Information processing device, system, information processing method, and program

Abstract

An information processing device (10) comprises an acquisition unit (110), an analysis unit (130), and an output unit (150). The acquisition unit (110) acquires measurement information indicating brain waves and / or vital signs of a subject during learning, and attribute information of the subject. The analysis unit (130) generates state information regarding the state of the subject using the measurement information and the attribute information. The output unit (150) outputs the state information.

Description

Information processing device, system, information processing method, and program

[0001] The present invention relates to an information processing device, a system, an information processing method, and a program.

[0002] In the field of learning and education, attempts are being made to estimate the learner's state and use this information to improve learning.

[0003] Patent Document 1 describes estimating a subject's concentration level by analyzing changes in the subject's vital data obtained by a vital sensor.

[0004] Patent Document 2 describes a method in which a wearable sensor attached to the learner's ear is used to detect the learner's heart rate, and based on the obtained heart rate information, it is determined whether the learner is concentrating.

[0005] Patent Document 3 describes a method for evaluating a learner's level of understanding and concentration by analyzing the learner's writing activities based on the learner's writing data.

[0006] JP 2021-23492 A JP 2022-77300 A International Publication No. 2014 / 141414

[0007] However, the techniques described in Patent Documents 1 to 3 above do not take into account the attributes of the learners in the analysis, which makes it difficult to correctly estimate the status of learners whose characteristics are different from one another.

[0008] In view of the above-described problems, an example of an object of the present invention is to provide an information processing device, a system, an information processing method, and a program that can perform more accurate state estimation by taking into account the attributes of a subject.

[0009] According to one aspect of the present invention, there is provided an information processing device comprising: an acquisition means for acquiring measurement information indicating at least one of electroencephalograms and vital signs of a subject while the subject is studying, and attribute information of the subject; an analysis means for generating status information regarding the status of the subject using the measurement information and the attribute information; and an output means for outputting the status information.

[0010] According to one aspect of the present invention, a system is provided comprising: the above-mentioned information processing device; a measuring device that measures at least one of the subject's brain waves and vital signs; and a terminal that outputs the status information using the output means.

[0011] According to one aspect of the present invention, an information processing method is provided in which one or more computers acquire measurement information indicating at least one of electroencephalograms and vital signs of a subject while the subject is studying, and attribute information of the subject, use the measurement information and the attribute information to generate status information regarding the state of the subject, and output the status information.

[0012] According to one aspect of the present invention, a program is provided that causes a computer to function as: an acquisition means for acquiring measurement information indicating at least one of electroencephalograms and vital signs of a subject while the subject is studying, and attribute information of the subject; an analysis means for generating status information regarding the status of the subject using the measurement information and the attribute information; and an output means for outputting the status information.

[0013] According to one aspect of the present invention, it is possible to provide an information processing device, a system, an information processing method, and a program that can perform more accurate state estimation by taking into account the attributes of a subject.

[0014] FIG. 1 is a diagram illustrating an overview of an information processing device according to a first embodiment. FIG. 2 is a diagram illustrating an overview of a system according to the first embodiment. FIG. 3 is a block diagram illustrating an example of the functional configuration of a system according to the first embodiment. FIG. 4 is a diagram illustrating an example of the configuration of subject information. FIG. 5 is a diagram illustrating an example of the display of status information by a terminal. FIG. 6 is a diagram illustrating a computer for realizing an information processing device. FIG. 7 is a diagram illustrating an overview of an information processing method according to the first embodiment. FIG. 8 is a diagram illustrating an example of the functional configuration of a system according to a second embodiment. FIG. 9 is a diagram illustrating an example of an image displayed on a terminal according to the second embodiment. FIG. 10 is a flowchart illustrating a processing flow performed by an analysis unit according to a third example. FIG. 11 is a flowchart illustrating another example of a processing flow performed by an analysis unit according to the third example.

[0015] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In all the drawings, like components are designated by like reference numerals, and their description will be omitted where appropriate.

[0016] First Embodiment FIG. 1 is a diagram illustrating an overview of an information processing device 10 according to a first embodiment. The information processing device 10 includes an acquisition unit 110, an analysis unit 130, and an output unit 150. The acquisition unit 110 acquires measurement information indicating at least one of an electroencephalogram and a vital sign of a subject during learning, and attribute information of the subject. The analysis unit 130 generates status information regarding the status of the subject using the measurement information and the attribute information. The output unit 150 outputs the status information.

[0017] According to this information processing device 10, more accurate state estimation can be performed by taking into account the attributes of the subject.

[0018] A detailed example of the information processing device 10 will be described below.

[0019] For example, in educational settings such as schools, teachers instruct multiple students and children. The students and children receiving instruction include children with a variety of characteristics. Some children require special support. In such circumstances, it is not easy for teachers to provide appropriate instruction for each child. Sometimes, teachers do not have sufficient knowledge about how to teach children who require special support. If teachers can easily grasp the condition of each child and provide instruction that is appropriate for each, it will benefit both the teacher and the children receiving instruction. In other words, for teachers, it will reduce the difficulties caused by insufficient instruction. For children, it will avoid ineffective instruction due to a lack of appropriate instruction.

[0020] In this embodiment, the target person is a learner, for example, at least one of a student (junior high school student), a child (elementary school student), and a student at a university, graduate school, vocational school, etc. The target person's age is not particularly limited, and may be an adult or a child (for example, 18 years old or younger). The information processing device 10 is particularly suitable for targeting students and children at elementary schools, junior high schools, high schools, special needs schools, etc. that provide education to various children.

[0021] The learning that the subject undertakes includes the study of subjects or courses, hobbies, qualifications, or skills. The subject can study under the guidance of an instructor such as a teacher or lecturer. There are no particular limitations on the place where the subject studies. For example, the subject may study in a classroom at school, at home, or in another study room. Examples of "during study" include, for example, during class, during a lecture, when the subject is receiving instruction, and when the subject is studying on their own.

[0022] The instructor may instruct the subject face-to-face in a classroom or the like, or may instruct the subject remotely online. Alternatively, the instructor may instruct the subject in a virtual space such as the Metaverse. The instructor may instruct the subject one-on-one, or may instruct multiple subjects simultaneously. When an instructor instructs multiple subjects simultaneously, it is particularly difficult to grasp the detailed status of each subject, so it is particularly preferable to obtain status information using the information processing device 10 according to this embodiment.

[0023] 2 is a diagram illustrating an example of an overview of a system 50 according to this embodiment. The system 50 according to this embodiment includes an information processing device 10, a measurement device 20, and a terminal 30. The measurement device 20 measures at least one of the subject's electroencephalogram and vital signs. The output unit 150 of the information processing device 10 outputs status information to the terminal 30.

[0024] The system 50 can be said to be a teaching support system or a learning support system. The information processing device 10 can communicate with the measurement device 20 via wired or wireless communication. The information processing device 10 can communicate with the terminal 30 via wired or wireless communication. The information processing device 10 may be connected to at least one of the measurement device 20 and the terminal 30 via a communication network.

[0025] As described above, the measuring device 20 measures at least one of the subject's brain waves and vital signs. In this embodiment, the vital signs are, for example, one or more of pulse, heart rate, respiration, blood pressure, surface temperature, and body temperature. The measuring device 20 is a measuring device that measures at least one of the brain waves and vital signs by being worn by the subject or touched by the subject during study. The measuring device 20 may be a contact-type measuring device.

