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

Abstract

To determine a degree of mental health of an object person in real time using pulse data on the object person, and provide it.SOLUTION: An information processing device 10 stores pulse data for a predetermined time or more (e.g., 200 minutes or more) acquired in an accumulation mode as accumulation data, and calculates reference zone data indicating a reference zone for each state of a degree of mental health using the accumulation data (S1). If a student A wears a measuring terminal 30, the measuring terminal 30 notifies pulse data on the student A to the information processing device 10 (S2). The information processing device 10 determines the degree of mental health using the received pulse data and the reference zone data (S3). The information processing device 10 notifies a determination result to a management terminal 20 (S4), and the management terminal 20 displays the received determination result (S5). For example, the degree of mental health of the student A is displayed as "waking state."SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an information processing system, an information processing apparatus, an information processing method, and an information processing program that can determine the mental health level of a subject in real time using the subject's pulse data.

Background Art

[0002] In recent years, while respecting the individuality of individuals, the realization of individually optimized learning according to the individual situation has been expected. In particular, it is required to grasp the concentration and mental state of students who are learning, and then conduct learning guidance according to that state.

[0003] For this reason, a technique has been disclosed in which concentration rule setting information associating the activity state and the concentration state in a plurality of nervous systems is set, and then measured biological information and concentration are estimated to obtain concentration estimation information (see, for example, Patent Document 1).

[0004] Also, a technique has been disclosed in which an image of the subject's expression is acquired, and a threshold is set from expression indexes (smile degree, smile formation speed, positions of the mouth and eyes, facial left-right symmetry, face angle) to estimate the presence or absence of a stress state (see, for example, Patent Document 2).

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0006] However, the invention described in Patent Document 1 merely identifies states of tension or relaxation. Furthermore, the invention described in Patent Document 2 cannot classify diverse mental states in detail and perform quantitative evaluation, making it difficult to classify the mental state of students learning in real time.

[0007] The present invention has been made to solve the problems of the prior art described above, and aims to provide an information processing system, information processing device, information processing method, and information processing program that can determine the mental health level of a subject in real time using the subject's pulse data. [Means for solving the problem]

[0008] To solve the above problems, the present invention provides an information processing system comprising an information processing device for determining the mental health status of a person being measured, and a measurement terminal connected to the information processing device in a communicative manner, wherein the measurement terminal has pulse information acquisition means for acquiring pulse information of the person being measured, and pulse information notification means for notifying the pulse information acquired by the pulse information acquisition means, and the information processing device has storage means for storing pulse information received from the measurement terminal, and pulse information stored in the storage means Using a predetermined number of pulse rate data, Feature Value Information The RRI difference value and pulse rate variability coefficient are the RRI difference value and pulse rate variability coefficient. A means for calculating the results, Using the aforementioned feature value information, the mean and standard deviation of the RRI difference value and the pulse rate variability coefficient are calculated, and using the mean and standard deviation, each of the mental health statuses is determined. A generation means for generating reference range information, If the pulse information corresponds to any of the mental health statuses included in the reference range information, the corresponding state is The aforementioned measurement subjects The aforementioned mental health level and The system is characterized by comprising a determination means for making a determination, and a notification means for notifying the mental health level determined by the determination means.

[0009] Furthermore, the present invention is characterized in that, in the above invention, the notification means notifies a predetermined management device of the mental health level and causes the management device to display the mental health level on its display unit.

[0013] Furthermore, the present invention is characterized in that, in the above invention, the mental health level includes at least a state of wakefulness, a flow state, a relaxed state, a state of decreased motivation, a drowsy / sleeping state, and a high-stress state.

[0014] Furthermore, the present invention relates to an information processing device that is communicably connected to a measurement terminal that acquires pulse information of a person being measured, comprising a storage means for storing pulse information received from the measurement terminal, and pulse information stored in the storage means Using a predetermined number of pulse rate data, Feature Value Information The RRI difference value and pulse rate variability coefficient are the RRI difference value and pulse rate variability coefficient. A means for calculating the results, Using the aforementioned feature value information, the mean and standard deviation of the RRI difference value and the pulse rate variability coefficient are calculated, and using the mean and standard deviation, each state of mental health is determined. A generation means for generating reference range information, If the pulse information corresponds to any of the mental health statuses included in the reference range information, the corresponding state is The aforementioned measurement subjects The aforementioned mental health level and The system is characterized by comprising a determination means for making a determination, and a notification means for notifying the mental health level determined by the determination means.

[0015] Furthermore, the present invention relates to an information processing method in an information processing system comprising an information processing device for determining the mental health status of a person being measured, and a measurement terminal that is communicably connected to the information processing device, wherein the measurement terminal performs a pulse information acquisition step of acquiring pulse information of the person being measured, a pulse information notification step of notifying the pulse information acquired in the pulse information acquisition step, and the information processing device performs a storage step of storing the pulse information received from the measurement terminal in a predetermined storage unit, and the pulse information stored in the storage unit Using a predetermined number of pulse rate data, Feature Value Information The RRI difference value and pulse rate variability coefficient are the RRI difference value and pulse rate variability coefficient. A calculation process for calculating the above, and the information processing device Using the aforementioned feature value information, the mean and standard deviation of the RRI difference value and the pulse rate variability coefficient are calculated, and using the mean and standard deviation, each of the mental health statuses is determined. A generation process for generating reference range information, and the information processing device, If the pulse information corresponds to any of the mental health states included in the reference range information, the corresponding state is The aforementioned measurement subjects The aforementioned mental health level and The present invention is characterized by including a determination step of making a determination, and a notification step in which the information processing device notifies the mental health level determined by the determination step.

