unit, 14. Work state estimation unit, 141. Information acquisition unit, 142. Calculation unit, 143. Comparison unit, 144. Determination unit, 15. Work 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; and estimating the task state of the user based on the acquired brain activity information, A method for determining working ability, wherein the step of estimating the user's working state is a step of estimating the user's level of alertness.

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

3. The method for determining work ability further includes, after the step of estimating the work state of the user, a step of guiding the work state of the user, The step of inducing a working state includes: acquiring information about the estimated alertness of the user; determining whether the user's working state is determined to be appropriate based on the information about the wakefulness level; When the user's work state is determined to be an appropriate state, instructing the user to work in the appropriate state; 3. The method for determining working ability according to claim 1, further comprising:

4. The step of estimating the user's work state further includes the step of estimating a work performance state, The method for determining work ability described in claim 1, characterized in that the estimation of the work performance state is performed 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.

5. 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; 5. The method for determining working ability according to claim 4, further comprising:

6. 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; 5. The method for determining working ability according to claim 4, further comprising:

7. 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; 5. The method for determining working ability according to claim 4, further comprising:

8. 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; 5. The method for determining working ability according to claim 4, further comprising:

9. 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; 5. The method for determining working ability according to claim 4, further comprising:

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

11. In the step of estimating a work state of the user, determining that the user's work state is appropriate when the user's level of alertness is high and the user's work performance state is good; determining that the user's work state is inappropriate when the user's level of alertness is low and the user's work performance state is poor; 5. The method for determining working ability according to claim 4, wherein a working state of the user other than the appropriate state and the inappropriate state is determined to be a distracted state.

12. The step of inducing a work performance state includes: acquiring information about the estimated alertness of the user and information about the task performance state of the user; When the user's working state is determined to be an appropriate state and when the user's attention is determined to be in a distracted state, instructing the user to enter the working state; 5. The method for determining working ability according to claim 4, further comprising:

13. When the user is determined to be in an appropriate state for inducing the user to be in the working state, The method for determining working ability according to claim 12, wherein the guidance is performed with a higher intensity as the level of the working state is lower.

14. When the work state of the user is determined to be the distracted state when the work state guidance is performed on the user, The method for determining work capacity according to claim 12, wherein the guidance is performed with a higher intensity as the level of the distracted state increases.

15. A method for determining work ability as described in claim 4, characterized in that in the step of estimating the user's work state, if it is determined from the information regarding the alertness that the user's work state is inappropriate and if it is determined from the information regarding the work performance state that the user's work state is poor, the method does not perform the work state induction for the user.

16. 16. The method for determining working ability according to claim 15, further comprising a step of informing the user to stop working if the guidance to the working state is not executed for the user.

17. a brain activity detection unit that acquires brain activity information of a user; an activity state estimation unit that estimates a level of alertness of the user based on the acquired brain activity information and estimates an activity state of the user from the level of alertness; A working ability determination device comprising:

18. The working ability discrimination device according to claim 17, wherein the working state estimation unit estimates the working performance state of the user based on the acquired brain activity information, and estimates the working state of the user from the working performance state.

19. 19. The working ability determination device according to claim 17, further comprising a working state induction unit that induces the user to change the working state after the working state of the user is estimated by the working state estimation unit.

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