[0026] Examples of the measuring device 20 include earphones, pencil-type devices, head-mounted devices, necklace-type devices, ring-type devices, wristwatches, and wristband-type devices. The system 50 may include multiple measuring devices 20. Furthermore, multiple measuring devices 20 may be provided for one subject. The system 50 may include multiple types of measuring devices 20.

[0027] A subject currently studying wears or uses one or more measuring devices 20. The measuring devices 20 measure at least one of the brain waves and vital signs of the subject wearing or using the measuring device 20. Each measuring device 20 is associated with the subject that the measuring device 20 is intended to measure. Specifically, each subject is assigned unique identification information (hereinafter also referred to as a "personal ID"). When there are multiple subjects, each subject can be identified by the personal ID. The measuring device 20 is pre-associated with the personal ID of the subject that the measuring device 20 is intended to measure.

[0028] The measuring device 20 measures at least one of the brain waves and vital signs of a subject wearing or using the measuring device 20, and generates and outputs measurement information indicating at least one of the brain waves and vital signs. The measurement information may be measurement results at a certain point in time, statistical values ​​(average, maximum, minimum, etc.) of measurement results over a predetermined period of time, or time-series measurement data over a predetermined period of time. At this time, the measuring device 20 outputs the measurement information in association with a personal ID associated with the measuring device 20. This makes it possible to identify which subject each piece of measurement information belongs to. The acquisition unit 110 of the information processing device 10 acquires the measurement information and personal ID output from the measuring device 20.

[0029] Instead of outputting the personal ID, the measurement device 20 may output identification information assigned to the measurement device 20 (hereinafter also referred to as "measurement device ID") in association with the measurement information. The measurement device ID is identification information unique to each measurement device 20. When the system 50 includes multiple measurement devices 20, each measurement device 20 can be identified by the measurement device ID. After acquiring the measurement device ID, the acquisition unit 110 identifies the personal ID corresponding to the measurement device ID using ID reference information that associates the measurement device ID with the personal ID. The acquisition unit 110 then associates the identified personal ID with the measurement information. The ID reference information is stored in advance in a storage device accessible from the acquisition unit 110 (for example, the subject storage unit 120 described below), and the acquisition unit 110 can read and use it. This storage device may be provided inside the information processing device 10 or outside the information processing device 10.

[0030] For example, the measurement device 20 may repeatedly output measurement information to the information processing device 10 at a predetermined interval. Alternatively, measurement may be performed by the measurement device 20 and measurement information may be output in response to a predetermined operation (hereinafter also referred to as a "request operation") being performed on the terminal 30 to request output of status information. Examples of request operations include an operation on the terminal 30 to display status information and an operation to select a desired subject.

[0031] FIG. 3 is a block diagram illustrating the functional configuration of a system 50 according to this embodiment. The information processing device 10 according to this embodiment further includes a subject memory 120, an analysis memory 140, and a status information memory 160. However, at least one of the subject memory 120, the analysis memory 140, and the status information memory 160 may be provided externally to the information processing device 10. The subject memory 120 is accessible from the acquisition unit 110 and stores subject information in advance. The subject information is information in which attribute information is associated with the personal IDs of multiple subjects. The analysis memory 140 is accessible from the analysis unit 130 and stores analysis information required to generate status information in advance. The status information memory 160 stores the generated status information. In the example of FIG. 3, the system 50 includes multiple measurement devices 20 and an imaging device 40. The imaging device 40 can capture an image of the subject. Examples of the imaging device 40 include a fixed camera, a wearable camera, and a VR camera. When the imaging device 40 is a wearable camera, the wearable camera is worn by, for example, an instructor.

[0032] FIG. 4 is a diagram illustrating an example of the configuration of subject information. The attribute information indicates one or more attributes. Each personal ID is associated with one or more attributes. A collection of one or more attributes associated with a subject's personal ID is called the subject's attribute information. The attribute information may include one or more of the following attributes: age, gender, attributes related to personality, attributes related to factors that require support, attributes related to hobbies, and attributes related to preferences.

[0033] In particular, the attribute information preferably includes attributes related to factors for which support is needed. Examples of attributes related to factors for which support is needed include information about the subject's disability, information about the subject's language, information about attendance status, and information indicating that factors for which support are not needed. In particular, the attribute information preferably includes at least one of information about the subject's disability (difficulty) and information about the subject's language as an attribute.

[0034] Examples of information about a subject's disability include visual impairment, hearing impairment, intellectual disability, physical disability, poor health, physical weakness, language disorder, autism, emotional disorder, learning disability, and attention deficit hyperactivity disorder. By including information about a subject's disability in the attribute information, instructors can understand changes in the condition of subjects with disabilities, which are difficult to assess using the same criteria as for children and students without disabilities, and use this information in their instruction. Examples of information about a subject's language include information that the language used in instruction is not the subject's first language, and the subject's level of understanding of the language used in instruction. By including information about a subject's language in the attribute information, instructors can understand the condition of subjects who are international students or foreign nationals and use this information in their instruction.

[0035] The attribute information of each subject can be determined based on the results of a prior questionnaire, test, survey, etc.

[0036] Returning to Fig. 3, when the acquisition unit 110 acquires the measurement information as described above, it acquires attribute information for the personal ID associated with the measurement information. Specifically, the acquisition unit 110 acquires attribute information associated with the personal ID of the subject in the subject information stored in the subject storage unit 120. In this way, the acquisition unit 110 can acquire the measurement information and attribute information of each subject. The acquisition unit 110 may acquire the measurement information and attribute information of multiple subjects all at once, or may acquire them sequentially.

[0037] When the acquisition unit 110 acquires the measurement information and the attribute information, the analysis unit 130 generates status information related to the subject's status using the measurement information and the attribute information. The status information may be information related to at least one of the subject's status at the time of measurement when the measurement information was acquired and the subject's status after the measurement time. The status information indicates, for example, at least one of the subject's emotions, concentration level, and comprehension level. In this embodiment, an example of status information related to the subject's status at the time of measurement when the measurement information was acquired will be described below.

[0038] The analysis unit 130 generates status information by reading out the analysis information stored in the analysis memory unit 140 and using it to analyze the measurement information. The analysis memory unit 140 stores multiple pieces of analysis information, each associated with one attribute or a combination of two or more attributes. That is, analysis information corresponding to the content of the attribute information is prepared in advance and stored in the analysis memory unit 140. The analysis unit 130 selects analysis information to be used from the multiple pieces of analysis information based on the subject's attribute information, and uses the selected analysis information to generate the status information. Specifically, the analysis unit 130 selects analysis information such that one or more attributes indicated in the subject's attribute information match one or more attributes associated with the analysis information used to generate the status information. In this way, analysis is performed using different analysis information depending on the attribute information, allowing analysis appropriate to the subject's attributes.

[0039] However, the analysis memory unit 140 does not necessarily need to store analysis information for all combinations of attributes. It is sufficient that analysis information is stored for at least attributes and combinations of attributes that may be used. If the analysis memory unit 140 does not store analysis information for an attribute combination that completely matches the attribute information, the analysis unit 130 may use analysis information for an attribute combination that is most similar to the attribute information. A method in which the analysis unit 130 generates condition information regarding the subject's condition using the measurement information and attribute information will be described in detail below.

[0040] The output unit 150 outputs the state information generated by the analysis unit 130 to the terminal 30. The terminal 30 is, for example, a terminal used by an instructor. Examples of the terminal 30 include a computer, a smartphone, a tablet, smart glasses, and earphones.