[0016] Furthermore, the present invention is an information processing program executed on an information processing device that is communicably connected to a measurement terminal that acquires pulse rate information of a person being measured and a management terminal that displays the mental health status, comprising a storage procedure for storing pulse rate information received from the measurement terminal in a predetermined storage unit, and pulse rate information stored in the storage unit Using a predetermined number of pulse rate data, Feature Value Information The RRI difference value and pulse rate variability coefficient are the RRI difference value and pulse rate variability coefficient. The calculation procedure for determining the result, Using the aforementioned feature value information, the mean and standard deviation of the RRI difference value and the pulse rate variability coefficient are calculated, and using the mean and standard deviation, each of the mental health statuses is determined.A generation procedure for generating reference area information, If the pulse information corresponds to any of the mental health states included in the reference range information, the corresponding state is of the person to be measured The aforementioned mental health and a determination procedure for determining, and a notification procedure for notifying the mental health determined by the determination procedure, and causing a computer to execute them.

Advantages of the Invention

[0017] According to the present invention, using the collected pulse data of the subject, the mental health of the subject can be determined in real time.

Brief Description of the Drawings

[0018] [Figure 1] FIG. 1 is an explanatory diagram for explaining the outline of an information processing system according to an embodiment. [Figure 2] FIG. 2 is a diagram showing the system configuration of an information processing system according to an embodiment. [Figure 3] FIG. 3 is a functional block diagram showing the configuration of the information processing apparatus shown in FIG. 2. [Figure 4] FIG. 4 is a diagram showing an example of student data, accumulated data, and feature value data shown in FIG. 3. [Figure 5] FIG. 5 is a diagram showing an example of reference area data shown in FIG. 3. [Figure 6] FIG. 6 is a diagram showing an example of pulse data and determination data shown in FIG. 3. [Figure 7] FIG. 7 is an explanatory diagram for explaining the Yerkes-Dodson law. [Figure 8] FIG. 8 is an explanatory diagram when transitioning from a high-stress state or a state of decreased motivation to a flow state. [Figure 9] FIG. 9 is an explanatory diagram for explaining RRI. [Figure 10] FIG. 10 is a diagram showing the relationship between feature values and mental health according to an embodiment. [Figure 11] FIG. 11 is a diagram showing a mental health matrix according to an embodiment. [Figure 12]Figure 12 is a diagram (part 1) showing an example of the analysis of mental health according to the embodiment. [Figure 13] Figure 13 is a diagram (part 2) showing an example of the analysis of mental health according to this embodiment. [Figure 14] Figure 14 is a diagram (part 3) showing an example of the analysis of mental health according to this embodiment. [Figure 15] Figure 15 is a flowchart showing the processing procedure for the reference range output processing in the information processing device according to the embodiment. [Figure 16] Figure 16 is a flowchart showing the processing procedure for determining mental health status in the information processing device according to the embodiment. [Modes for carrying out the invention]

[0019] The information processing system, information processing device, information processing method, and information processing program according to this embodiment will be described in detail below. Note that this embodiment will focus on cases where the target audience is students engaged in learning.

[0020] <Overview of the Information Processing System According to the Embodiment> First, an overview of the information processing system according to this embodiment will be described. Figure 1 is an explanatory diagram illustrating the overview of the information processing system according to this embodiment.

[0021] As shown in Figure 1, student A wears the measurement terminal 30, for example, on their wrist. The measurement terminal 30 measures student A's pulse and notifies the information processing device 10 of the pulse data at predetermined intervals (for example, every 4 seconds). This notification is made wirelessly. This pulse data is specifically shown as the RRI (RR Interval) value. The wave during a heartbeat in an electrocardiogram is called the R wave, and the interval between R waves (heartbeat interval) is the RRI.

[0022] The information processing device 10 stores pulse rate data acquired in storage mode for a predetermined period of time or longer (for example, 200 minutes or more) as stored data, and uses this stored data to calculate reference range data indicating the reference range for each mental health state (S1).

[0023] The "accumulation mode" is a mode of the information processing device 10 for acquiring pulse rate data in advance in order to calculate reference range data. In addition to this "accumulation mode," there is a "judgment mode" that uses the most recent pulse rate data acquired from the measurement terminal 30 to determine the mental health status of students. Mental health status is classified into "awake state," "flow state," "relaxed state," "decreased motivation state," "drowsy / sleeping state," and "high stress state."

[0024] If student A wears the measurement terminal 30, the measurement terminal 30 notifies the information processing device 10 of student A's pulse rate data (S2). The information processing device 10 uses the received pulse rate data and reference range data to determine the student's mental health status (S3).

[0025] The information processing device 10 notifies the management terminal 20 of the judgment result (S4), and the management terminal 20 displays the received judgment result (S5). For example, it displays "Aroused" as the mental health status of student A.

[0026] In this embodiment, the information processing system uses the subject's pulse data to determine the subject's mental health status in real time and displays the determination result.

[0027] <System Configuration> Next, the system configuration of the information processing system according to this embodiment will be described. Figure 2 is a diagram showing the system configuration of the information processing system according to this embodiment. As shown in Figure 2, the information processing device 10 is connected to the management terminal 20 and the wireless router 40 via a communication line within the facility. The measurement terminal 30 is connected to the wireless router 40 via wireless communication and is connected to the information processing device 10 via the wireless router 40.

[0028] The information processing device 10 is a device that determines the mental health status of a subject using pulse rate data acquired by the measurement terminal 30. When the information processing device 10 receives data relating to a student, it stores this data in the student data. When the information processing device 10 receives a request to specify either a storage mode or a judgment mode, it sets itself to the requested mode.

[0029] Furthermore, if the information processing device 10 receives pulse rate data from the measurement terminal 30 in storage mode, it stores this pulse rate data in the storage data. If the information processing device 10 receives a feature value calculation instruction, it uses the storage data to calculate the RRI difference value and pulse rate variation coefficient, which are feature values, for each student ID and stores them in the feature value data. If the feature value data is updated, the information processing device 10 uses this feature value data to calculate the reference range for each state of mental health and stores it in the reference range data.

