State estimation device, state estimation method and state estimation program

Abstract

SOLUTION: A state estimation device comprises: an information acquisition unit for acquiring brain wave information of a subject to whom a content is provided; and a state estimation unit for estimating the state of the subject to whom a content is provided, on the basis of the brain wave information of the subject, where the information acquisition unit may acquire the biological information of the subject to whom the content is provided, and the state estimation unit may estimate the state on the basis of the brain wave information and the biological information. The information acquisition unit may acquire the brain wave information of the subject before provision of the content and during provision of the content. The state estimation unit may estimate the state on the basis of the change of the brain wave information during provision of the content and the biological information, according to the brain wave information before provision of the content.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] The present invention relates to a state estimation device, a state estimation method, and a state estimation program.

Background Art

[0002] Patent Document 1 describes that "in the learning phase, ··· first brain activity data indicating the amount of variation in brain activity is acquired" (Claim 1). [Prior Art Document] [Patent Document] [Patent Document 1] Japanese Patent No. 6935774

Summary of the Invention

[0003] In a first aspect of the present invention, a state estimation device is provided. The state estimation device includes an information acquisition unit that acquires electroencephalogram information of a subject for whom content is provided, and a state estimation unit that estimates the state of the subject for whom content is provided based on the electroencephalogram information of the subject.

[0004] The information acquisition unit may further acquire biological information of the subject for whom content is provided. The state estimation unit may estimate the state based on the electroencephalogram information and the biological information.

[0005] In any of the above state estimation devices, the information acquisition unit may acquire the electroencephalogram information of the subject before and during the provision of the content. The state estimation unit may estimate the state based on the change from the electroencephalogram information before the provision of the content to the electroencephalogram information during the provision of the content and the biological information.

[0006] In any of the above state estimation devices, the state estimation unit may estimate the state based on the change from the ratio of the amplitude of the brain waves in a predetermined frequency band in the brain wave information before the provision of the content to the ratio of the amplitude of the brain waves in the frequency band in the brain wave information during the provision of the content, and the ratio of the magnitude of the first power spectrum in the heartbeat of the subject to the magnitude of the second power spectrum. The overall amplitude is the sum of the amplitudes of alpha waves, beta waves, theta waves, gamma waves, and delta waves. The frequency band of the second power spectrum is a higher frequency band than the frequency band of the first power spectrum.

[0007] In any of the above state estimation devices, the state estimation unit may estimate the state based on the ratio of the magnitude of the first power spectrum to the magnitude of the second power spectrum during the provision of the content, the magnitude relationship between the ratio of the magnitude of the first power spectrum to the magnitude of the second power spectrum and a predetermined heartbeat threshold, and the change from the ratio of the amplitude of the brain waves in the frequency band before the provision of the content to the ratio of the amplitude of the brain waves in the frequency band during the provision of the content.

[0008] In any of the above state estimation devices, the information acquisition unit may acquire the brain wave information of each of a plurality of subjects to whom common content is provided. The state estimation unit may estimate the state of each of the plurality of subjects based on the brain wave information of each of the plurality of subjects. Any of the above state estimation devices may further include a content control unit that controls the content based on the state of each of the plurality of subjects.

[0009] In any of the above state estimation devices, the information acquisition unit may further acquire identification information for identifying each of the terminals of a plurality of subjects to whom the content is provided. The content control unit may control the content based on the identification information and the state of each of the plurality of subjects.

[0010] In any of the above state estimation apparatuses, the content control unit may divide a plurality of target persons into stages based on their respective states, and control the content provided to each group of the plurality of target persons divided into stages to be content corresponding to the state at each stage.

[0011] Any of the above state estimation apparatuses may further include a providing unit that provides other content for making the state of the target person a predetermined state based on the state of the target person.

[0012] In any of the above state estimation apparatuses, the information acquisition unit may further acquire a test result regarding the degree of understanding of the content by the target person. The state estimation unit may determine the understanding state of the content by the target person based on the estimated state, which is the state estimated by the state estimation unit, and the test result.

[0013] Any of the above state estimation apparatuses may further include a state learning unit that generates an understanding state inference model for inferring the understanding state based on the estimated state by machine learning the relationship between the respective estimated states of a plurality of target persons and the respective test results of the plurality of target persons.

[0014] In a second aspect of the present invention, a state estimation method is provided. The state estimation method includes an information acquisition step in which an information acquisition unit acquires electroencephalogram information of a target person to whom content is provided, and a state estimation step in which a state estimation unit estimates the state of the target person to whom content is provided based on the electroencephalogram information of the target person.

[0015] In a third aspect of the present invention, a state estimation program is provided. The state estimation program causes a computer to execute an information acquisition step of acquiring electroencephalogram information of a target person to whom content is provided, and a state estimation step of estimating the state of the target person to whom content is provided based on the electroencephalogram information of the target person.

[0016] Note that the above summary of the invention does not enumerate all the features of the present invention. Also, sub-combinations of these feature groups can also be inventions.

Brief Description of the Drawings

[0017]

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Modes for Carrying Out the Invention

[0018] Hereinafter, the present invention will be described through embodiments of the invention. However, the following embodiments do not limit the invention according to the claims. Also, not all combinations of features described in the embodiments are essential for the solution means of the invention.

[0019] FIG. 1 is a diagram showing an example of a situation where content 120 is provided to target person 110. The content 120 is information on an object of experience or an object of viewing for the target person 110. In this specification, "experience" refers to participating in a seminar or the like at a venue such as a seminar, and "viewing" refers to receiving the provision of video and audio of a seminar or the like via a terminal 130 (described later). When referred to as "viewing" in this specification, it may include both "viewing" and "experience", and when referred to as "experience", it may include both "viewing" and "experience". The information on the object of viewing or the object of experience includes at least one of visual information and auditory information.