[0041] The output unit 150 can output the status information in real time to a terminal used by the instructor who is instructing the subject at the time the measurement information is obtained. Specifically, the output unit 150 outputs the status information as soon as the status information is generated by the analysis unit 130. The time lag between measurement and display of the measurement information is, for example, within one minute. Additionally, if the measurement is taken during a class, the output unit 150 outputs the measurement information during that class at the latest. By outputting the status information in real time, the instructor can grasp the subject's status and take appropriate action.

[0042] Furthermore, the output unit 150 may further output notification information to the terminal 30 when the state information satisfies a predetermined condition. For example, when the state information indicates a calmness level, the analysis unit 130 determines whether the calmness level is equal to or less than a predetermined threshold. When the calmness level is equal to or less than the predetermined threshold, the analysis unit 130 generates notification information. Furthermore, the output unit 150 further outputs notification information to the terminal 30. On the other hand, when the calmness level exceeds the predetermined threshold, the analysis unit 130 does not generate notification information. Furthermore, the output unit 150 does not output notification information to the terminal 30. In this way, the trainer is alerted when a subject is emotionally aroused and can take necessary measures for the subject. Note that the threshold used here may be included in the analysis information. In this case, a determination is made using different thresholds depending on the attribute information.

[0043] When the terminal 30 acquires the status information from the output unit 150, it outputs the status information so that the user of the terminal 30 can recognize it. For example, the terminal 30 outputs the status information by at least one of audio and display. The terminal 30 may output the status information using AR (Augmented Reality) technology. The user of the terminal 30 is not particularly limited, but may be, for example, at least one of an instructor who is instructing the subject, a supervisor who oversees the subject's learning situation or the instructor's teaching situation, a doctor, and a researcher. The terminal 30 may be used in the same space as the subject, for example, the same classroom, or in a different location from the subject, for example, a separate room.

[0044] For example, if the terminal 30 is a pair of smart glasses, the user of the terminal 30 sees the target person through a light-transmitting display member provided in the smart glasses. In the smart glasses, status information is displayed so as to be superimposed on the target person using AR technology.

[0045] FIG. 5 is a diagram illustrating an example of status information displayed by the terminal 30. In the example of FIG. 5, a teacher is teaching a class while wearing smart glasses, which are the terminal 30. The terminal 30 is equipped with a sensor that detects the teacher's eye movements. When the teacher directs his or her gaze at a specific child among multiple children (subjects) taking the class for a predetermined period of time or longer, the status of that child is displayed in real time. For example, in this diagram, the child's face is circled, and a diagram showing the child's main situation ("STATUS"), the balance between relaxation and stress levels, and the level of each emotion is displayed. Directing one's gaze at a specific child for a predetermined period of time or longer may correspond to the request operation described above.

[0046] Additionally, an alert symbol ("ATTENTION") is displayed for children who require attention, such as those who are confused. Furthermore, the average level of concentration for the entire class ("Class Concentration Level") is displayed.

[0047] When the terminal 30 outputs status information using AR technology, the acquisition unit 110 of the information processing device 10 acquires an image including the subject from the imaging device 40, and the analysis unit 130 identifies the position of the subject using the acquired image. The imaging device 40 is, for example, a camera such as a VR (Virtual Reality) camera. The imaging device 40 may capture images of the subject from multiple directions. The imaging area of ​​the imaging device 40 may be fixed or variable. The imaging device 40 may be worn by the user of the terminal 30 or may be provided in the terminal 30.

[0048] Furthermore, in the subject information stored in the subject storage unit 120, each personal ID is further associated with a feature for facial recognition processing. When the analysis unit 130 of the information processing device 10 acquires the feature of the subject from the subject storage unit 120, it uses the feature to perform facial recognition processing on the image generated by the imaging device 40. This identifies the position of the subject in the image. Furthermore, the position of the subject in real space can be identified using the position of the subject in the image, the position of the imaging device 40, and the relationship between the imaging device 40 and the imaging area. Once the position of the subject is identified, the output unit 150 associates the position of the subject with status information and outputs it to the terminal 30. The terminal 30 can identify the position at which to display the status information on the terminal 30 based on the position of the subject, the position of the terminal 30, and the orientation of the terminal 30. The above-described identification of the subject's position may be performed by the terminal 30.

[0049] As another example, when the terminal 30 is a computer, smartphone, or tablet, an image including the subject is displayed on the display of the terminal 30 together with status information. The image including the subject is captured by, for example, the imaging device 40. As described above, the imaging device 40 may be provided in the terminal 30. Then, by performing face recognition processing as described above on the image including the subject, the position of each subject in the image may be recognized, and the display position of the status information for each subject may be determined based on that position. The face recognition processing and the determination of the display position may be performed by the information processing device 10 or the terminal 30.

[0050] The method by which the analysis unit 130 generates status information regarding the subject's status using the measurement information and attribute information will be described in detail below.

[0051] The analysis unit 130 may generate the state information based on a predetermined rule, or may use a model generated by machine learning. Each example will be described below.

[0052] <First Example> In the first example, the analysis unit 130 generates status information based on predetermined rules. In this case, the analysis information read and used by the analysis unit 130 from the analysis memory unit 140 is information indicating rules for generating status information based on measurement information. The analysis information includes, for example, one or more of a mathematical formula, a condition, and a threshold. The analysis information corresponding to each attribute information can be prepared in advance by collecting and analyzing data indicating the relationship between the electroencephalograms and vital signs of a learner having that attribute and the state of the learner when the electroencephalograms and vital signs were measured.

[0053] <<Electroencephalograms>> When the measurement information includes electroencephalograms, the analysis unit 130 can estimate the subject's state using existing techniques such as the Russell circle model. For example, the analysis unit 130 performs a process to remove noise from the electroencephalogram waveform and then extracts the alpha, gamma, beta, theta, and delta wave components. The analysis unit 130 calculates the activity level and comfort level by applying these components to a predetermined mathematical formula. The analysis unit 130 can then estimate the subject's emotions based on the positions of the calculated activity levels and comfort levels when placed on a two-axis plane with the vertical axis representing activity and the horizontal axis representing comfort level. Here, the analysis information includes, for example, mathematical formulas for calculating the activity level and comfort level from each electroencephalogram component.

[0054] As another example, the analysis unit 130 calculates the ratio of alpha waves to beta waves (β / α) and the fluctuation of components of the electroencephalogram waveform below a predetermined frequency (LF fluctuation).The analysis unit 130 can then estimate emotions based on the positions of the calculated β / α and LF fluctuation when placed on a biaxial plane with β / α on the vertical axis and LF fluctuation on the horizontal axis.Here, the area on the biaxial plane allocated to each emotion can be analysis information.

[0055] <<Vital Signs>> The analysis unit 130 can estimate the subject's condition using one or more vital sign values, such as pulse rate, heart rate, respiration, blood pressure, surface temperature, and body temperature. For example, a lower pulse rate indicates a calmer subject. Furthermore, a decrease in comprehension can be associated with increased impatience and agitation, leading to increased pulse rate, body temperature, blood pressure, and other factors. For example, the analysis unit 130 obtains a score indicating the likelihood that the subject is in that state by substituting one or more vital sign values ​​into a formula prepared for each state. Specifically, a score indicating the level of comprehension can be obtained by substituting pulse rate, body temperature, and blood pressure into a formula for calculating the "level of comprehension." Furthermore, the smaller the fluctuation in pulse rate, the more focused the subject is. Therefore, the analysis unit 130 can obtain a score indicating the level of concentration by substituting the variability of pulse rate or heart rate into a formula for calculating the "level of concentration." In this case, the measurement information includes time-series measurement data of pulse rate or heart rate. The analysis information may include such formulas. For example, the analytical information may include a formula for each emotion, such as happiness, anger, anxiety, calmness, etc.

