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

Abstract

The objective is to provide an information processing device, an information processing method, and an information processing program that can improve a user's physical condition by prompting actions appropriate to the user's state. [Solution] The information processing device includes a heart rate variability acquisition unit 110A that acquires the user's heart rate variability, and a presentation unit 110B that presents improvement actions to improve the heart rate variability based on the acquired heart rate variability.

Description

Technical Field

[0001] The disclosed technique relates to an information processing apparatus, an information processing method, and an information processing program.

Background Art

[0002] Patent Document 1 discloses an information processing apparatus that improves the accuracy of determining an abnormality based on data related to a user's body compared to conventional techniques. This information processing apparatus includes an acquisition unit that acquires measurement data including data measured in relation to the user's body, the acquired measurement data, and a model generated for each user to estimate the user's heart rate from the measurement data, and an estimation unit that estimates the heart rate of the user at a future point in time and a normal range that is a normal heart rate range for the user at the future point in time, and when the estimated heart rate at the future point in time, or the heart rate of the user measured when the future point in time arrives, falls outside the estimated normal range of heart rates at the future point in time, a notification unit that issues a notification, and an update unit that updates the model using the acquired measurement data.

Prior Art Literature

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problem to be Solved by the Invention

[0004] It is known that when a person's physical state (for example, the balance of their autonomic nervous system) becomes unbalanced due to reasons such as excessive concentration, they are more likely to experience negative emotions. However, people often do not realize that their physical state is unbalanced, and before they know it, they are experiencing negative emotions. Therefore, there was room for improvement in ensuring that people rest and improve their physical condition before they even realize that their physical state is unbalanced.

[0005] The disclosed technology aims to provide an information processing device, an information processing method, and an information processing program that can improve a user's physical condition by prompting actions that are appropriate to the user's state. [Means for solving the problem]

[0006] The information processing device according to the first embodiment includes a heart rate variability acquisition unit that acquires the user's heart rate variability, and a presentation unit that presents improvement actions to improve the heart rate variability based on the acquired heart rate variability.

[0007] The information processing device according to the second embodiment includes a setting unit for setting rest timings at predetermined intervals, and the heart rate variability acquisition unit acquires heart rate variability measured at the set rest timings.

[0008] The information processing device according to the third embodiment includes a plan acquisition unit that acquires a work plan that includes at least information regarding the time of work input by the user, and the setting unit sets the rest timing according to the acquired work plan.

[0009] The information processing device according to the fourth embodiment includes an evaluation unit that evaluates the psychological state of the user when the rest timing has elapsed a predetermined number of times, based on a measurement value that includes at least one of the user's heart rate measured by a heart rate sensor and the user's heart rate variability acquired, and the presentation unit presents rest actions that allow the user to rest, based on the evaluation by the evaluation unit.

[0010] The information processing device according to the fifth embodiment, in the information processing device according to the fourth embodiment, presents the resting behavior output from a trained model that has been trained to output the resting behavior by inputting the evaluation using training data including feedback information from the user when presenting the resting behavior.

[0011] The information processing device according to the sixth embodiment includes an information processing device according to the fourth embodiment, which includes an achievement receiving unit that receives performance records of work performed by the user from the user, and the evaluation unit evaluates the user's emotions based on the performance records received by the achievement receiving unit, in addition to the measured values.

[0012] The information processing device according to the seventh embodiment, in the information processing device according to the sixth embodiment, presents the reflection content generated by inputting a prompt sentence instructing the generating AI to estimate the measured value, the performance record, and the reflection content based on the user's emotions, when receiving the reflection content of the work.

[0013] The information processing device according to the eighth embodiment includes a recording unit that records the user's responses to predetermined questions based on the user's psychological state, and the presentation unit presents the user's mental state, which is generated by inputting a prompt sentence instructing a generating AI to estimate the user's mental state based on the measured value and the recorded responses.

[0014] The information processing device according to the ninth embodiment is an information processing device according to any one of the first to seventh embodiments, wherein the presentation unit, when presenting the improvement action, presents the improvement action output from a trained model that has been trained to output the improvement action by inputting the heart rate variability using training data including feedback information from the user.