[0020] FIG. 1 is an example of a case where provider 112 provides content 120 to target person 110 at a school, a cram school, or the like. In the example of FIG. 1, the target person 110 is a child, the provider 112 is a teacher at a school, a cram school, or the like, and the content 120 is a lesson at a school, a cram school, or the like. In the example of FIG. 1, the provider 112 provides the content 120 to two target persons 110 (target person 110-1 and target person 110-2) in a classroom. In the example of FIG. 1, target person 110-1 feels that they "understand" the content 120, and target person 110-2 feels that they "do not understand at all" the content 120.

[0021] The providing unit 30 provides the state S of the target person 110. As will be described later, the state S is estimated based on the brain wave information Ib (described later) of the target person 110. The providing unit 30 is, for example, a tablet, a display, a monitor, or the like. The provider 112 may be a user of the state estimation device 100. In the example of FIG. 1, the provider 112 is checking the state S of the target person 110 by the providing unit 30.

[0022] FIG. 2 is a diagram showing another example of a situation where content 120 is provided to target person 110. FIG. 2 is an example of a case where provider 112 provides content 120 to target person 110 in an online meeting at a workplace or the like. In the example of FIG. 2, the target person 110 is a participant in the meeting, the provider 112 is the organizer of the meeting, and the content 120 is the content of the explanation in the meeting.

[0023] In the example of FIG. 2, provider 112 provides content 120 to four subjects 110 (subjects 110-3 to 110-6). In the example of FIG. 2, subject 110-6 is in a state of having an idea. In the example of FIG. 2, provider 112 confirms, based on state S provided by providing unit 30, that subject 110-6 is in a state of having an idea. In the example of FIG. 2, provider 112 asks subject 110-6, "Do you have any opinions?"

[0024] In the example of FIG. 2, subjects 110 are displayed on providing unit 30, and state S is provided. State S may be provided by providing unit 30 such as a tablet. When state S is provided by providing unit 30 such as a tablet, subject 110 may be displayed on a display, monitor, etc. separate from the providing unit 30.

[0025] FIG. 3 is a diagram showing another example of a situation where content 120 is provided to subjects 110. FIG. 3 is an example where provider 112 provides content 120 to a plurality of subjects 110 in a seminar. In the example of FIG. 3, subject 110 is a participant in the seminar, provider 112 is a lecturer of the seminar, and content 120 is the explanatory content in the seminar.

[0026] Let state S be the state of subject 110 that reflects the mental state of subject 110. State S includes the potential state of subject 110. The potential state of subject 110 is a state that reflects the mental state of the subject himself / herself but that the subject 110 is not aware of himself / herself. For example, when subject 110 feels that he / she cannot understand content 120, the mental state of subject 110 that he / she cannot understand the content may be reflected in state S. In the example of FIG. 3, the comprehension levels of a plurality of subjects 110 are provided on providing unit 30. In the example of FIG. 3, the comprehension levels are divided into stages, and for each stage of the comprehension level, the proportion of the corresponding subject 110 is provided. In the example of FIG. 3, provider 112 is thinking, based on the comprehension level provided by providing unit 30, that "it might be better to start from a more basic explanation. Should we also increase the videos?"

[0027] FIG. 4 is a block diagram showing an example of a state estimation device 100 according to one embodiment of the present invention. The state estimation device 100 includes an information acquisition unit 10 and a state estimation unit 20. The state estimation device 100 may include a providing unit 30, a content control unit 40, a storage unit 50, a state learning unit 70, and a control unit 90.

[0028] A part or all of the state estimation device 100 may be realized by a computer. The control unit 90 may be a CPU (Central Processing Unit) of the computer. When the state estimation device 100 is realized by a computer, an evaluation support program for causing the computer to function as the state estimation device 100 may be installed in the computer, and an information processing program for executing the information processing method described later may also be installed.

[0029] The information acquisition unit 10 acquires electroencephalogram information of the subject 110 to whom the content 120 is provided. Let the electroencephalogram information of the subject 110 be electroencephalogram information Ib. The electroencephalogram information Ib of the subject 110 to whom the content 120 is provided refers to the electroencephalogram information Ib of the subject 110 during the provision of the content 120. The information acquisition unit 10 may further acquire the electroencephalogram information Ib of the subject 110 before the provision of the content 120.

[0030] The electroencephalogram information Ib may be information that reproduces at least a part of the time waveform of the electroencephalogram of the subject 110. The electroencephalogram information Ib may include data obtained by sampling the time waveform of the electroencephalogram, may include data indicating the magnitude of the frequency component of the electroencephalogram at one or a plurality of frequencies, and may include other data. For example, the electroencephalogram information Ib includes data indicating the magnitude of at least one component of delta wave (less than 4 Hz), theta wave (4 Hz or more and less than 8 Hz), alpha wave (8 Hz or more and less than 14 Hz), beta wave (14 Hz or more and less than 26 Hz), and gamma wave (26 Hz or more and less than 40 Hz).

[0031] Alpha waves may be further classified into lower alpha waves (more than 8 Hz and less than 10 Hz), middle alpha waves (more than 10 Hz and less than 12 Hz), and higher alpha waves (more than 12 Hz and less than 14 Hz) according to the frequency band. The electroencephalogram information Ib may include data indicating the magnitude of at least one of the lower alpha waves, middle alpha waves, and higher alpha waves.

[0032] Beta waves may be further classified into lower beta waves (more than 14 Hz and less than 18 Hz) and higher beta waves (more than 18 Hz and less than 26 Hz) according to the frequency band. The electroencephalogram information Ib may include data indicating the magnitude of at least one of the lower beta waves and higher beta waves.