[0056] The analysis unit 130 may also generate state information by combining multiple methods as described above. For example, the analysis unit 130 obtains a state score indicating the likelihood or degree of a certain state (such as "highly understanding," "happy," "angry," "anxious," or "calm") using each of the multiple methods. The analysis unit 130 then calculates the average, sum, or weighted sum of the obtained state scores to obtain a comprehensive state score for that state. A higher comprehensive state score indicates a higher likelihood that the subject is in that state. Note that each weight in the weighted sum may also be included in the analysis information. The analysis unit 130 may similarly obtain a comprehensive state score for each of the multiple states.

[0057] The condition information generated by the analysis unit 130 may be information indicating a specified condition, or may be a score (condition score or total condition score) indicating the likelihood of each of a plurality of conditions. Alternatively, if the score (condition score or total condition score) calculated for a certain condition is equal to or greater than a predetermined reference value, the analysis unit 130 may estimate that the subject is in that condition and include information indicating that condition in the condition information. This reference value may also be included in the analysis information.

[0058] <Second Example> In a second example, the analysis unit 130 generates status information using a model generated by machine learning. In this case, the analysis information is a model. That is, the analysis storage unit 140 stores multiple models, each associated with one attribute or a combination of two or more attributes. The analysis unit 130 then selects a model to be used from the multiple models based on the attribute information of the subject, and uses the selected model to generate status information.

[0059] A model corresponding to each attribute information can be prepared in advance by performing machine learning using training data that indicates the relationship between the brain waves and vital signs of a learner with that attribute and the learner's state at the time those brain waves and vital signs are measured.

[0060] The input of the model in this example is measurement information, and the output of the model is each likelihood of one or more states. The higher the likelihood, the more likely the subject is in that state. The analysis unit 130 obtains each likelihood of one or more states by inputting the measurement information into the model read from the analysis memory unit 140. The state information may be the likelihood for each of one or more states. Alternatively, if the likelihood calculated for a certain state is equal to or greater than a predetermined reference value, the analysis unit 130 may identify the subject as being in that state and include information indicating the state in the state information. This reference value may also be included in the analysis information.

[0061] The analysis unit 130 may also generate state information using images of the subject currently undergoing training. In this case, the model input further includes images of the subject currently undergoing training. Such a model corresponding to each attribute information can be prepared in advance by performing machine learning using images of a learner currently undergoing training who has that attribute as training data.

[0062] The acquisition unit 110 acquires an image of the subject under study from the imaging device 40. The analysis unit 130 inputs the image acquired by the acquisition unit 110 together with measurement information into a model, thereby obtaining the likelihood of each state.

[0063] The method by which the analysis unit 130 generates the state information is not limited to the above-described example, and various methods may be adopted.

[0064] The analysis unit 130 associates a personal ID with the status information so that it is possible to identify which subject the generated status information belongs to. Alternatively, the analysis unit 130 may associate information indicating the position of the subject with the status information. As described above, the information indicating the position can be generated based on an image obtained by the imaging device 40. The output unit 150 further outputs the personal ID or the information indicating the position associated with the status information. The terminal 30 acquires the personal ID or the information indicating the position associated with the status information. The terminal 30 can determine at least one of the display position and the display format of each piece of status information based on the personal ID or the information indicating the position.

[0065] In a situation where multiple subjects are studying simultaneously, the analysis unit 130 generates state information for each subject. The analysis unit 130 may then calculate an average of the state information for the multiple subjects. That is, the analysis unit 130 calculates an average value of the scores or likelihoods included in the state information for each state. The output unit 150 can then output the calculated average value. For example, by checking such average values ​​during a lesson, an instructor can grasp the overall situation (atmosphere, etc.) of the classroom.

[0066] The hardware configuration of the information processing device 10 will be described below. Each functional component of the information processing device 10 (the acquisition unit 110, the analysis unit 130, and the output unit 150) may be realized by hardware that realizes each functional component (e.g., a hardwired electronic circuit, etc.), or may be realized by a combination of hardware and software (e.g., a combination of an electronic circuit and a program that controls it, etc.). Below, a case where each functional component of the information processing device 10 is realized by a combination of hardware and software will be further described.

[0067] FIG. 6 is a diagram illustrating a computer 1000 for implementing the information processing device 10. The computer 1000 is any computer. For example, the computer 1000 may be a system on chip (SoC), a personal computer (PC), a server machine, a tablet terminal, or a smartphone. The computer 1000 may be a dedicated computer designed to implement the information processing device 10, or may be a general-purpose computer. The information processing device 10 may be implemented by a single computer 1000 or by a combination of multiple computers 1000.

[0068] The computer 1000 includes a bus 1020, a processor 1040, a memory 1060, a storage device 1080, an input / output interface 1100, and a network interface 1120. The bus 1020 is a data transmission path through which the processor 1040, the memory 1060, the storage device 1080, the input / output interface 1100, and the network interface 1120 transmit and receive data to and from each other. However, the method of interconnecting the processor 1040 and other components is not limited to bus connection. The processor 1040 may be any of various processors, such as a central processing unit (CPU), a graphics processing unit (GPU), or a field-programmable gate array (FPGA). The memory 1060 is a main storage device implemented using a random access memory (RAM) or the like. The storage device 1080 is an auxiliary storage device implemented using a hard disk, a solid state drive (SSD), a memory card, a read-only memory (ROM), or the like.

[0069] The input / output interface 1100 is an interface for connecting the computer 1000 to an input / output device. For example, an input device such as a keyboard and an output device such as a display are connected to the input / output interface 1100. The input / output interface 1100 may be connected to the input device or output device via a wireless connection or a wired connection.

[0070] The network interface 1120 is an interface for connecting the computer 1000 to a network. This communication network is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network). The network interface 1120 may be connected to the network via a wireless connection or a wired connection.

[0071] The computer 1000 according to this embodiment can be connected to the measurement device 20 via the input / output interface 1100 or the network interface 1120. The computer 1000 according to this embodiment can also be connected to the terminal 30 via the input / output interface 1100 or the network interface 1120.

[0072] The storage device 1080 stores program modules that realize the various functional components of the information processing device 10. The processor 1040 reads these program modules into the memory 1060 and executes them to realize the functions corresponding to the respective program modules.

[0073] Furthermore, when the subject memory unit 120, the analysis memory unit 140, and the status information memory unit 160 are each provided inside the information processing device 10, for example, the subject memory unit 120, the analysis memory unit 140, and the status information memory unit 160 are each realized using a storage device 1080.

[0074] FIG. 7 is a diagram showing an overview of an information processing method according to this embodiment. The information processing method according to this embodiment is executed by one or more computers. This information processing method includes an acquisition step S10, an analysis step S20, and an output step S30. In the acquisition step S10, measurement information indicating at least one of electroencephalograms and vital signs of the subject during learning, and attribute information of the subject, are acquired. In the analysis step S20, status information regarding the status of the subject is generated using the measurement information and attribute information. In the output step S30, the status information is output.

[0075] The information processing method according to this embodiment can be executed by the information processing device 10 .