[0030] Furthermore, if the information processing device 10 receives pulse rate data from the measurement terminal 30 in judgment mode, it stores this pulse rate data in the pulse rate data. If the number of data points related to each student ID in the pulse rate data exceeds a predetermined number (for example, 75 or more), the information processing device 10 uses this pulse rate data to calculate feature values, and uses the calculated feature values ​​and reference range data to determine the mental health status of the student related to the student ID, and notifies the management terminal 20 of the judgment result.

[0031] Furthermore, if the information processing device 10 receives an analysis instruction from the management terminal 20, it performs an analysis process related to mental health status and notifies the management terminal 20 of the analysis results.

[0032] The management terminal 20 is a device that displays the mental health level of the subject determined by the information processing device 10. When the management terminal 20 receives an operation for analysis instructions, it notifies the information processing device 10 of these instructions. When the management terminal 20 receives the mental health level or analysis results from the information processing device 10, it displays the mental health level or analysis results.

[0033] The measurement terminal 30 is a device that measures the pulse rate of the person wearing the measurement terminal 30. The measurement terminal 30 measures the wearer's pulse rate and notifies the information processing device 10 of the pulse rate data via the wireless router 40 at predetermined intervals (for example, every 4 seconds).

[0034] <Configuration of the information processing device 10> Next, the configuration of the information processing device 10 shown in Figure 1 will be described. Figure 3 is a functional block diagram showing the configuration of the information processing device 10 shown in Figure 2. As shown in Figure 3, the information processing device 10 is connected to a display unit 11 and an input unit 12, and includes a communication unit 13, a storage unit, and a control unit 15.

[0035] The display unit 11 is a display device such as an LCD panel or a display device. The input unit 12 is an input device such as a keyboard or mouse. The communication unit 13 is an interface unit for data communication with the management terminal 20 and the measurement terminal 30 via a communication line.

[0036] The memory unit 14 is a storage device consisting of a non-volatile memory or a hard disk drive, and stores student data 14a, accumulated data 14b, feature value data 14c, reference range data 14d, pulse rate data 14e, and judgment data 14f.

[0037] Student data 14a is data that shows information about students who use the information processing system. Stored data 14b is data that shows RRI values ​​that have been measured in advance by the measurement terminal 30 for a predetermined period of time or longer (for example, 200 minutes or more).

[0038] Feature value data 14c is data showing the RRI difference value and pulse rate variability coefficient (hereinafter referred to as "feature value") calculated using the accumulated data 14b. Reference range data 14d is data showing the reference range for each state of mental health calculated using the feature value data 14c.

[0039] Pulse rate data 14e is data showing the RRI value measured by the measurement terminal 30 from students wearing the measurement terminal 30 within the facility. Judgment data 14f is data showing the mental health level of students during their studies.

[0040] The control unit 15 is a control unit that performs overall control of the information processing device 10, and includes a student data management unit 15a, a mode management unit 15b, a stored data management unit 15c, a feature value calculation unit 15d, a reference range calculation unit 15e, a pulse rate data management unit 15f, a determination unit 15g, an analysis unit 15h, and a notification unit 15i. In practice, programs corresponding to these functional units are stored in non-volatile memory such as ROM (not shown), and these programs are loaded into the CPU (Central Processing Unit) and executed, causing the processes corresponding to the student data management unit 15a, mode management unit 15b, stored data management unit 15c, feature value calculation unit 15d, reference range calculation unit 15e, pulse rate data management unit 15f, determination unit 15g, analysis unit 15h, and notification unit 15i to be executed.

[0041] The student data management unit 15a is a processing unit that manages student data 14a. When the student data management unit 15a receives student-related data from the input unit 12, it stores this data in student data 14a.

[0042] The mode management unit 15b is a processing unit that manages the mode of the information processing device 10. When the mode management unit 15b receives a specification of storage mode or determination mode from the input unit 12, it sets the information processing device 10 to the mode that was received.

[0043] The stored data management unit 15c is a processing unit that manages the stored data 14b. When the stored data management unit 15c receives pulse rate data from the measurement terminal 30 in storage mode, it stores this pulse rate data in the stored data 14b.

[0044] The feature value calculation unit 15d is a processing unit that calculates feature values ​​and manages the feature value data 14c. When the feature value calculation unit 15d receives a feature value calculation instruction from the input unit 12, it uses the stored data 14b to calculate the feature values, namely the RRI difference value and the pulse rate variability coefficient, for each student ID and stores them in the feature value data 14c. The calculation of feature values ​​will be explained later.

[0045] Furthermore, if the feature value calculation unit 15d receives a feature value calculation instruction from the pulse data management unit 15f, it calculates a feature value using the pulse data 14e related to the student ID included in this feature value calculation instruction, and passes the calculated feature value and student ID to the determination unit 15g.

[0046] The reference range calculation unit 15e is a processing unit that manages the reference range data 14d. When the feature value data 14c is updated, the reference range calculation unit 15e uses this feature value data 14c to calculate the reference range for each state of mental health and stores it in the reference range data 14d. The calculation of the reference range will be explained later.

[0047] The pulse data management unit 15f is a processing unit that manages pulse data 14e. When the pulse data management unit 15f receives pulse data from the measurement terminal 30 in judgment mode, it stores this pulse data in pulse data 14e.

[0048] Furthermore, if the number of data points related to each student ID in the pulse data 14e exceeds a predetermined number (for example, 75 or more), the pulse data management unit 15f passes a feature value calculation instruction including the corresponding student ID to the feature value calculation unit 15d.

[0049] The determination unit 15g is a processing unit that determines the mental health level of students and manages the determination data 14f. When the determination unit 15g receives feature values ​​and student IDs from the feature value calculation unit 15d, it uses the reference range data 14d related to the feature values ​​and student IDs to determine the mental health level of the student related to the student ID. The determination unit 15g stores the feature values ​​received from the feature value calculation unit 15d and the determination result in the determination data 14f.