[0033] The electroencephalogram information Ib may include information on the time waveforms of one or more electroencephalograms measured at one or more positions on the head including the head and face of the subject 110. For example, the electroencephalogram information Ib may be obtained by measuring the time waveforms of the potentials of electrodes arranged at equal intervals near the scalp of the subject 110, such as the international 10-20 method, or may be obtained by other methods. The plurality of electrodes arranged on the scalp do not have to be at equal intervals. The electrodes may be provided in wearable devices worn on the head of the subject 110, such as a headgear, headphones, earphones, glasses, etc. The electroencephalogram information Ib may be information obtained by wireless communication of an electrical signal at an electrode embedded in the body of the subject 110.

[0034] Let the sum of the amplitudes of the alpha waves, beta waves, theta waves, gamma waves, and delta waves at a certain timing be the overall amplitude As. As an example, if the ratio of the amplitude of the delta waves of the subject 110 to the overall amplitude As is greater than any of the ratios of the amplitude of the alpha waves to the overall amplitude As, the ratio of the amplitude of the beta waves to the overall amplitude As, the ratio of the amplitude of the theta waves to the overall amplitude As, and the ratio of the amplitude of the gamma waves to the overall amplitude As, it can be inferred that the subject 110 is in a sleeping state.

[0035] The state estimation unit 20 estimates the state S of the subject 110 for whom the content 120 is provided, based on the brain wave information Ib of the subject 110. The providing unit 30 may provide the state S to the provider 112. Thereby, the provider 112 can recognize the state S of the subject 110. By recognizing the state S of the subject 110, the provider 112 can control the progress mode of the content 120. For example, when the subject 110 is in a state S with low or decreasing comprehension, the provider 112 may stop the progress of the content 120 and perform additional explanation of the content 120 to increase the comprehension. For example, when the subject 110 is in a state S with high or increasing comprehension, the provider 112 can continue the progress of the content while confirming the comprehension.

[0036] The brain wave information Ib may reflect the state S of the subject 110 based on the comprehension of the content 120. The state estimation device 100 estimates the state S based on the brain wave information Ib of the subject 110. Therefore, the user (for example, the provider 112) of the state estimation device 100 can recognize the state S of the subject 110.

[0037] FIG. 5 is a diagram showing an example of the information acquisition unit 10. The information acquisition unit 10 may have an electroencephalograph capable of measuring the brain wave information Ib, or may have a communication device that acquires the brain wave information Ib measured by an external electroencephalograph. The information acquisition unit 10 in this example is a headgear type electroencephalograph. The information acquisition unit 10 may be an earphone type electroencephalograph. In this example, the subject 110 may be provided with the content 120 while wearing a headgear type or earphone type electroencephalograph. Thereby, the information acquisition unit 10 acquires the brain wave information Ib of the subject 110 in the state where the content 120 is provided.

[0038] When the information acquisition unit 10 is a headgear type electroencephalograph, the content control unit 40 and the control unit 90 do not have to be housed in the housing of the headgear. The brain wave information Ib acquired by the information acquisition unit 10 may be wirelessly transmitted to the control unit 90.

[0039] The information acquisition unit 10 may further acquire the biological information of the subject 110 to whom the content 120 is provided. Let the biological information be biological information Ig. The information acquisition unit 10 may further acquire the biological information Ig before the provision of the content 120. The biological information Ig may include at least one of the heartbeat information, sweating amount information, and body temperature information of the subject 110. The biological information Ig of the subject 110 may be acquired by a sensor provided in a wearable device worn by the subject 110.

[0040] The state estimation unit 20 may estimate the state S based on the brain wave information Ib and the biological information Ig. The state estimation unit 20 may estimate the state S of the subject 110 based on the change from the brain wave information Ib before the provision of the content 120 to the brain wave information Ib during the provision and the biological information Ig.

[0041] Let the magnitude of the first power spectrum in the heartbeat of the subject 110 be LF, and the magnitude of the second power spectrum be HF. The frequency band of the second power spectrum is a higher frequency band than the frequency band of the first power spectrum. The frequency band of the first power spectrum and the frequency band of the second power spectrum do not have to overlap. The frequency band of the first power spectrum is, for example, 0.04 - 0.15 Hz. The frequency band of the second power spectrum is, for example, 0.15 - 0.4 Hz.

[0042] Let the change from the ratio of the amplitude of the brain wave in a predetermined frequency band in the brain wave information Ib before the provision of the content 120 to the total amplitude As of the brain wave to the ratio of the amplitude of the brain wave in the predetermined frequency band in the brain wave information Ib during the provision of the content 120 to the total amplitude As of the brain wave be the change C. The state estimation unit 20 may estimate the state S based on the change C and the ratio of LF to HF (LF / HF).

[0043] Let the ratio of LF to HF (LF / HF) during the provision of content 120 be the ratio Rag. Let the predetermined threshold value of the ratio Rag be the heart rate threshold value Pth. As an example, when the ratio of the total amplitude As of the sum of the amplitudes of the high beta waves and gamma waves of subject 110 during the provision of content 120 to the total amplitude As is greater than the ratio to the total amplitude As before the provision of content 120, and the ratio of LF to HF (LF / HF) during the provision of content 120 is equal to or higher than the heart rate threshold value Pth, it can be inferred that the irritation state, hypersensitive state, or stress state of subject 110 is increasing. When the ratio of LF to HF (LF / HF) is equal to or higher than the heart rate threshold value Pth, subject 110 may be determined to be in a state where the sympathetic nerve is dominant over the parasympathetic nerve. When the ratio of LF to HF (LF / HF) is less than the heart rate threshold value Pth, subject 110 may be determined to be in a state where the parasympathetic nerve is dominant over the sympathetic nerve. The heart rate threshold value Pth may be 2, may be 3, may be 4, or may be 5.

[0044] As an example, when the ratio of the total amplitude As of the sum of the amplitudes of the high beta waves and gamma waves of subject 110 during the provision of content 120 to the total amplitude As is greater than the ratio to the total amplitude As before the provision of content 120, and the ratio of LF to HF (LF / HF) during the provision of content 120 is less than the heart rate threshold value Pth, it can be inferred that the excitement state of subject 110 is increasing.