[0076] In this embodiment, when an operation to start processing on the information processing device 10 is performed, steps S10 to S30 are repeatedly performed. Alternatively, the output of status information from the output unit 150 to the terminal 30 may be performed in response to a request operation. In this case, the acquisition step S10 and the analysis step S20 may be performed only when a request operation is performed, or may be performed at other times. It is preferable that the status information generated by the analysis unit 130 be stored in a storage device accessible from the output unit 150, regardless of whether the status information is output from the output unit 150 to the terminal 30. That is, the output unit 150 may output the status information to the status information storage unit 160. This allows the information to be checked after the fact. Furthermore, the measurement information acquired by the acquisition unit 110 may be further stored in the analysis storage unit 140.

[0077] When the status information includes multiple types of information, some types of information (for example, average values ​​for multiple subjects) may always be output from the output unit 150, and other types of information (for example, status information for a specific subject) may be output from the output unit 150 in response to a request operation. This prevents the information output by the terminal 30 from becoming cluttered, making it easier for the user to understand the content.

[0078] As described above, according to this embodiment, the analysis unit 130 generates condition information regarding the condition of the subject using the measurement information and attribute information. Therefore, more accurate condition estimation can be performed by taking the subject's attributes into account.

[0079] 8 is a diagram illustrating a functional configuration of a system 50 according to a second embodiment. The system 50 according to this embodiment is the same as the system 50 according to the first embodiment except for the points described below.

[0080] In the first embodiment, an example was described in which the status information is the subject's status at the measurement time when the measurement information was obtained. However, in this embodiment, an example is described in which the status information is related to the subject's status after the measurement time when the measurement information was obtained. However, the analysis unit 130 of the information processing device 10 according to this embodiment may generate both status information related to the subject's status at the measurement time when the measurement information was obtained and status information related to the subject's status after the measurement time when the measurement information was obtained. The status information related to the subject's status at the measurement time when the measurement information was obtained will hereinafter be referred to as first status information. The status information related to the subject's status after the measurement time when the measurement information was obtained will hereinafter be referred to as second status information.

[0081] The analysis unit 130 of the information processing device 10 according to this embodiment generates status information relating to the learning status of the subject after the time of measurement by further using learning information indicating at least the learning content at the time of measurement of the measurement information. The analysis unit 130 also generates the status information using accumulated information or a model stored in the accumulation storage unit 145. In the example of FIG. 8 , the accumulation storage unit 145 is provided inside the information processing device 10, but the accumulation storage unit 145 may also be provided outside the information processing device 10. When the accumulation storage unit 145 is provided inside the information processing device 10, the accumulation storage unit 145 is realized using, for example, a storage device 1080.

[0082] The learning information includes at least the learning content. Examples of the learning content include the subject, unit, and page number of the text, etc. The learning content is input to the terminal 30 at the start of learning. For example, the user inputs the subject, unit, etc. of the lesson into the terminal 30 at the start of the lesson. The acquisition unit 110 of the information processing device 10 can acquire the learning information from the terminal 30.

[0083] The learning status may include, for example, one or more of achievement level, understanding level, concentration level, and test results.

[0084] According to the information processing device 10 of this embodiment, the analysis unit 130 generates status information regarding the learning status of the subject after the time of measurement, making it possible to grasp the subject's status transition in advance and consider appropriate responses as necessary.

[0085] FIG. 9 is a diagram illustrating an example of an image displayed on the terminal 30 according to this embodiment. This image allows an overview of the information for the entire class. A teacher or other user of the terminal 30 can check this display content after a lesson to understand the current state of the class and improve subsequent instruction. The "TOPICS" column in this image displays messages indicating the status of multiple members (subjects) of the class. Messages indicating the status of each subject are generated based on status information. By checking these messages, instructors can identify students who require their attention. Note that messages may be displayed only when the status information meets certain conditions.

[0086] 9, the "class atmosphere" is displayed based on the average emotions of all class members. This average emotion may be, for example, the average of status information during the most recent lesson or the average of status information during the most recent specified period (e.g., one week).

[0087] 9, the status of multiple class members is displayed in a list. Information about each member includes a photo of the member's face, a symbol indicating their emotion, their name, and attributes. The status of each member is, for example, the average of their status information during the most recent class, or the average of their status information over the most recent specified period (e.g., one week).

[0088] Fig. 10 is a diagram showing another example of an image displayed on the terminal 30 according to this embodiment. Fig. 10 is an image displaying information about an individual target person. For example, by selecting any member in the member list of Fig. 9, the image of Fig. 10 can be displayed.

[0089] In the example of Figure 10, a photograph of a subject's face, name, class, and attributes are displayed. Advice based on predicted future status is also displayed. Additionally, a line graph showing time-series data on interest in classes (an example of emotion) is displayed in the lower left portion of Figure 10. This graph can display not only the status of the subject displayed in this image ("Individual"), but also the overall average ("Overall") based on the status information of multiple subjects, the average of subjects with each attribute ("ADHD," "Autism Spectrum Disorder," "Specific Learning Disorder," "Depression"), and the average of subjects with the same combination of attributes as the subject displayed ("Same Disorder"). Therefore, the user can check the progress of the average status of learners with similar characteristics. In the example of Figure 10, "Individual," "Overall," "Autism Spectrum Disorder," and "Same Disorder" are selected, and their data are displayed. Note that this graph may include past and future information.

[0090] This graph can also be displayed by switching between subjects. In this way, the terminal 30 can output status information in a comparable state between multiple subjects or multiple units. Furthermore, the type of status to be displayed may be changed, for example, from "interest in class" to "level of understanding," etc.

[0091] 10 also displays information for identifying scenes in which emotions changed. Specifically, a list of dates and times when emotions changed is displayed. A time when emotions changed is, for example, a time when the likelihood or score of any emotion indicated in the state information generated by the analysis unit 130 changed by a predetermined rate or more. The subject's state at a selected date and time from the listed dates and times is then displayed in a radar chart.

[0092] The information processing device 10 according to this embodiment can compare the information of the subject with the accumulated data of children or students with the same or similar characteristics, and output future predicted comments, information on subjects and units that the subject is likely to have difficulty with, predicted comprehension levels, etc. This allows instructors to detect signs that the subject may need support.

[0093] As another example, the terminal 30 may further output a three-dimensional model image that reproduces the situation of the subject during learning, including the surrounding environment. For example, by reproducing and confirming the situation before and after the time when the above-mentioned emotional change occurred, it becomes easier to identify the cause. The image for generating the three-dimensional model image is captured by, for example, the imaging device 40.

[0094] In the example of FIG. 8 , the information processing device 10 includes a measurement storage unit 100. When the measurement storage unit 100 is provided inside the information processing device 10, the measurement storage unit 100 is realized using, for example, a storage device 1080. In this embodiment, the acquisition unit 110 can associate the measurement information acquired from the measurement device 20 with a personal ID and store it in the measurement storage unit 100. In this embodiment, the analysis unit 130 does not need to generate status information in real time. The analysis unit 130 can generate status information by processing the measurement information held in the measurement storage unit 100 after the fact. Furthermore, the analysis unit 130 may generate status information using time-series data of the measurement information.

[0095] In this embodiment, the output unit 150 stores the status information generated by the analysis unit 130 in the status information storage unit 160. For example, in response to a request from the terminal 30, the output unit 150 reads the status information from the status information storage unit 160 and outputs the status information to the terminal 30. Therefore, the user of the terminal 30 can check the status information at a desired timing after the fact.

[0096] Examples of a method in which the analysis unit 130 according to this embodiment generates state information relating to the learning state of the subject after the time point at which the measurement information is measured will be described below as third and fourth examples.