[0050] The analysis unit 15h is a processing unit that performs analysis processing related to the student's motivation to learn using the judgment data 14f. When the analysis unit 15h receives an analysis instruction from the management terminal 20, it performs the following analysis processing: The analysis unit 15h evaluates the student's motivation to learn based on whether or not the feature values ​​of the judgment data 14f are within a predetermined threshold set in advance. When the feature values ​​of the judgment data 14f are plotted on a graph in time series, the analysis unit 15h extracts the direction in which the feature values ​​change and evaluates the student's motivation to learn. The specific evaluation procedure will be described later.

[0051] The notification unit 15i is a processing unit that notifies the results of the assessment of a student's mental health. If the assessment data 14f is updated, the notification unit 15i notifies the management terminal 20 of this assessment data 14f. If the analysis by the analysis unit 15h is completed, the notification unit 15i notifies the management terminal 20 of the analysis results.

[0052] Next, an example of data stored in the storage unit 14 of the information processing device 10 shown in Figure 3 will be described. Figures 4 to 6 show an example of student data 14a, stored data 14b, feature value data 14c, reference range data 14d, pulse rate data 14e, and judgment data 14f shown in Figure 3.

[0053] As shown in Figure 4(a), student data 14a associates the student ID "KD12345" with the name "Taro Tokachi", grade "1st year of junior high", gender "male", and age "13", and associates the student ID "KD67890" with the name "Hanako Kaihatsu", grade "2nd year of junior high", gender "female", and age "14".

[0054] As shown in Figure 4(b), the stored data 14b associates the student ID "KD12345" and date / time "2022 / 07 / 01 16:10:04" with an RRI value of "900", the student ID "KD12345" and date / time "2022 / 07 / 01 16:10:08" with an RRI value of "910", and the student ID "KD12345" and date / time "2022 / 07 / 01 16:10:12" with an RRI value of "950".

[0055] As shown in Figure 4(c), the feature value data 14c associates the student ID "KD12345" and date / time "2022 / 07 / 01 16:15:00" with an RRI difference value of "15" and a pulse rate variability coefficient of "7.5", the student ID "KD12345" and date / time "2022 / 07 / 01 16:16:00" with an RRI difference value of "21" and a pulse rate variability coefficient of "8.1", and the student ID "KD12345" and date / time "2022 / 07 / 01 16:17:00" with an RRI difference value of "8" and a pulse rate variability coefficient of "7.9".

[0056] As shown in Figure 5, the reference range data 14d associates the student ID "KD12345" with the following: mean value of the RRI difference related to the feature value data "0", standard deviation "40", mean value of the pulse rate variability coefficient "8.0", standard deviation "2.0", RRI difference value related to the arousal state "-80 to 80", pulse rate variability coefficient "4.0 to 12.0", RRI difference value related to the flow state "40 to 200", pulse rate variability coefficient "10.0 to 18.0", and RRI difference value related to the high-stress state "-180 to 20", pulse rate variability coefficient "9.0 to 17.0".

[0057] Furthermore, for student ID "KD67890," the following data are associated with the feature value data: mean value of "2" for the RRI difference, standard deviation of "35," mean value of "7.5" for the pulse rate variability coefficient, standard deviation of "1.8," RRI difference values ​​related to the arousal state of "-68~72" and pulse rate variability coefficient of "3.9~11.1," RRI difference values ​​related to the flow state of "37~177" and pulse rate variability coefficient of "9.3~16.5," and RRI difference values ​​related to the high-stress state of "-155.5~-15.5" and pulse rate variability coefficient of "8.4~15.6."

[0058] As shown in Figure 6(a), the pulse data 14e associates the student ID "KD12345" and date / time "2022 / 08 / 20 10:25:04" with an RRI value of "890", the student ID "KD12345" and date / time "2022 / 08 / 20 10:25:08" with an RRI value of "880", and the student ID "KD12345" and date / time "2022 / 08 / 20 10:25:12" with an RRI value of "900".

[0059] As shown in Figure 6(b), the judgment data 14f associates the student ID "KD12345" and date / time "2022 / 08 / 20 10:30:00" with an RRI difference value of "100", pulse rate variability coefficient of "6.5", and mental health status of "relaxed state"; the student ID "KD12345" and date / time "2022 / 08 / 20 10:31:00" with an RRI difference value of "85", pulse rate variability coefficient of "7.8", and mental health status of "relaxed state"; and the student ID "KD12345" and date / time "2022 / 08 / 20 10:32:00" with an RRI difference value of "68", pulse rate variability coefficient of "9.0", and mental health status of "awake state".

[0060] <The relationship between tension levels and the ability to perform> Next, we will explain the relationship between tension levels and the abilities that can be demonstrated. A person's ability to demonstrate is influenced by their level of tension. This relationship between tension levels and demonstrable abilities can be explained by the Yerkes-Dodson Law. Figure 7 is an explanatory diagram for illustrating the Yerkes-Dodson Law.

[0061] As shown in Figure 7, as a person's level of tension increases, their abilities (for example, the number of words they can remember) also increase, but beyond a certain point, those abilities begin to decline.

[0062] In Figure 7, range a indicates a low level of tension, resulting in fewer words being memorized. Range b indicates a moderate level of tension, allowing for efficient memorization of words. Range c indicates excessive tension, resulting in fewer words being memorized.

[0063] When performing intellectually demanding tasks, an appropriate level of tension is required to enable mental concentration. On the other hand, in the case of complex, unfamiliar, or difficult tasks, the relationship between tension and the ability to perform is reversed; it is said that as tension increases, the ability to perform decreases.