[0045] The state estimation unit 20 may estimate the state S based on the magnitude relationship between the ratio Rag and the heart rate threshold value Pth and the change C. Let the ratio of the total amplitude As of the sum of the amplitudes of the high beta waves and gamma waves of subject 110 before the provision of content 120 be the ratio Ra1. Let the ratio of the total amplitude As of the sum of the amplitudes of the high beta waves and gamma waves of subject 110 during the provision of content 120 be the ratio Ra2.

[0046] When the ratio Ra2 is greater than the ratio Ra1 and the ratio Rag is equal to or greater than the heart rate threshold Pth, the state estimation unit 20 may estimate that the stress on the subject 110 with respect to the content 120 is increasing. When the ratio Ra2 is greater than the ratio Ra1 and the ratio Rag is less than the heart rate threshold Pth, the state estimation unit 20 may estimate that the degree of immersion of the subject 110 with respect to the content 120 is increasing.

[0047] FIG. 6 is a diagram showing an example of the state S of the subject 110 estimated by the state estimation unit 20. The state S may include a plurality of states (first state S1 to nth state Sn) of the subject 110. In this example, the state S includes four states (first state S1 to fourth state S4) of the subject 110. In FIG. 6, the brain wave of the low frequency f1 refers to at least one of a delta wave, a theta wave, a low alpha wave, and a middle alpha wave, and the brain wave of the high frequency f2 refers to at least one of a high alpha wave, a low beta wave, a high beta wave, and a gamma wave.

[0048] Let the amplitude of the brain wave of the subject 110 in a predetermined frequency band be the amplitude Af. Let the amplitude Af1 be the amplitude of the brain wave of the subject 110 before the content 120 is provided. Let the amplitude Af2 be the amplitude of the brain wave of the subject 110 during the provision of the content 120. The brain wave in the predetermined frequency band may be at least one of a low alpha wave, a middle alpha wave, a high alpha wave, a low beta wave, a high beta wave, a gamma wave, and a theta wave.

[0049] In this example, the first state S1 is a state S of the subject 110 when, in the brain wave of the low frequency f1, the ratio of the overall amplitude As of the amplitude Af2 to the overall amplitude As of the amplitude Af1 is greater, and the ratio of LF to HF (LF / HF) (the ratio Rag described above) during the provision of the content 120 is equal to or greater than the threshold value Rth. When the subject 110 is in the first state S1, it can be inferred that the subject 110 is in a state S where fatigue or drowsiness is increasing. Therefore, the state estimation unit 20 may estimate the state S of the subject 110 as the first state S1 in which the degree of interest in the content 120 is decreasing.

[0050] In this example, the second state S2 is a state S of the subject 110 when, in the brain wave of the low frequency f1, the ratio of the amplitude Af2 to the overall amplitude As is larger than the ratio of the amplitude Af1 to the overall amplitude As, and the ratio Rag is less than the threshold value Rth. When the subject 110 is in the second state S2, it can be inferred that the subject 110 is in a state S where the degree of relaxation is increasing. Therefore, the state estimation unit 20 may estimate the state S of the subject 110 as the second state S2 in which the degree of confidence in the content 120 is increasing.

[0051] In this example, the third state S3 is a state S of the subject 110 when, in the brain wave of the high frequency f2, the ratio of the amplitude Af2 to the overall amplitude As is larger than the ratio of the brain wave Af1 to the overall amplitude As, and the ratio Rag is greater than or equal to the threshold value Rth. When the subject 110 is in the third state S3, it can be inferred that the subject 110 is in a state S where irritation, nervousness, or stress is increasing. Therefore, the state estimation unit 20 may estimate the state S of the subject 110 as the third state S3 in which irritation, nervousness, or stress with respect to the content 120 is increasing.

[0052] In this example, the fourth state S4 is a state S of the subject 110 when, in the brain wave of the high frequency f2, the ratio of the amplitude Af2 to the overall amplitude As is larger than the ratio of the brain wave Af1 to the overall amplitude As, and the ratio Rag is less than the threshold value Rth. When the subject 110 is in the fourth state S4, it can be inferred that the subject 110 is in a state S where the degree of immersion is increasing. Therefore, the state estimation unit 20 may estimate the state S of the subject 110 as the fourth state S4 in which the degree of interest in the content 120 is increasing.

[0053] FIG. 7 is a diagram showing another example of a situation where content 120 is provided to subject 110. In this example, content 120 is provided online to a plurality of subjects 110. In the example of FIG. 7, content 120 is provided through terminal 130-1 of subject 110-1, and content 120 is provided through terminal 130-2 of subject 110-2. The plurality of subjects 110 may be located in different places. In the example of FIG. 7, content 120 is provided to subject 110-1 and subject 110-2 at their respective homes.

[0054] The information acquisition unit 10 may acquire the electroencephalogram information Ib of each of the plurality of subjects 110 to whom the common content 120 is provided. In the examples of FIGS. 1 and 7, the electroencephalogram information Ib of subject 110-1 is defined as electroencephalogram information Ib1, and the electroencephalogram information Ib of subject 110-2 is defined as electroencephalogram information Ib2. In the examples of FIGS. 1 and 7, the information acquisition unit 10 acquires the electroencephalogram information Ib1 of subject 110-1 and the electroencephalogram information Ib2 of subject 110-2 to whom the common content 120 is provided.

[0055] The common content 120 may be the same class, lecture, etc. The common content 120 may be video, image, or audio related to the same viewing object.