[0015] The information processing device according to the tenth embodiment includes a selection receiving unit that, when presenting the improvement action, receives a selection from the user whether or not to perform the improvement action, in addition to the information processing device according to any one of the first to eighth embodiments.

[0016] The information processing device according to the 11th embodiment is an information processing device according to the 10th embodiment in which the presentation unit presents a plurality of improvement actions when presenting the improvement actions, and the selection receiving unit receives the improvement action selected from the plurality of improvement actions when receiving the selection to perform the improvement actions.

[0017] The information processing device according to the twelfth embodiment is an information processing device according to any one of the first to eleventh embodiments, wherein the presentation unit presents the improvement action until a predetermined number of times set for each improvement action is reached, if the improvement action does not improve the heart rate variability.

[0018] The information processing device according to the 13th embodiment is an information processing device according to any one of the first to 12 embodiments, wherein the presentation unit presents the user's improvement behavior set based on the measured brainwave information of the user.

[0019] The information processing method according to the 14th embodiment involves a computer performing a process to acquire the user's heart rate variability and, based on the acquired heart rate variability, suggest improvement actions to improve the heart rate variability.

[0020] An information processing program according to a fifteenth aspect causes a computer to execute processing of acquiring a user's heart rate variability, and presenting an improvement action for improving the heart rate variability based on the acquired heart rate variability. [Effects of the Invention]

[0021] According to the disclosed technology, it is possible to improve the user's physical condition by prompting an action corresponding to the user's state. [Brief Description of the Drawings]

[0022] [Figure 1] It is a diagram showing an outline of the configuration of a rest proposal system according to the first embodiment. [Figure 2] It is a block diagram showing an example of the hardware configuration of the wearable terminal according to the first embodiment. [Figure 3] It is a diagram schematically showing an example of the configuration of a storage according to the first embodiment. [Figure 4] It is a block diagram showing an example of the hardware configuration of a center server according to the first embodiment. [Figure 5] It is a block diagram showing an example of the configuration of a learned model storage unit according to the first embodiment. [Figure 6] It is a block diagram showing an example of the functional configuration of the wearable terminal according to the first embodiment. [Figure 7] It is a flowchart showing an example of rest proposal processing according to the first embodiment. [Figure 8A] It is an example of a display screen of the wearable terminal according to the first embodiment. [Figure 8B] It is an example of a display screen of an auxiliary terminal according to a modification of the first embodiment. [Figure 9] It is a flowchart showing an example of improvement action proposal processing according to the first embodiment. [Figure 10] It is an example of a display screen of the wearable terminal according to the first embodiment. [Figure 11] It is a flowchart showing an example of rest action proposal processing according to the first embodiment. [Figure 12] This is an example of the display screen of a wearable device according to the first embodiment. [Figure 13] This is a flowchart showing an example of the review process flow according to the first embodiment. [Figure 14] This is an example of the display screen of a wearable device according to the first embodiment. [Figure 15] This is an example of the flow of the rest action suggestion process according to the second embodiment. [Figure 16] This is a flowchart showing an example of the query processing flow according to the second embodiment. [Modes for carrying out the invention]

[0023] The rest suggestion system 10 according to this embodiment will be described below with reference to the drawings. In each drawing, the same or equivalent components are given the same reference numerals. Also, the dimensional ratios in the drawings are exaggerated for illustrative purposes and may differ from the actual ratios. Furthermore, this disclosure is not limited in any way to the following embodiments and can be implemented with appropriate modifications within the scope of the purpose of this disclosure.

[0024] <Summary of this embodiment> Individuals with developmental disorders such as ASD (Asperger's Syndrome) or ADHD (Attention Deficit Hyperactivity Disorder), or those with characteristics such as perfectionism, workaholism, or HSP (Highly Sensitive Person), tend to burn out easily due to excessive focus.

[0025] When hyperfocus leads to a state of malaise, one suffers from various cognitive distortions. For example, one might equate "not being able to do something perfectly" with "being completely useless," or overgeneralize a single failure. Furthermore, one may tend to focus on negative information, overlook positive aspects, and fall into black-and-white thinking, viewing things in extremes.