[0064] Furthermore, there is a psychological concept called "Csikszentmihalyi's flow theory." "Flow" refers to an optimal state of being completely absorbed in an activity, so engrossed that one loses track of time. The opposite of this flow state is a state of high stress or low motivation. People transition between high stress / low motivation and flow depending on the environment they are in.

[0065] Figure 8 illustrates the transition from a high-stress or low-motivation state to a flow state. As shown in Figure 8(a), when a person is asked to perform a high-stress task (for example, a math problem), if the challenge exceeds their capabilities, they will enter a high-stress state (a1).

[0066] In a high-stress state, efficient skill improvement cannot be expected, so it is necessary to transition to a flow state. For example, by lowering the level of the challenge, the challenge becomes appropriate to the individual's abilities, thus transitioning to a flow state (a2). Alternatively, by continuing to take on a high challenge, even if it is initially inefficient, skill improvement is gradually promoted, and then a transition to a flow state is achieved (a3).

[0067] Furthermore, as shown in Figure 8(b), if the challenge continues in the flow state (b1), the ability improves, leading to a state of decreased motivation (b2). In this case, increasing the level of the challenge will lead to a transition back to the flow state (b3).

[0068] <Calculation of Feature Values> Next, the calculation of feature values ​​according to this embodiment will be explained. It is believed that when a person experiences stress, the sympathetic nervous system is activated and the parasympathetic nervous system is suppressed at the same time. Pulse rate is closely related to the level of tension, and is higher during work than at rest. As learning progresses in a particular task, the level of tension towards that task decreases compared to the initial state, and the pulse rate tends to decrease compared to the initial state. Also, since the difficulty level differs depending on the type of work task, the pulse rate also differs depending on the work task. Furthermore, it is said that when a person shifts to a different emotional state, their heart rhythm changes immediately.

[0069] Therefore, in this embodiment, characteristic values ​​(RRI difference value and pulse rate variability coefficient) based on RRI are calculated, and the mental health status of the subject is determined using these characteristic values. First, let's explain RRI. Figure 9 is an explanatory diagram for explaining RRI. As shown in Figure 9, in an electrocardiogram (ECG), R waves (waves during heartbeat) from R1 to R4 are recorded, and the interval between these R waves is the RRI.

[0070] A normal heartbeat may appear to beat regularly, but closer observation reveals that it fluctuates with each pulse. For example, in Figure 9, the RRI fluctuates between 900ms, 910ms, and 950ms.

[0071] The RRI is acquired over a predetermined period of time, and the RRI difference value is calculated by adding the average of all acquired RRIs to the difference between each individual RRI. The method for calculating the RRI difference value is as follows.

[0072] The average of all RRI() data (number of data points: n) obtained for each individual is calculated using the following formula.

number

[0073] RRI difference value (y i The RRI difference value (y) is calculated using RRI data from the past 5 minutes from the calculation point (i). In this embodiment, since the measurement terminal 30 acquires RRI every 4 seconds, the RRI difference value is calculated using 75 RRI data points corresponding to 5 minutes. i ) is calculated every minute using the following formula.

number

[0074] Let's explain using the specific example in Figure 4. We will explain how to calculate the RRI difference value "15" for the date and time "2022 / 07 / 01 16:15:00" in the feature value data 14c shown in Figure 4(c). First, we calculate the average of all the RRI values ​​for student ID "KD12345" in the accumulated data 14b shown in Figure 4(b). The RRI data to be used to calculate the RRI difference value "15" are the 75 RRI values ​​from the date and time "2022 / 07 / 01 16:10:04" to the date and time "2022 / 07 / 01 16:15:00" in the accumulated data 14b shown in Figure 4(b). Using these 75 RRI values ​​and the average value calculated above, we calculate the RRI difference value using (Equation 2).

[0075] Similarly, the RRI difference value of "21" at the date and time "2022 / 07 / 01 16:16:00" in the feature value data 14c is calculated using (Equation 2) with the 75 RRI values ​​from the date and time "2022 / 07 / 01 16:11:04" to the date and time "2022 / 07 / 01 16:16:00" in the accumulated data 14b, and the average value calculated above.

[0076] Next, we will explain the coefficient of variation of RR interval (CVRR). The coefficient of variation is a coefficient that indicates the degree of variation in heart rate, and is expressed by the following formula.

number

[0077] SDNN (standard deviation of NN intervals) is the standard deviation of RRI and is expressed by the following formula.

number

[0078] The pulse rate variability coefficient (C) in this embodiment i Since it is calculated using data from 75 RRIs, it can be expressed by the following formula.

number

[0079] Let's explain using the specific example in Figure 4. We will explain the calculation method for the pulse rate variability coefficient "7.5" for the date and time "2022 / 07 / 01 16:15:00" in the feature value data 14c shown in Figure 4(c). First, we calculate the average of all RRI values ​​for student ID "KD12345" in the accumulated data 14b shown in Figure 4(b). The RRI data to be used to calculate the pulse rate variability coefficient "7.5" are the 75 RRI values ​​from the date and time "2022 / 07 / 01 16:10:04" to the date and time "2022 / 07 / 01 16:15:00" in the accumulated data 14b shown in Figure 4(b). Using these 75 RRI values ​​and the average value calculated above, we calculate the pulse rate variability coefficient using (Equation 5).

[0080] Similarly, the pulse rate variability coefficient "8.1" for the date and time "2022 / 07 / 01 16:16:00" in the feature value data 14c is calculated using (Equation 5) with the 75 RRI values ​​from the date and time "2022 / 07 / 01 16:11:04" to the date and time "2022 / 07 / 01 16:16:00" in the accumulated data 14b and the average value calculated above.

[0081] <Relationship between characteristic values ​​and mental health> Next, the relationship between the feature values ​​and mental health levels according to this embodiment will be explained. Figure 10 is a diagram showing the relationship between the feature values ​​and mental health levels according to this embodiment.