[0097] <Third Example> In the third example, the accumulation memory unit 145 holds, for example, accumulated information of multiple learners in the past. Each piece of accumulated information is time-series data of at least one of measurement information and first status information. This first status information is, for example, information generated in advance based on the measurement information using the method described in the first embodiment. Each piece of accumulated information is associated with one attribute or a combination of two or more attributes. Furthermore, in each piece of accumulated information, a learning log is associated with data at each point in time in the time-series data. Each learning log includes at least the learning content. Each learning log also includes the learning status.

[0098] The analysis unit 130 predicts the subject's subsequent condition by comparing at least one of the measurement information and the first condition information of the subject with the accumulated information held in the accumulation memory unit 145.

[0099] 11 is a flowchart illustrating the flow of processing executed by the analysis unit 130 according to this example. In step S110, the analysis unit 130 extracts one or more pieces of accumulated information associated with one or more attributes corresponding to the attribute information of the subject from among the plurality of pieces of accumulated information held in the accumulation storage unit 145. That is, the analysis unit 130 extracts one or more pieces of accumulated information such that one or more attributes indicated in the attribute information of the subject match one or more attributes associated with the extracted accumulated information.

[0100] In step S120, the analysis unit 130 identifies, from the time-series measurement information of each piece of extracted accumulated information, measurement information associated with the same learning content as the learning content indicated in the learning information acquired by the acquisition unit 110. This makes it possible to compare information relating to the same learning content.

[0101] In step S130, the analysis unit 130 compares the measurement information identified in each of the extracted one or more pieces of accumulated information with the measurement information of the subject acquired by the acquisition unit 110. As a result, the analysis unit 130 identifies, among the one or more pieces of accumulated information, the accumulated information in which the identified measurement information is most similar to the measurement information of the subject as similar accumulated information. Note that the comparison between the measurement information of the accumulated information and the measurement information of the subject may be performed using any one of the indices included in the measurement information, or may be performed using multiple indices (e.g., brain waves and pulse rate). In the latter case, the analysis unit 130 calculates the difference between the measurement information of the subject and the measurement information of the accumulated information for each indices, and can identify the accumulated information with the smallest sum or average of the obtained multiple differences as similar accumulated information.

[0102] Furthermore, the comparison of the measurement information of the stored information with the measurement information of the subject may be a comparison of their respective time-series data. In this case, the similarity of the time-series data is calculated using an existing method, and the stored information with the highest similarity is determined to be similar stored information.

[0103] It is estimated that the learning status of the subject regarding the content to be subsequently studied will be similar to the learning status indicated in the learning log of the identified similar accumulated information. Therefore, in step S140, the analysis unit 130 generates second status information of the subject based on the learning log indicated in the identified accumulated information. That is, the analysis unit 130 regards the learning status indicated in the learning log included in the identified similar accumulated information as the second status information. Information indicating the learning content is associated with the second status information. Furthermore, the analysis unit 130 associates the second status information with the subject's personal ID.

[0104] According to the second status information, for example, it is possible to predict what learning content the subject will have difficulty with in the future.

[0105] 12 is a flowchart showing another example of the flow of processing executed by the analysis unit 130. In this example, similar stored information is identified by comparing the first status information of the stored information with the first status information of the subject.

[0106] Step S210 is the same as step S110 in Fig. 11. Next, in step S220, the analysis unit 130 generates first status information based on the measurement information of the subject acquired by the acquisition unit 110. The first status information can be generated in the same manner as described in the first embodiment.

[0107] In step S230, the analysis unit 130 identifies, from the first status information of the time series of each accumulated information extracted in step S210, the first status information associated with the same learning content as the learning content indicated in the learning information acquired by the acquisition unit 110.

[0108] In step S240, the analysis unit 130 compares the first status information identified in each of the extracted one or more pieces of accumulated information with the first status information of the subject generated in step S220. As a result, the analysis unit 130 identifies, among the one or more pieces of accumulated information, the accumulated information in which the identified first status information is most similar to the first status information of the subject as similar accumulated information. Note that the comparison between the first status information of the accumulated information and the first status information of the subject may be performed using any one index included in the first status information, or may be performed using multiple indexes (e.g., calmness level and comprehension level). Specific examples of the latter case are similar to those described above for the measurement information. Furthermore, the comparison between the first status information of the accumulated information and the first status information of the subject may be performed by comparing their respective time-series data. In this case, the similarity of the time-series data is calculated using an existing method, and the accumulated information with the highest similarity is determined to be similar accumulated information.

[0109] Step S250 is the same as step S140 in FIG.

[0110] The analysis unit 130 may identify similar stored information by combining the measurement information and the first status information.

[0111] In the third example, the measurement information acquired by the acquisition unit 110 and the state information generated by the analysis unit 130 may be held in the accumulation storage unit 145 as at least a part of the accumulated information.

[0112] <Fourth Example> In a fourth example, the accumulation storage unit 145 stores multiple models generated by machine learning. Each of the multiple models is associated with one attribute or a combination of two or more attributes. The analysis unit 130 selects a model to be used from the multiple models based on the attribute information of the subject, in the same manner as described in the first embodiment, and reads out the selected model for use in generating status information.

[0113] The inputs of the model according to this example are the subject's measurement information and learning information. However, the measurement information input to the model may be time-series data of the measurement information. The output of the model according to this example is information indicating the learning state of each learning content. Such a model can be generated, for example, by machine learning using the multiple pieces of accumulated information described in the third example as training data.

[0114] The analysis unit 130 inputs the measurement information and learning information acquired by the acquisition unit 110 into the model read from the accumulation storage unit 145. Then, as an output of the model, it acquires information indicating the learning state for each learning content. The analysis unit 130 then sets the acquired information indicating the learning state as second status information. The analysis unit 130 associates the learning content and the subject's personal ID with the second status information.

[0115] According to the third and fourth examples described above, second status information is generated for multiple learning contents. That is, the analysis unit 130 generates status information for each subject or unit. The analysis unit 130 may further extract second status information associated with learning contents that the subject has not yet studied from the multiple pieces of generated second status information. For example, information indicating learning contents that the subject has studied is stored in a storage device accessible to the analysis unit 130, and the analysis unit 130 can read and use this information.

[0116] Furthermore, the analysis unit 130 may include advice information regarding guidance to the subject in the status information. The analysis unit 130 can select advice information corresponding to the predicted learning status from a plurality of pieces of advice information prepared in advance and include the selected advice information in the status information.

[0117] The method by which the analysis unit 130 generates the state information is not limited to the above-described example, and various methods may be adopted.

[0118] Next, the operation and effect of this embodiment will be described. In this embodiment, the same operation and effect as in the first embodiment can be obtained. In addition, the analysis unit 130 of the information processing device 10 according to this embodiment further uses learning information indicating at least the learning content at the time of measurement of the measurement information to generate status information regarding the learning status of the subject after the time of measurement. Therefore, it is possible to grasp the subject's status transition in advance and consider preventive measures as necessary.

[0119] (Modification) The system 50 according to this embodiment is the same as the system 50 according to the first or second embodiment except for the points described below.

[0120] In this modification, the system 50 includes, as the measuring device 20, an imaging device such as a thermographic camera that captures an image of the subject from a position remote from the subject to measure their vital signs. If the measuring device 20 is an imaging device, the measuring device 20 outputs an image. If the measuring device 20 is a thermographic camera, the measuring device 20 outputs an image showing the temperature at each position within the imaging range. The imaging area of ​​the measuring device 20 may be fixed or variable.