[0026] Therefore, in this embodiment, the system is configured to suggest the optimal rest period based on the measured heart rate variability (HRV). This restores the user's physical condition to a state where they can make appropriate decisions.

[0027] Here, heart rate variability (HDR) is a value that represents the variation in the time interval (RRI: RR Interval) between each heartbeat. Specifically, HDR is expressed as the standard deviation of the RRI over a predetermined period (SDNN: Standard Deviation of the RR interval), and / or the square root of the mean of the squared differences between two consecutive RRIs (rMSSD: root Mean Square of Successive RR interval Differences). In this embodiment, the guidelines for evaluating the user's physical condition based on HDR are defined using SDNN as shown in Table 1 below. In the following explanation, good HDR is defined as a HDR value of 40 ms or more.

[0028] [Table 1]

[0029] [First Embodiment] (Rest suggestion system) Figure 1 is a diagram illustrating the schematic configuration of the rest suggestion system 10 according to the first embodiment. As shown in Figure 1, the rest suggestion system 10 of this embodiment comprises a wearable terminal 100, a central server 200, and an auxiliary terminal 300. The wearable terminal 100, the central server 200, and the auxiliary terminal 300 are connected via a network N. The network N can be, for example, the Internet, a LAN (Local Area Network), or a WAN (Wide Area Network). In this embodiment, the case in which a user uses the wearable terminal 100, the central server 200, and the auxiliary terminal 300 will be described. Note that multiple users may use the rest suggestion system 10 using their respective wearable terminals 100 and auxiliary terminals 300. In other words, there may be multiple wearable terminals 100 and auxiliary terminals 300 connected to the central server 200. The wearable terminal 100 is an example of an "information processing device".

[0030] The wearable device 100 is a portable device used by a user while it is attached to their body. Examples of wearable devices 100 include wristband-type devices, smartwatch-type devices, clip-type devices, eyeglass-type devices, and ring-type devices, all of which are equipped with a heart rate sensor and capable of wireless communication. Below, we will describe the case where a smartwatch, which is an example of a smartwatch-type device, is used as the wearable device 100.

[0031] The central server 200 is a server that manages user information. For example, the central server 200 is a device managed by the operating company of the rest suggestion system 10. Examples of central servers 200 include general-purpose information processing devices such as personal computers and server computers.

[0032] The auxiliary terminal 300 is a user-owned information terminal capable of inputting and outputting various types of information. Examples of auxiliary terminals 300 include smartphones, tablet devices, and personal computers. The auxiliary terminal 300 is used to input information that is difficult to input from the wearable device 100, or to display information that is difficult to display on the wearable device 100. The following section will describe the case where a smartphone is used as the auxiliary terminal 300.

[0033] (Wearable device) Figure 2 is a block diagram showing an example of the hardware configuration of a wearable terminal 100 according to the first embodiment. The wearable terminal 100 of this embodiment is composed of a CPU (Central Processing Unit) 110, ROM (Read Only Memory) 120, RAM (Random Access Memory) 130, storage 140, communication I / F 150, and input / output I / F 160. Each component is connected to the others via a bus 170 so as to be able to communicate with each other.

[0034] The CPU 110 is a central processing unit that executes various programs and controls various parts. The ROM 120 stores various programs and data. In this embodiment, the ROM 120 stores data including the proposed program 121. The proposed program 121 may also be stored in the storage 140, which will be described later. The RAM 130 temporarily stores programs or data as a working area. That is, the CPU 110 reads a program from the ROM 120 or storage 140 and executes the program using the RAM 130 as a working area.

[0035] The proposed program 121 is a program that executes processes including the rest suggestion process (see Figure 7) and the reflection process (see Figure 13), which will be described later. When the proposed program 121 is executed, the wearable terminal 100 uses various hardware resources to execute processes based on the proposed program 121.

[0036] The storage 140 is composed of flash memory, an SSD (Solid State Drive), etc., and stores various programs and various data. Figure 3 is a schematic diagram showing an example of the configuration of the storage 140 according to the first embodiment. The storage 140 stores data including heart rate data 141, heart rate variability data 142, response data 143, work plan data 144, and performance record data 145.