[0082] In this embodiment, mental health levels are classified into "awake state," "flow state," "relaxed state," "decreased motivation state," "drowsy / sleeping state," and "high-stress state." As shown in Figure 10, characteristic features are observed in the characteristic values ​​of each mental health state. When the mental health state changes, the change in the RRI difference value and the change in the pulse rate variability coefficient are inversely proportional.

[0083] For example, the RRI difference value increased from 641 in the awake state to 819 in the sleep state, while the pulse rate variability coefficient decreased from 6.8 in the awake state to 3.3 in the sleep state.

[0084] Furthermore, the RRI difference value decreased from 819 in the sleep state to 569 in the flow state, while the pulse rate variability coefficient increased from 3.3 in the sleep state to 9.2 in the flow state.

[0085] <Mental Health Matrix> Next, the mental health matrix according to this embodiment will be described. Figure 11 is a diagram showing the mental health matrix according to this embodiment.

[0086] As shown in Figure 11, mental health is represented as a matrix on a graph with the RRI difference value on the x-axis and the pulse rate variability coefficient on the y-axis, with reference ranges indicating the range of each state. The reference range is defined by the median value, which is the center of the range, and the deviation from the median.

[0087] First, let's explain the median of the reference range. The median for each condition is calculated using the mean and standard deviation of the RRI difference value and pulse rate variability coefficient, respectively, as follows.

[0088] In the awake state, the following applies: Median of RRI difference = mean ± standard deviation × 1.5 (Equation 6) Median pulse rate variability = mean ± standard deviation × 1.5 (Equation 7)

[0089] In equations (6) and (7), the mean is shown within a range of ± the standard deviation, but to define the median, which is the central value, the following equation is used. The median of the RRI difference values ​​= the mean value (Equation 6') Median of pulse rate variability = mean value (Equation 7') For example, using the feature value data of reference range data 14d in Figure 5, the median of the awake state is (0, 8.0).

[0090] In the flow state, the following applies: Median of RRI difference = Mean + Standard Deviation × 3.0 or greater (Equation 8) Median pulse rate variability = mean + standard deviation × 3.0 or greater (Equation 9)

[0091] In equations (8) and (9), the range is shown by adding the standard deviation × 3.0 or more to the mean, but in order to define the median, which is the central value, the following equation is used. Median of RRI difference = Mean + Standard Deviation × 3.0 (Equation 8') Median of pulse rate variability = mean + standard deviation × 3.0 (Equation 9') For example, using the feature value data of reference range data 14d in Figure 5, the median of the flow state is (120, 14.0).

[0092] In a relaxed state, the following applies: Median of RRI difference = Mean + Standard Deviation × 2.0 or greater (Equation 10) Median pulse rate variability = Mean - Standard deviation × 2.0 or greater (Equation 11)

[0093] In equations 10 and 11, the range is shown by adding / subtracting a standard deviation of 2.0 or more from the mean, but in order to define the median, which is the central value, the following equation is used. Median of RRI difference = Mean + Standard Deviation × 2.0 (Equation 10') Median of pulse rate variability = Mean - Standard deviation × 2.0 (Equation 11') For example, using the feature value data of reference range data 14d in Figure 5, the median for the relaxed state is (80, 4.0).

[0094] In a state of decreased motivation, the following occurs: Median of RRI difference = Mean - Standard Deviation × 2.0 or greater (Equation 12) Median pulse rate variability = Mean - Standard deviation × 2.5 or greater (Equation 13)

[0095] In equations 12 and 13, the range is shown by subtracting a standard deviation of 2.0 / 2.5 or more from the mean, but in order to define the median, which is the central value within that range, the following equation is used. Median of RRI difference = Mean - Standard Deviation × 2.0 (Equation 12') Median of pulse rate variability = Mean - Standard deviation × 2.5 (Equation 13') For example, using the feature value data of the baseline data 14d in Figure 5, the median value for the state of decreased motivation is (-80, 3.0).

[0096] Regarding drowsiness and sleep states, the following applies: Median of RRI difference = Mean + Standard Deviation × 4.0 or greater (Equation 14) Median pulse rate variability = Mean - Standard deviation × 3.0 or greater (Equation 15)

[0097] In equations 14 and 15, the range is shown by adding / subtracting a standard deviation of 4.0 / 3.0 or more from the mean, but in order to define the median, which is the central value within that range, the following formula is used. Median of RRI difference = Mean + Standard Deviation × 4.0 (Equation 14') Median of pulse rate variability = Mean - Standard deviation × 3.0 (Equation 15') For example, using the feature data of reference range data 14d in Figure 5, the median values ​​for drowsiness / sleep status are (160, 2.0).

[0098] Under high-stress conditions, the following applies: Median of RRI difference = Mean - Standard Deviation × 2.5 or greater (Equation 16) Median pulse rate variability = mean + standard deviation × 2.5 or greater (Equation 17)

[0099] In equations 16 and 17, the range is shown by subtracting / adding a standard deviation of 2.5 or more from the mean, but in order to define the median, which is the central value within that range, the following equation is used. Median of RRI difference = Mean - Standard deviation × 2.5 (Equation 16') Median of pulse rate variability = mean + standard deviation × 2.5 (Equation 17') For example, using the feature data of the baseline data 14d in Figure 5, the median values ​​for high stress levels are (-100, 13.0).

[0100] Next, we will explain the deviation width from the median of the reference range. This deviation width is set using the standard deviations of the RRI difference value and the pulse rate variability coefficient. In the reference range data 14d in Figure 5, the standard deviation of the RRI difference value is "40" and the standard deviation of the pulse rate variability coefficient is "2.0". If we define this value as 1SD (standard deviation), then in this embodiment, 2SD is used as the deviation width from the median. This deviation width can also be specified as 1SD or other values.