[0121] When the measurement device 20 is an imaging device, the acquisition unit 110 of the information processing device 10 obtains measurement information by processing an image of the subject under study. That is, the measurement device 20 captures an image of the subject under study and generates an image. The measurement device 20 then outputs the generated image. The acquisition unit 110 acquires the image output from the measurement device 20.

[0122] Here, when the measurement device 20 is set up to mainly capture an image of a specific subject, the measurement device 20 is associated with the subject that is the measurement target of the measurement device 20. Then, similar to the example of the contact-type measurement device 20 described above, a personal ID is associated with the measurement information.

[0123] On the other hand, when the measurement device 20 captures images of multiple subjects, the acquisition unit 110 can associate the measurement information of each subject with a personal ID by performing the following process, for example.

[0124] In the subject information stored in the subject storage unit 120 according to this modification, position information indicating a position within an image is associated in advance with each of a plurality of personal IDs. The position information can be prepared, for example, based on the seating positions assigned to each subject in a classroom. The acquisition unit 110 uses the position information to identify a position corresponding to each personal ID. The acquisition unit 110 then extracts position information corresponding to each personal ID from the image acquired by the measuring device 20. For example, if the image acquired by the measuring device 20 is a thermographic image, the acquisition unit 110 uses the position information to identify coordinates within the image corresponding to a particular personal ID. The acquisition unit 110 then associates the temperature at those coordinates in the thermographic image with that personal ID. Note that each personal ID may be associated with information indicating an area within the image. In this case, the acquisition unit 110 may calculate the average temperature within the area in the thermographic image and associate it with the personal ID.

[0125] Alternatively, the position of each subject may be detected using an image acquired by the imaging device 40. In this case, in the subject information stored in the subject storage unit 120, each personal ID is further associated with a feature for facial recognition processing. When the acquisition unit 110 of the information processing device 10 acquires the feature of the subject from the subject storage unit 120, it uses the feature to perform facial recognition processing on the image generated by the imaging device 40. This identifies the position of the subject in the image. Furthermore, the position of the subject in real space can be identified using the position of the subject in the image, the position of the imaging device 40, and the relationship between the imaging device 40 and the imaging area of ​​the imaging device 40. Furthermore, the acquisition unit 110 can identify the position of the subject in the image captured by the measuring device 20 using the position of the measuring device 20 and the relationship between the measuring device 20 and the imaging area of ​​the measuring device 20.

[0126] In this embodiment, the same actions and effects as those of the first or second embodiment can be obtained.

[0127] Although the embodiments of the present invention have been described above with reference to the drawings, these are merely examples of the present invention, and various other configurations can also be adopted.

[0128] In addition, although the flowcharts used in the above description show multiple steps (processes) in a sequential order, the order of steps executed in each embodiment is not limited to the order shown. In each embodiment, the order of the steps shown in the drawings can be changed as long as it does not cause any problems in terms of content. Furthermore, the above-described embodiments can be combined as long as the content is not contradictory.