[0037] Heart rate data 141 is data showing the user's heart rate measured by the heart rate sensor 161, which will be described later. As an example, heart rate data 141 stores data showing the user's heart rate for one week.

[0038] Heart rate variability data 142 is data showing the user's heart rate variability calculated based on heart rate data 141. As an example, heart rate variability data 142 stores data showing the user's heart rate variability for one week.

[0039] Response data 143 represents data showing responses received from users. For example, response data 143 is data from a questionnaire about user behavior during rest periods. Data showing responses received from users is an example of "feedback information."

[0040] Work plan data 144 is data of a work plan entered by the user. Specifically, the work plan is data that shows the user's work plan, including the start time, end time, and work content. As an example, work plan data 144 stores the work content for each hour of working hours.

[0041] The performance record data 145 is data showing the actual results against the daily work plan entered by the user. Specifically, the performance record data includes data entered by the user, such as the progress of the work at the time of recording, the type and degree of the user's emotions, text, and audio. The performance record data only needs to record the actual work performed by the user, and a work plan does not necessarily need to have been created.

[0042] The communication interface 150 is an interface for communicating with other devices. Specifically, the communication interface 150 communicates with various devices such as the center server 200 and auxiliary terminals 300 via a network. Wireless communication standards such as 4G, 5G, Wi-Fi (registered trademark), or Bluetooth (registered trademark) are used for this communication. The wearable terminal 100 may also communicate with the center server 200 via the auxiliary terminal 300.

[0043] Furthermore, the input / output interface 160 is an interface for connecting to input / output devices and is used for inputting and outputting various types of information. In this embodiment, the input / output interface 160 is connected to the heart rate sensor 161, the voice sensor 162, and the display 163.

[0044] The heart rate sensor 161 is a sensor that measures the heart rate of a user wearing the wearable device 100. The heart rate sensor 161 may be an optical, capacitive, or electrical heart rate sensor. The heart rate measured by the heart rate sensor 161 is stored in the heart rate data 141.

[0045] The voice sensor 162 accepts voice input from the user by detecting the user's voice. The voice sensor 162 is, for example, a small microphone installed in a smartwatch.

[0046] The display 163 is a liquid crystal display or an organic EL (Electro-Luminescence) display, etc., that displays various types of information. Furthermore, the display 163 in this embodiment is equipped with a touch panel such as a resistive touch panel or a capacitive touch panel, and user instructions are input through user touch operations.

[0047] (Central Server) Figure 4 is a block diagram showing an example of the hardware configuration of the center server 200 according to the first embodiment. The center server 200 is a so-called computer. The functions of the CPU 210, ROM 220, RAM 230, storage 240, communication I / F 250, input / output I / F 260, and bus 270 of the center server 200 are the same as those of the CPU 110, ROM 120, RAM 130, storage 140, communication I / F 150, input / output I / F 160, and bus 170 of the wearable terminal 100 described above. The differences from the wearable terminal 100 described above will be explained below.

[0048] ROM220 stores data including the server program 221. The server program 221 is a program that performs various processes. For example, the server program uses the data stored in the user database 241 (described later) as training data to train the machine learning model stored in the trained model storage unit 242 (described later).

[0049] The storage 240 consists of HDDs (Hard Disk Drives), SSDs, etc., and stores data including a user database 241, a trained model storage unit 242, and a large-scale language model 243.

[0050] The user database 241 is a database that stores user information. Specifically, the user database 241 stores user information transmitted from the wearable terminal 100 and the auxiliary terminal 300. In this embodiment, the user database 241 stores various types of data stored in the storage 140 described above.

[0051] Furthermore, the user database 241 stores activities that promote at least one of alpha waves and theta waves, which are determined by measuring the user's brainwaves. Activities that promote at least one of alpha waves and theta waves include, for example, deep breathing, meditation, listening to music, and light exercise. The user database 241 stores, as an example, actions based on the user's brainwave data measured at a medical institution, at home, etc. Specifically, it stores actions that, when performed while measuring the user's brainwaves, were found to have the effect of promoting at least one of alpha waves and theta waves. In addition, the user database 241 classifies and stores the above actions according to the level of heart rate variability. For example, "slow abdominal breathing through the nose" is stored as an action when heart rate variability is "20ms or more but less than 30ms," and "close your eyes and slowly breathe abdominally through your nose" is stored as an action when heart rate variability is "less than 20ms." Hereinafter, activities that promote alpha waves will also be referred to as alpha wave promoting activities, and activities that promote theta waves will also be referred to as theta wave promoting activities. In this embodiment, an example in which alpha wave promoting activity is applied will be described, but the technology of this disclosure only requires that at least one of alpha wave promoting activity and theta wave promoting activity be applied. Alpha wave promoting activity and theta wave promoting activity are examples of "improvement behaviors". Electroencephalogram data is an example of "electroencephalogram information".