[0056] When it is said that the common content 120 is provided, in the case where the same class, lecture, etc. are provided to a plurality of subjects 110, it may refer to a situation where the same class, lecture, etc. are provided at a common location (example of FIG. 1). When it is said that the common content 120 is provided, in the case where video, image, or audio related to the same class, lecture, etc. are provided to a plurality of subjects 110, it may refer to a case where the same video, the same image, or the same audio is provided to a plurality of subjects 110 existing in different places (example of FIG. 7), and it may also refer to a case where it is provided to a plurality of subjects 110 at a common location.

[0057] When the same video, image, or audio is provided to a plurality of subjects 110 existing in different locations (example in Fig. 7), it may refer to the case where the same such video, such image, or such audio is provided to each of the plurality of subjects 110 via the Internet. When the same video, image, or audio is provided to each of the plurality of subjects 110 via the Internet, the same video, image, or audio may be provided to each of the plurality of subjects 110 at different timings or at the same timing. The case where the same video, image, or audio is provided at different timings via the Internet is, for example, the case where e-learning content 120 is provided to the subject 110.

[0058] The state estimation unit 20 may estimate the state S of each of the plurality of subjects 110 based on the electroencephalogram information Ib of each of the plurality of subjects 110. In the examples of Figs. 1 and 7, the state estimation unit 20 estimates the state S of the subject 110-1 based on the electroencephalogram information Ib1 of the subject 110-1, and estimates the state S of the subject 110-2 based on the electroencephalogram information Ib2 of the subject 110-2.

[0059] The content control unit 40 may control the content 120 based on the state S of each of the plurality of subjects 110. The content control unit 40 may change the content 120 according to the state S of each of the plurality of subjects 110. When the same video, image, or audio related to a class, lecture, etc. is provided to the plurality of subjects 110 at different locations (example in FIG. 7), the content control unit 40 may change the content 120 provided to one subject 110 to the content 120 corresponding to the state S of the one subject 110, and change the content 120 provided to another subject 110 to the content 120 corresponding to the state S of the other subject 110. The content control unit 40 may make the content 120 provided to one subject 110 different from the content 120 provided to another subject 110 according to the state S. Thereby, the content control unit 40 can provide the content 120 corresponding to the state S to each of the plurality of subjects 110. The content 120 controlled by the content control unit 40 may be provided wirelessly, such as via the Internet, to the subjects 110 existing in different locations.

[0060] When the state S of the subject 110 is the first state S1 or the third state S3 (see FIG. 6), the content control unit 40 may control the content 120 to provide the subject 110 with the content 120 corresponding to the state S of the subject 110 and facilitating understanding. For example, when the e-learning content 120 is provided to the subject 110, the content control unit 40 may change the content 120 provided via the Internet to the content 120 facilitating understanding, or may add other content 120 facilitating understanding to the content 120 being provided. Adding other content 120 facilitating understanding may include, for example, adding a URL (Uniform Resource Locator) indicating the location of the content 120 facilitating understanding.

[0061] When the state S of the target person 110 is the second state S2 or the fourth state S4 (see FIG. 6), the content control unit 40 may control the content 120 according to the state S of the target person 110, and provide the content 120 that is more challenging and rewarding for the target person 110. For example, when the content 120 of e-learning is provided to the target person 110, the content control unit 40 may change the content 120 provided via the Internet to a more challenging and rewarding content 120, or add other more challenging and rewarding content 120 to the content 120 being provided.

[0062] The content control unit 40 may generate a task for the target person 110 based on the state S of the target person 110, and control the content 120 to provide the generated task to the target person 110. The task based on the state S may refer to a task for promoting the understanding degree of the content 120 of the target person 110. The task for the target person 110 may be created in advance. A plurality of tasks for the target person 110 may be created in advance according to a plurality of assumed states S. The tasks created in advance may be stored in the storage unit 50 (see FIG. 4). The content control unit 40 may select one of a plurality of tasks created in advance according to the state S, and control the content 120 to provide the selected task to the target person 110.

[0063] In the example of FIG. 7, the information acquisition unit 10 may further acquire identification information for identifying each terminal 130 of a plurality of target persons 110 to whom the content 120 is provided. Let the identification information be identification information Id. The content control unit 40 may control the content 120 based on the respective identification information Id and the state S of the plurality of target persons 110. The content control unit 40 may change the content 120 provided to each of the plurality of target persons 110 to the content 120 corresponding to the respective state S based on the identification information Id, and may add other content 120 for promoting understanding. Adding other content 120 for promoting understanding may include, for example, adding a URL indicating the location of the content 120 for promoting understanding. Thereby, when the state S of the target person 110 is the first state S1 or the third state S3 (see FIG. 6), the state S of the target person 110 may change to the second state S2 or the fourth state S4 (see FIG. 6).

[0064] The content control unit 40 may divide the plurality of target persons 110 into stages based on their respective states S. Dividing the plurality of target persons 110 into stages means, for example, dividing the plurality of target persons 110 into groups such as seniors, intermediates, and beginners. The content control unit 40 may control the content 120 provided to each group of the plurality of target persons 110 divided into stages to the content 120 corresponding to the state S of the target person 110 at each stage. Thereby, it becomes easier to provide the optimal content 120 corresponding to the state S to the target persons 110 in each group.

[0065] The providing unit 30 (see FIG. 4) may provide other content to the provider 112 of the content 120 to make the state S of the target person 110 a predetermined state S based on the state S of the target person 110. The providing unit 30 may provide other content to make the states S of the plurality of target persons 110 a predetermined state S based on the respective states S of the plurality of target persons 110. The predetermined state S may refer to the second state S2 or the fourth state S4 (see FIG. 6). When the target person 110 is in the first state S1 or the third state S3 (see FIG. 6), the other content for making the target person 110 into the second state S2 or the fourth state S4 is, for example, "Let's add intonation to your way of speaking", "Let's explain with easier-to-understand examples", etc.