[0101] For example, in the awake state, the median is (0, 8.0), and the reference range is RRI difference value "-80 to 80" and pulse rate variability coefficient "4.0 to 12.0". In the flow state, the median is (120, 14.0), and the reference range is RRI difference value "40 to 200" and pulse rate variability coefficient "10.0 to 18.0". The mental health matrix shown in Figure 11 is a graph representing the reference ranges for each state set in this way.

[0102] The information processing system in this embodiment acquires pulse rate data from a measurement terminal 30 worn by the student during learning, calculates the student's characteristic values ​​at that time using this data, and determines the student's mental health by plotting these characteristic values ​​on a mental health matrix.

[0103] For example, if a student's characteristic values ​​are an RRI difference of "-50" and a pulse rate variability coefficient of "14.0", they are judged to be in a high-stress state. Conversely, if the RRI difference is "100" and the pulse rate variability coefficient is "2.0", they are judged to be in a relaxed state or a drowsy / sleeping state. Thus, there is an overlapping range for each state on the mental health matrix, and if a student's characteristic values ​​are plotted within this range, the student's mental health is judged to be in both states.

[0104] Mental health is categorized into "arousal," "flow," "relaxed," "decreased motivation," "drowsiness / sleep," and "high stress." For each state determined in the mental health matrix, the following measures can be considered to enhance mental stability or learning effectiveness.

[0105] In the "awakened state," there is a balance between ability and challenge, and the current situation is maintained. In the "flow state," growth is possible, and the current situation is maintained. In the "relaxed state," mental and physical stability is achieved.

[0106] In a state of "decreased motivation," the balance between ability and challenge is off, requiring adjustments to raise or lower the difficulty level of tasks and the required skill level according to the student's situation. In a state of "drowsiness or sleepiness," there is a significant imbalance between ability and challenge, or improvement of sleep problems is required. In a state of "high stress," relaxation and achieving mental and physical stability are required.

[0107] <An example of mental health analysis> Next, an example of mental health analysis according to this embodiment will be described. Figures 12 to 14 show an example of mental health analysis according to this embodiment.

[0108] Figures 12 and 13 plot the RRI difference value and pulse rate variability coefficient on the y axis against time on the x axis. In each figure, if the values ​​are plotted within the range indicated by the border, the student is judged to be actively learning.

[0109] In Figure 12, since the values ​​are generally plotted within the bounding box, it can be determined that the user is actively learning. On the other hand, in Figure 13, since the values ​​are plotted below the bounding box for a longer period of time, it can be determined that the user's motivation to learn is decreasing.

[0110] Figure 14 plots the RRI difference value on the x-axis and the pulse rate variability coefficient on the y-axis in a time series, and extracts the direction of change in the plot position. If this direction is towards the upper left, it can be determined that the student's motivation to learn is decreasing, and if it is towards the upper right, it can be determined that their motivation to learn is improving.

[0111] For example, in Figure 14(a), the plotted position is sloping towards the upper left, indicating a decrease in motivation to learn. In Figure 14(b), the plotted position is sloping towards the upper right, indicating an improvement in motivation to learn.

[0112] In this way, by plotting the RRI difference value and pulse rate variability coefficient on a graph and extracting the characteristics of that graph, or by checking where the student falls within the reference range of the mental health matrix, it is possible to determine each student's mental state toward learning. Teachers who instruct students can use this objective assessment result, in addition to their own experience and intuition, to strive to understand students' growth, stumbling blocks, and worries, and to provide detailed guidance and learning support that takes into account each student's interests, concerns, and motivation.

[0113] Furthermore, by recognizing the assessment results, students can understand their own learning situation and engage in learning proactively. For example, they can recognize at what point in their learning motivation changed, and by sharing this change with their teacher, they can contribute to improving the teacher's instruction. This sharing of learning status between students and teachers is expected to be particularly effective in online education.

[0114] Furthermore, by registering students' study schedules in advance, it becomes possible to analyze the relationship between changes in students' mental health and their study schedules. This analysis can then be used to optimize the balance between students' abilities and challenges.

[0115] <Processing procedure for outputting the reference range> Next, the processing procedure for the reference range output processing in the information processing device 10 according to this embodiment will be described. Figure 15 is a flowchart showing the processing procedure for the reference range output processing in the information processing device 10 according to this embodiment.

[0116] If the information processing device 10 has acquired pulse rate data from the measurement terminal 30, it stores this pulse rate data in the stored data 14b (step S101). If the acquisition of pulse rate data is not yet complete (step S102; No), the process proceeds to step S101.

[0117] Once the acquisition of pulse rate data is complete (Step S102; Yes), the feature values ​​RRI difference value and pulse rate variability coefficient are calculated from the accumulated data 14b and stored in the feature value data 14c (Step S103).

[0118] Using the feature value data 14c, the mean and standard deviation of each feature value are calculated (step S104). Using the calculated mean and standard deviation, the median of the reference range for each state is calculated (step S105).

[0119] The range of 2 standard deviations from the calculated median is used as the reference range, and the reference range data 14d is stored (step S106), and the process is terminated.

[0120] <Processing procedure for determining mental health> Next, the processing procedure for determining mental health status in the information processing device 10 according to this embodiment will be described. Figure 16 is a flowchart showing the processing procedure for determining mental health status in the information processing device 10 according to this embodiment.

[0121] The information processing device 10 sets the variable n to 0 (step S201). If pulse rate data is received from the measurement terminal 30 (step S202; Yes), 1 is added to the variable n and the received pulse rate data is stored in pulse rate data 14e (step S203). If the variable n is less than 75 (step S204; No), the process proceeds to step S202.

[0122] If the variable n is 75 or greater (Step S204; Yes), subtract 15 from the variable n (Step S205), and calculate the feature value using the pulse rate data 14e (Step S206).