[0129] Some or all of the above embodiments can be described as in the following supplementary notes, but are not limited to them. 1-1. An information processing device comprising: acquisition means for acquiring measurement information indicating at least one of electroencephalograms and vital signs of a subject while the subject is learning, and attribute information of the subject; analysis means for generating status information regarding the status of the subject using the measurement information and the attribute information; and output means for outputting the status information. 1-2. An information processing device according to 1-1, wherein the status information relates to at least one of the status of the subject at the measurement time when the measurement information was obtained, and the status of the subject after the measurement time. 1-3. An information processing device according to 1-1 or 1-2, wherein the status information indicates at least one of the subject's emotion, level of concentration, and level of understanding. 1-4. An information processing device according to any one of 1-1 to 1-3, wherein the output means outputs the status information in real time to a terminal used by an instructor who is instructing the subject at the measurement time. 1-5. 1-1. An information processing device according to any one of 1-1. to 1-4., wherein the output means further outputs notification information to the terminal when the status information satisfies a predetermined condition. 1-6. An information processing device according to 1-2., wherein the analysis means generates the status information regarding the learning status of the subject after the time of measurement, further using learning information indicating at least the learning content at the time of measurement. 1-7. An information processing device according to 1-6., wherein the status information includes advice information regarding instruction to the subject. 1-8. An information processing device according to 1-6. or 1-7., wherein the analysis means generates the status information for each subject or unit. 1-9. An information processing device according to 1-8., wherein the output means outputs the status information to a terminal, and the terminal outputs the status information in a state that allows comparison between multiple subjects or multiple units. 1-10. An information processing device according to 1-1. to 1-9. 2. The information processing device according to claim 1, wherein the attribute information includes an attribute related to a factor that requires support.1-11. The information processing device described in 1-10., wherein the attribute information includes at least one of information about the subject's disability and information about the subject's language. 1-12. The information processing device described in any one of 1-1. to 1-11., wherein the analysis means generates the status information using a model generated by machine learning. 1-13. The information processing device described in 1-12., wherein the attribute information indicates one or more attributes, and the analysis storage means holds a plurality of models, each associated with one attribute or a combination of two or more attributes, and the analysis means selects the model to be used from the plurality of models based on the attribute information of the subject, and uses the selected model to generate the status information. 1-14. The information processing device described in any one of 1-1. to 1-13., wherein the acquisition means obtains the measurement information by processing an image of the subject during training. 1-15. The information processing device described in 1-1. to 1-14. 2-1. An information processing device according to any one of items 1-1. to 1-15., wherein the analysis means generates the status information by further using an image of the subject during learning. 2-1. An information processing device according to any one of items 1-1. to 1-15., a measurement device that measures at least one of the subject's brain waves and vital signs, and a terminal from which the output means outputs the status information. 3-1. An information processing method in which one or more computers acquire measurement information indicating at least one of the subject's brain waves and vital signs during learning and attribute information of the subject, generate status information regarding the subject's status using the measurement information and the attribute information, and output the status information. 3-2. An information processing method according to item 3-1., wherein the status information relates to at least one of the subject's status at the measurement time when the measurement information was obtained and the subject's status after the measurement time. 3-3. An information processing method according to item 3-1. or 3-2. The information processing method according to claim 1, wherein the state information indicates at least one of an emotion, a concentration level, and a comprehension level of the subject.3-4. An information processing method according to any one of 3-1. to 3-3., wherein the output means outputs the status information in real time to a terminal used by an instructor who is instructing the subject at the time of measurement. 3-5. An information processing method according to any one of 3-1. to 3-4., wherein the one or more computers further output notification information to the terminal if the status information satisfies a predetermined condition. 3-6. An information processing method according to 3-2., wherein the one or more computers further use learning information indicating at least learning content at the time of measurement to generate the status information related to the learning status of the subject after the time of measurement. 3-7. An information processing method according to 3-6., wherein the status information includes advice information related to instruction to the subject. 3-8. An information processing method according to 3-6. or 3-7. 3-10. The information processing method described in any one of 3-1. to 3-9., wherein the attribute information includes attributes related to factors that require support. 3-11. The information processing method described in 3-10., wherein the attribute information includes at least one of information related to the disability of the subject and information related to the language of the subject. 3-12. The information processing method described in any one of 3-1. to 3-11., wherein the one or more computers generate the status information using a model generated by machine learning.3-13. An information processing method according to 3-12, wherein the attribute information indicates one or more attributes, wherein the analysis storage means holds a plurality of models, each associated with one attribute or a combination of two or more attributes, and wherein the one or more computers select the model to be used from the plurality of models based on the attribute information of the subject, and use the selected model to generate the status information. 3-14. An information processing method according to any one of 3-1 to 3-13, wherein the one or more computers obtain the measurement information by processing an image of the subject during training. 3-15. An information processing method according to any one of 3-1 to 3-14, wherein the one or more computers generate the status information by further using an image of the subject during training. 4-1. A program that causes a computer to function as: an acquisition means that acquires measurement information indicating at least one of electroencephalograms and vital signs of a subject while the subject is learning, and attribute information of the subject; an analysis means that generates status information regarding the status of the subject using the measurement information and the attribute information; and an output means that outputs the status information. 4-2. A program in accordance with 4-1., wherein the status information relates to at least one of the subject's status at the measurement time when the measurement information was obtained, and the subject's status after the measurement time. 4-3. A program in accordance with 4-1. or 4-2., wherein the status information indicates at least one of the subject's emotion, level of concentration, and level of understanding. 4-4. A program in accordance with any one of 4-1. to 4-3., wherein the output means outputs the status information in real time to a terminal used by an instructor who is instructing the subject at the measurement time. 4-5. A program in accordance with 4-1. to 4-4. wherein the output means further outputs notification information to the terminal when the status information satisfies a predetermined condition.4-6. A program described in 4-2., wherein the analysis means generates the status information regarding the learning status of the subject after the time of measurement, further using learning information indicating at least the learning content at the time of measurement. 4-7. A program described in 4-6., wherein the status information includes advice information regarding instruction to the subject. 4-8. A program described in 4-6. or 4-7., wherein the analysis means generates the status information for each subject or unit. 4-9. A program described in 4-8., wherein the output means outputs the status information to a terminal, and the terminal outputs the status information in a comparable state between multiple subjects or multiple units. 4-10. A program described in any one of 4-1. to 4-9., wherein the attribute information includes attributes related to factors that require support. 4-11. A program described in 4-10., wherein the attribute information includes at least one of information regarding the subject's disability and information regarding the subject's language. 4-12. A program according to any one of 4-1. to 4-11., wherein the analysis means generates the status information using a model generated by machine learning. 4-13. A program according to 4-12., wherein the attribute information indicates one or more attributes, and the analysis storage means holds a plurality of models, each associated with one attribute or a combination of two or more attributes, and the analysis means selects the model to be used from the plurality of models based on the attribute information of the subject, and uses the selected model to generate the status information. 4-14. A program according to any one of 4-1. to 4-13., wherein the acquisition means obtains the measurement information by processing an image of the subject during training. 4-15. A program according to any one of 4-1. to 4-14., wherein the analysis means generates the status information by further using an image of the subject during training.5-1. A computer-readable recording medium having a program recorded thereon, wherein the program causes a computer to function as: an acquisition means for acquiring measurement information indicating at least one of an electroencephalogram and a vital sign of a subject while the subject is learning, and attribute information of the subject; an analysis means for generating status information regarding the status of the subject using the measurement information and the attribute information; and an output means for outputting the status information. 5-2. A recording medium described in 5-1, wherein the status information relates to at least one of the status of the subject at the time of measurement when the measurement information was obtained, and the status of the subject after the time of measurement. 5-3. A recording medium described in 5-1 or 5-2, wherein the status information indicates at least one of the subject's emotion, level of concentration, and level of understanding. 5-4. A recording medium described in any one of 5-1 to 5-3, wherein the output means outputs the status information in real time to a terminal used by an instructor who is instructing the subject at the time of measurement. 5-5. A recording medium described in 5-1 to 5-4. 5-6. A recording medium described in any one of 5-1., wherein the output means further outputs notification information to the terminal when the status information satisfies a predetermined condition. 5-6. A recording medium described in 5-2., wherein the analysis means generates the status information regarding the learning status of the subject after the time of measurement by further using learning information indicating at least the learning content at the time of measurement. 5-7. A recording medium described in 5-6., wherein the status information includes advice information regarding instruction to the subject. 5-8. A recording medium described in 5-6. or 5-7., wherein the analysis means generates the status information for each subject or unit. 5-9. A recording medium described in 5-8., wherein the output means outputs the status information to a terminal, and the terminal outputs the status information in a state that allows comparison between multiple subjects or multiple units. 5-10. A recording medium described in any one of 5-1. to 5-9., wherein the attribute information includes attributes related to factors that require support.5-11. A recording medium according to 5-10., wherein the attribute information includes at least one of information about the subject's disability and information about the subject's language. 5-12. A recording medium according to any one of 5-1. to 5-11., wherein the analysis means generates the status information using a model generated by machine learning. 5-13. A recording medium according to 5-12., wherein the attribute information indicates one or more attributes, the analysis storage means holds a plurality of models, each associated with one attribute or a combination of two or more attributes, and the analysis means selects the model to be used from the plurality of models based on the attribute information of the subject, and uses the selected model to generate the status information. 5-14. A recording medium according to any one of 5-1. to 5-13., wherein the acquisition means obtains the measurement information by processing an image of the subject during training. 5-15. A recording medium according to 5-1. to 5-14. The recording medium according to any one of the preceding claims, wherein the analysis means generates the state information by further using an image of the subject during training.

[0130] This application claims priority based on Japanese Patent Application No. 2022-150214, filed September 21, 2022, the disclosure of which is incorporated herein by reference in its entirety.

[0131] REFERENCE SIGNS LIST 10 Information processing device 20 Measurement device 30 Terminal 40 Imaging device 50 System 100 Measurement storage unit 110 Acquisition unit 120 Subject storage unit 130 Analysis unit 140 Analysis storage unit 145 Accumulation storage unit 150 Output unit 160 Status information storage unit 1000 Computer 1020 Bus 1040 Processor 1060 Memory 1080 Storage device 1100 Input / output interface 1120 Network interface

Claims

1. An acquisition means for acquiring measurement information indicating at least one of an electroencephalogram and a vital sign of a subject during learning, and attribute information of the subject; an analysis means for generating status information regarding a status of the subject using the measurement information and the attribute information; and an output means for outputting the status information. Information processing device.

2. 2. The information processing device according to claim 1, The output means outputs the state information in real time to a terminal used by an instructor who is instructing the subject at the time when the measurement information is obtained. Information processing device.

3. 3. The information processing device according to claim 2, The output means further outputs notification information to the terminal when the state information satisfies a predetermined condition. Information processing device.

4. 4. The information processing device according to claim 1, The attribute information includes attributes related to factors for which support is needed. Information processing device.

5. 5. The information processing device according to claim 4, The attribute information includes at least one of information regarding a disability of the subject and information regarding the language of the subject. Information processing device.

6. 4. The information processing device according to claim 1, The acquiring means acquires the measurement information by processing an image of the subject during training. Information processing device.

7. 4. The information processing device according to claim 1, The analysis means further uses images of the subject during training to generate the status information. Information processing device.

8. An information processing device according to any one of claims 1 to 3; A measuring device for measuring at least one of the subject's brain waves and vital signs; The output means includes a terminal for outputting the status information. system.

9. One or more computers Acquire measurement information indicating at least one of an electroencephalogram and a vital sign of a subject during learning, and attribute information of the subject; generating status information regarding a status of the subject using the measurement information and the attribute information; Output the status information Information processing methods.

10. Computer, An acquisition means for acquiring measurement information indicating at least one of an electroencephalogram and a vital sign of a subject during learning, and attribute information of the subject; an analysis means for generating condition information regarding a condition of the subject using the measurement information and the attribute information; and The state information is outputted. program.