[0066] FIG. 8 is a diagram showing an example of the relationship among the state S, the test result Rt, and the understanding state Cs. The state S is the state S of the target person 110 estimated by the state estimation unit 20 as described above. In this example, the state S is denoted as the estimated state S. The test result Rt is the result of a test regarding the degree of understanding of the content 120 by the target person 110. The test may be performed after the content 120 is provided to the target person 110. In the example of FIG. 7, the test may be provided to each of the terminals 130 of the plurality of target persons 110, and each of the plurality of target persons 110 may take the test on the terminal 130. The information acquisition unit 10 may acquire the test result Rt.

[0067] The state estimation unit 20 may determine the understanding state Cs of the target person 110 regarding the content 120 based on the estimated state S and the test result Rt. The understanding state Cs may include a plurality of states regarding the understanding of the content 120 by the target person 110. In this example, the understanding state Cs includes four states Cs1 to state Cs4.

[0068] When the estimated state S is the second state S2 or the fourth state S4 (a state indicating understanding), and the test result Rt is less than a predetermined correct answer rate threshold, the state estimation unit 20 may determine that the understanding state Cs of the subject 110 is a state in which the subject 110 misunderstands the content 120 or a state in which the subject makes a careless mistake and answers the test incorrectly. This state is the state Cs1. The predetermined correct answer rate threshold may be, for example, 70% of the correct answer rate, or may be 80%, or may be 90%. When the estimated state S is the second state S2 or the fourth state S4 (a state indicating understanding), and the test result Rt is equal to or higher than the predetermined correct answer rate threshold, the state estimation unit 20 may determine that the understanding state Cs of the subject 110 is a state in which the subject 110 understands the content 120. This state is the state Cs2.

[0069] When the estimated state S is the first state S1 or the third state S3 (a state indicating non-understanding), and the test result Rt is less than a predetermined correct answer rate threshold, the state estimation unit 20 may determine that the understanding state Cs of the subject 110 is a state in which the subject 110 does not understand the content 120. This state is the state Cs3. When the estimated state S is the first state S1 or the third state S3 (a state indicating non-understanding), and the test result Rt is equal to or higher than the predetermined correct answer rate threshold, the state estimation unit 20 may determine that the understanding state Cs of the subject 110 is a semi-understanding state. Let this semi-understanding state be the semi-understanding state Cs4. The semi-understanding state Cs4 will be described later.

[0070] FIG. 9 is a diagram showing an example of the understanding state inference model 72. The state learning unit 70 (see FIG. 4) performs machine learning on the relationship between the respective estimated states S of a plurality of subjects 110 and the respective test results Rt of the plurality of subjects 110. The state learning unit 70 generates the understanding state inference model 72 by performing machine learning on the relationship between the respective estimated states S of a plurality of subjects 110 and the respective test results Rt of the plurality of subjects 110. The understanding state inference model 72 infers the understanding state Cs based on the estimated state S. The understanding state inference model 72 may be stored in the storage unit 50.

[0071] The state learning unit 70 machine-learns the relationship between the estimated state S and the test result Rt. Therefore, the understanding state Cs inferred by the understanding state inference model 72 has a high probability of being the true understanding state Cs of the subject 110. The understanding state Cs inferred by the understanding state inference model 72 may be provided by the providing unit 30. As a result, the provider 112 of the content 120 can easily recognize the true understanding state Cs of the subject 110.

[0072] When the state learning unit 70 generates the understanding state inference model 72 by machine-learning the estimated state S and the test result Rt related to the quasi-understanding state Cs4, the state estimation unit 20 may estimate the quasi-understanding state Cs4 as the state in which the subject 110 understands the content 120. When the state learning unit 70 generates the understanding state inference model 72 by machine-learning the estimated state S and the test result Rt related to the quasi-understanding state Cs4 refers to the case where the number of cases determined as the quasi-understanding state Cs4 is a certain number, and the quasi-understanding state Cs4 can be estimated as the state in which the subject 110 understands the content 120. In the case where the quasi-understanding state Cs4 is estimated as the state of understanding the content 120, when the state estimation unit 20 estimates the estimated state S related to the quasi-understanding state Cs4 based on the brain wave information Ib, the brain wave information Ib in this case may be estimated as the brain wave information Ib when the subject 110 understands the content 120.

[0073] FIG. 10 is a flowchart showing an example of a state estimation method according to an embodiment of the present invention. The state estimation method according to an embodiment of the present invention will be described by taking the state estimation device 100 shown in FIG. 4 as an example. The state estimation method includes an information acquisition step S100 and a state estimation step S102. The state estimation method may include a content control step S104, a providing step S106, an evaluation step S108, and a state learning step S110.

[0074] The information acquisition step S100 is a step in which the information acquisition unit 10 acquires the electroencephalogram information Ib of the subject 110 for whom the content 120 is provided. The state estimation step S102 is a step in which the state estimation unit 20 estimates the state S of the subject 110 for whom the content 120 is provided based on the electroencephalogram information Ib of the subject 110.

[0075] The information acquisition step S100 may be a step in which the information acquisition unit 10 further acquires the biological information Ig of the subject 110 for whom the content 120 is provided. The state estimation step S102 may be a step in which the state estimation unit 20 estimates the state S based on the electroencephalogram information Ib and the biological information Ig.

[0076] The information acquisition step S100 may be a step in which the information acquisition unit 10 acquires the electroencephalogram information Ib of the subject 110 before and during the provision of the content 120. The state estimation step S102 may be a step in which the state estimation unit 20 estimates the state based on the change from the electroencephalogram information Ib before the provision of the content 120 to the electroencephalogram information Ib during the provision of the content 120 and the biological information Ig.