[0123] The mental health level is determined using the calculated feature values ​​and reference range data 14d (step S207), and the determination result is notified to the management terminal 20 (step S208). If the mental health level determination is to be continued (step S209; Yes), the process proceeds to step S202. If the mental health level determination is to be terminated (step S209; No), the process ends.

[0124] As described above, the information processing system according to this embodiment is configured to calculate a reference range using pulse rate data acquired in advance, and to determine and provide the mental health level of a subject using pulse rate data acquired from the subject during the learning process and the reference range. Therefore, it is possible to determine and provide the mental health level of a subject in real time using the pulse rate data of the subject that has been collected.

[0125] In the above embodiment, a configuration for determining the mental health of students learning was described, but the present invention is not limited thereto, and can also be configured to be applied to the health management of working people, the verification of game content and educational content, and the prevention of dementia.

[0126] Furthermore, the configurations illustrated in the above embodiments are functionally schematic and do not necessarily have to be physically represented as shown. In other words, the form of distribution and integration of each device is not limited to those shown, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions. [Industrial applicability]

[0127] The information processing system, information processing device, information processing method, and information processing program according to the present invention are suitable for determining the mental health level of a subject in real time. [Explanation of symbols]

[0128] 10 Information Processing Devices 11 Display section 12 Input section 13 Communications Department 14 Storage section 14a Student Data 14b Accumulated data 14c Feature Value Data 14d Reference Range Data 14e Pulse data 14f Judgment Data 15 Control Unit 15a Student Data Management Department 15b Mode Management Unit 15c Data Management Department 15d Feature Value Calculation Unit 15e Reference area calculation section 15f Pulse Data Management Department 15g Judgment part 15h Analysis Department 15i Notification section 20 Management terminals 30 Measurement terminals 40 Wireless Routers

Claims

1. An information processing system comprising an information processing device for determining the mental health status of a person being measured, and a measurement terminal that is communicatively connected to the information processing device, The aforementioned measuring terminal is A pulse information acquisition means for acquiring pulse information of the person being measured, A pulse information notification means that notifies the pulse information acquired by the pulse information acquisition means, It has, The aforementioned information processing device is A storage means for storing pulse information received from the aforementioned measuring terminal, A calculation means that uses a predetermined number of pulse information from the pulse information stored in the storage means to calculate the RRI difference value and pulse rate variation coefficient, which are characteristic value information, A generation means that uses the aforementioned feature value information to calculate the mean and standard deviation of the RRI difference value and the pulse rate variability coefficient, respectively, and uses the mean and standard deviation to generate reference range information for each state of mental health, A determination means that determines the state corresponding to the mental health status of the person being measured when the pulse information corresponds to any of the mental health statuses included in the reference range information, A notification means for notifying the mental health level determined by the determination means, and An information processing system characterized by having the following features.

2. The aforementioned notification means is, The information processing system according to claim 1, characterized in that it notifies a predetermined management device of the mental health level and causes the management device to display the mental health level on its display unit.

3. The aforementioned mental health level is, The information processing system according to claim 1 or 2, characterized in that it includes at least an awakened state, a flow state, a relaxed state, a state of decreased motivation, a drowsy / sleeping state, and a high-stress state.

4. An information processing device that is communicatively connected to a measurement terminal that acquires pulse rate information of a person being measured, A storage means for storing pulse information received from the aforementioned measuring terminal, A calculation means that uses a predetermined number of pulse information from the pulse information stored in the storage means to calculate the RRI difference value and pulse rate variation coefficient, which are characteristic value information, A generation means that uses the aforementioned feature value information to calculate the mean and standard deviation of the RRI difference value and the pulse rate variability coefficient, respectively, and uses the mean and standard deviation to generate reference range information for each state of mental health, A determination means that determines the state corresponding to the mental health status of the person being measured when the pulse information corresponds to any of the mental health statuses included in the reference range information, A notification means for notifying the mental health level determined by the determination means, and An information processing device characterized by having the following features.

5. An information processing method in an information processing system comprising an information processing device for determining the mental health status of a person being measured, and a measurement terminal that is communicatively connected to the information processing device, The measurement terminal includes a pulse information acquisition step for acquiring pulse information of the person being measured, and a pulse information notification step for notifying the pulse information acquired in the pulse information acquisition step. The information processing device includes a storage step of storing pulse information received from the measurement terminal in a predetermined storage unit, and a calculation step of calculating a RRI difference value and pulse rate variation coefficient, which are characteristic value information, using a predetermined number of pulse information from the pulse information stored in the storage unit. The information processing device performs a generation step of using the feature value information to calculate the mean and standard deviation of the RRI difference value and the pulse rate variability coefficient, respectively, and using the mean and standard deviation to generate reference range information for each state of mental health. The information processing device includes a determination step of determining that the state corresponding to the mental health status included in the reference range information is the mental health status of the person being measured, when the pulse information corresponds to one of the states of mental health status included in the reference range information. The information processing device includes a notification step that notifies the mental health level determined by the determination step. An information processing method characterized by including

6. An information processing program executed on an information processing device that is communicatively connected to a measurement terminal that acquires pulse rate information of a subject and a management terminal that displays mental health status, A storage procedure for storing pulse information received from the aforementioned measuring terminal in a predetermined storage unit, A calculation procedure for calculating the RRI difference value and pulse rate variability coefficient, which are characteristic value information, using a predetermined number of pulse rate information from the pulse rate information stored in the memory unit, A generation procedure that uses the aforementioned feature value information to calculate the mean and standard deviation of the RRI difference value and the pulse rate variability coefficient, respectively, and uses the mean and standard deviation to generate reference range information for each state of mental health, A determination procedure for determining the mental health status of the person being measured if the pulse information corresponds to any of the mental health statuses included in the reference range information, A notification procedure for notifying the mental health level determined by the aforementioned determination procedure, and An information processing program characterized by causing a computer to execute it.

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