[0077] The state estimation step S102 may be a step in which the state estimation unit 20 estimates the state S based on the change (the above-described change C) from the ratio of the amplitude Af1 to the overall amplitude As in the electroencephalogram information Ib before the provision of the content 120 to the ratio of the amplitude Af2 to the overall amplitude As in the electroencephalogram information Ib during the provision of the content 120 and the ratio of LF to HF (LF / HF) (the above-described ratio Rag) during the provision of the content 120. The state estimation step S102 may be a step in which the state estimation unit 20 estimates the state S based on the magnitude relationship between the ratio Rag and the heart rate threshold Pth and the change C.

[0078] The information acquisition step S100 may be a step in which the information acquisition unit 10 acquires the electroencephalogram information Ib of each of a plurality of subjects 110 for whom common content 120 is provided. The state estimation step S102 may be a step in which the state estimation unit 20 estimates the state S of each of the plurality of subjects 110 based on the electroencephalogram information Ib of each of the plurality of subjects 110. The content control step S104 is a step in which the content control unit 40 controls the content 120 based on the state S of each of the plurality of subjects 110.

[0079] The information acquisition step S100 may be a step in which the information acquisition unit 10 further acquires identification information Id for identifying each of the terminals 130 of the plurality of subjects 110 for which the content 120 is provided. The content control step S104 may be a step in which the content control unit 40 controls the content 120 based on the identification information Id and the state S of each of the plurality of subjects 110. The content control step S104 may be a step in which the content control unit 40 divides the plurality of subjects 110 into stages based on the state S of each, and controls the content 120 provided to each group of the plurality of subjects 110 divided into stages to the content 120 corresponding to the state S at each stage.

[0080] The provision step S106 is a step in which the provision unit 30 provides other content for making the state S of the subject 110 into a predetermined state S to the provider 112 of the content 120 based on the state S of the subject 110.

[0081] The information acquisition step S100 may be a step in which the information acquisition unit 10 further acquires the test result Rt regarding the degree of understanding of the content 120 of the subject 110. The state estimation step S102 may be a step in which the understanding state Cs of the subject 110 regarding the content 120 is determined based on the estimated state which is the state S estimated by the state estimation unit 20 and the test result Rt.

[0082] The state learning step S110 is a step in which the state learning unit 70 generates an understanding state inference model 72 that infers an understanding state Cs based on an estimated state S by machine learning the relationship between the respective estimated states S of a plurality of subjects 110 and the respective test results Rt of the plurality of subjects 110.

[0083] FIG. 11 is a diagram showing an example of a computer 2200 in which a state estimation device 100 according to an embodiment of the present invention may be wholly or partially embodied. The program installed in the computer 2200 can cause the computer 2200 to perform operations associated with the state estimation device 100 according to the embodiment of the present invention, or function as one or a plurality of sections of the state estimation device 100, or execute the operations or the one or the plurality of sections, or cause the computer 2200 to execute each stage (see FIG. 10) of the state estimation method according to the present invention. The program may be executed by the CPU 2212 so as to cause the computer 2200 to perform specific operations associated with some or all of the blocks in the flowcharts (FIG. 10) and block diagrams (FIG. 4) described herein.

[0084] A computer 2200 according to an embodiment of the present invention includes a CPU 2212, a RAM 2214, a graphic controller 2216, and a display device 2218. The CPU 2212, the RAM 2214, the graphic controller 2216, and the display device 2218 are interconnected by a host controller 2210. The computer 2200 further includes input / output units such as a communication interface 2222, a hard disk drive 2224, a DVD-ROM drive 2226, and an IC card drive. The communication interface 2222, the hard disk drive 2224, the DVD-ROM drive 2226, and the IC card drive are connected to the host controller 2210 via an input / output controller 2220. The computer further includes legacy input / output units such as a ROM 2230 and a keyboard 2242. The ROM 2230 and the keyboard 2242 are connected to the input / output controller 2220 via an input / output chip 2240.

[0085] The CPU 2212 controls each unit by operating according to programs stored in the ROM 2230 and the RAM 2214. The graphic controller 2216 causes image data generated by the CPU 2212 to be displayed on the display device 2218 by acquiring the image data in a frame buffer or the like provided in the RAM 2214 or in the RAM 2214.

[0086] The communication interface 2222 communicates with other electronic devices via a network. The hard disk drive 2224 stores programs and data used by the CPU 2212 in the computer 2200. The DVD-ROM drive 2226 reads a program or data from the DVD-ROM 2201 and provides the read program or data to the hard disk drive 2224 via the RAM 2214. The IC card drive reads programs and data from an IC card or writes programs and data to an IC card.

[0087] The ROM 2230 stores a boot program or the like executed by the computer 2200 when activated, or a program dependent on the hardware of the computer 2200. The input / output chip 2240 may be connected to the input / output controller 2220 via various input / output units through a parallel port, a serial port, a keyboard port, a mouse port, or the like.

[0088] The program is provided by a computer-readable medium such as a DVD-ROM 2201 or an IC card. The program is read from the computer-readable medium, installed in the hard disk drive 2224, the RAM 2214, or the ROM 2230, which is also an example of a computer-readable medium, and executed by the CPU 2212. The information processing described in these programs is read by the computer 2200, resulting in the cooperation between the programs and the various types of hardware resources described above. The apparatus or method may be configured by realizing the operation or processing of information according to the use of the computer 2200.

[0089] For example, when communication is executed between the computer 2200 and an external device, the CPU 2212 may execute a communication program loaded in the RAM 2214 and instruct the communication interface 2222 to perform communication processing based on the processing described in the communication program. The communication interface 2222 reads the transmission data stored in the transmission buffer processing area provided in a recording medium such as the RAM 2214, the hard disk drive 2224, the DVD-ROM 2201, or the IC card under the control of the CPU 2212, transmits the read transmission data to the network, or writes the received data received from the network to the reception buffer processing area or the like provided on the recording medium.

[0090] The CPU 2212 may read all or necessary parts of files or databases stored in external recording media such as a hard disk drive 2224, a DVD-ROM drive 2226 (DVD-ROM 2201), an IC card, etc. into the RAM 2214. The CPU 2212 may execute various types of processing on the data on the RAM 2214. The CPU 2212 may then write back the processed data to the external recording media.

[0091] Various types of information such as various types of programs, data, tables, and databases may be stored in a recording medium and processed. The CPU 2212 may execute various types of processing on the data read from the RAM 2214, including various types of operations, information processing, conditional judgment, conditional branch, unconditional branch, information search or replacement, etc. specified by the program instruction sequence described in this disclosure. The CPU 2212 may write back the result to the RAM 2214.

[0092] The CPU 2212 may search for information in files, databases, etc. in the recording medium. For example, when a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording medium, the CPU 2212 searches for an entry that matches the condition where the attribute value of the first attribute is specified from among the plurality of entries, reads the attribute value of the second attribute stored in the entry, and by reading the second attribute value, may obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0093] The above-described program or software module may be stored on the computer 2200 or on a computer-readable medium of the computer 2200. A recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can be used as a computer-readable medium. The program may be provided to the computer 2200 by the recording medium.

[0094] As described above, the present invention has been described using embodiments. However, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is obvious to those skilled in the art that various changes or improvements can be made to the above embodiments. It is clear from the description of the claims that forms with such changes or improvements can also be included in the technical scope of the present invention.

[0095] It should be noted that the execution order of each process such as operations, procedures, steps, and stages in the devices, systems, programs, and methods shown in the claims, the specification, and the drawings is not explicitly indicated as "before" or "preceding" etc. in particular, and can be realized in any order unless the output of the previous process is used in the subsequent process. Regarding the operation flow in the claims, the specification, and the drawings, even if it is described using "first," "next," etc. for convenience, it does not mean that it is essential to implement in this order.

Explanation of Reference Numerals

[0096] 10... Information acquisition unit, 20... State estimation unit, 30... Provision unit, 40... Content control unit, 50... Storage unit, 70... State learning unit, 72... Understanding state inference model, 90... Control unit, 100... State estimation device, 110... Subject, 112... Provider, 120... Content, 130... Terminal

Claims

1. An information acquisition unit that acquires electroencephalogram information of a subject to whom content is provided; A state estimation unit that estimates the state of the subject to whom the content is provided based on the electroencephalogram information of the subject; A state estimation device comprising the above.

2. The information acquisition unit further acquires biological information of the subject to whom the content is provided; The state estimation unit estimates the state based on the electroencephalogram information and the biological information; The state estimation device according to claim 1.

3. The information acquisition unit acquires the electroencephalogram information of the subject before and during the provision of the content; The state estimation unit estimates the state based on the change from the electroencephalogram information before the provision of the content to the electroencephalogram information during the provision of the content and the biological information; The state estimation device according to claim 2.

4. The state estimation unit estimates the state based on the ratio of the amplitude of the electroencephalogram in a predetermined frequency band in the electroencephalogram information before the provision of the content to the total amplitude of the electroencephalogram in the same frequency band in the electroencephalogram information during the provision of the content, and the ratio of the magnitude of the first power spectrum in the subject's heartbeat to the magnitude of the second power spectrum; The total amplitude is the sum of the amplitudes of alpha waves, beta waves, theta waves, gamma waves, and delta waves; The frequency band of the second power spectrum is a higher frequency band than the frequency band of the first power spectrum; The state estimation device according to claim 3.

5. The state estimation unit estimates the state based on the ratio of the magnitude of the first power spectrum to the magnitude of the second power spectrum during the provision of the content, the magnitude relationship between the ratio of the magnitude of the first power spectrum to the magnitude of the second power spectrum and a predetermined heartbeat threshold, and the change from the ratio of the amplitude of the electroencephalogram in the frequency band before the provision of the content to the total amplitude of the electroencephalogram in the same frequency band during the provision of the content; the state estimation device according to claim 4.

6. The information acquisition unit acquires the electroencephalogram information of each of a plurality of subjects to whom the same content is provided; The state estimation unit estimates the state of each of the plurality of subjects based on the brain wave information of each of the plurality of subjects, further comprising a content control unit that controls the content based on the state of each of the plurality of subjects, The state estimation device according to any one of claims 1 to 5.

7. The information acquisition unit further acquires identification information for identifying each terminal of the plurality of subjects to which the content is provided, The content control unit controls the content based on the identification information and the state of each of the plurality of subjects, The state estimation device according to claim 6.

8. The content control unit divides the plurality of subjects into stages based on their respective states, and controls the content provided to each group of the plurality of subjects divided into stages to content corresponding to the state at each stage. The state estimation device according to claim 7.

9. The state estimation device according to claim 6, further comprising a providing unit that provides, to a provider of the content, other content for bringing the state of the subject into a predetermined state based on the state of the subject.

10. The information acquisition unit further acquires a test result regarding the degree of understanding of the content by the subject, The state estimation unit determines the content understanding state of the subject based on an estimated state that is the state estimated by the state estimation unit and the test result, The state estimation device according to any one of claims 1 to 5.

11. The state estimation device according to claim 10, further comprising a state learning unit that generates an understanding state inference model for inferring the understanding state based on the estimated state by machine learning the relationship between the estimated state of each of the plurality of subjects and the test result of each of the plurality of subjects.

12. An information acquisition step in which an information acquisition unit acquires brain wave information of a subject for whom content is provided; A state estimation step in which a state estimation unit estimates the state of the subject for whom the content is provided based on the brain wave information of the subject; A state estimation method comprising:

13. In a computer, An information acquisition step of acquiring brain wave information of a subject for whom content is provided; A state estimation step of estimating the state of the subject for whom the content is provided based on the electroencephalogram information of the subject; A state estimation program for causing the above to be executed.

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