[0052] The trained model storage unit 242 stores a trained model that has been trained using data containing various types of data stored in the user database 241 as training data. The training data includes, for example, data recording user questionnaire responses stored in the response data 143.

[0053] Figure 5 is a block diagram showing an example of the configuration of the trained model storage unit 242 according to the first embodiment. The trained model storage unit 242 stores the improvement action suggestion model 242A and the rest action suggestion model 242B.

[0054] The improvement action suggestion model 242A is a pre-trained model that outputs alpha wave-promoting activities recommended for a user based on the user's heart rate variability input. The rest action suggestion model 242B is a pre-trained model that outputs rest actions recommended for a user based on an evaluation of the user's emotional state input. Rest actions include, for example, Zen meditation, consultation, and emotional input. The pre-trained models stored in the pre-trained model memory unit 242 may consist of a deep learning neural network, as well as known learning methods such as support vector machines (SVM), convolutional neural networks (CNN), and recurrent neural networks (RNN).

[0055] The large-scale language model 243 stores a large-scale language model (LLM) built on known technologies. The large-scale language model 243 outputs generated sentences when an instruction (hereinafter also referred to as a prompt) is input. In this embodiment, the large-scale language model 243 generates generated sentences by referring to data stored in the user database 241. The central server 200 may also utilize a large-scale language model owned by an external server. The large-scale language model is an example of "generative AI (Artificial Intelligence)".

[0056] (Auxiliary terminal) The auxiliary terminal 300, like the central server 200, is configured to include a CPU, ROM, RAM, storage, input / output interface, and display, etc. (not shown).

[0057] In this embodiment, the auxiliary terminal 300 receives input of a work plan from the user and transmits work plan data indicating the received work plan to the central server 200. The auxiliary terminal 300 may also transmit the work plan data to the wearable terminal 100.

[0058] (Functional configuration of wearable devices) Figure 6 is a block diagram showing an example of the functional configuration of a wearable terminal 100 according to the first embodiment. As shown in Figure 6, in this embodiment, the wearable terminal 100 functions as a heart rate variability acquisition unit 110A, a presentation unit 110B, a plan acquisition unit 110C, a setting unit 110D, an evaluation unit 110E, a performance reception unit 110F, a selection reception unit 110G, and a recording unit 110H, when the CPU 110 executes the proposed program 121.

[0059] The heart rate variability acquisition unit 110A has the function of acquiring heart rate variability. In this embodiment, the heart rate variability acquisition unit 110A calculates heart rate variability from heart rate data 141 stored in the storage 140 and acquires the calculated heart rate variability. The heart rate variability acquisition unit 110A also stores the acquired heart rate variability in heart rate variability data 142.

[0060] The presentation unit 110B has the function of presenting alpha wave promoting activities. The presentation unit 110B presents alpha wave promoting activities selected based on the heart rate variability acquired by the heart rate variability acquisition unit 110A. In this embodiment, the presentation unit 110B displays the suggested alpha wave promoting activities output by inputting the acquired heart rate variability into the improvement behavior suggestion model 242A on the display 163. The presentation unit 110B may also present alpha wave promoting activities selected on a rule basis. For example, the presentation unit 110B suggests alpha wave promoting activities that were determined to have high alpha wave values ​​when electroencephalograms were measured.

[0061] Furthermore, the presentation unit 110B has the function of presenting resting behaviors. The presentation unit 110B presents resting behaviors determined based on the evaluation of the evaluation unit 110E, which will be described later. In this embodiment, the presentation unit 110B displays the suggested resting behaviors output by inputting the evaluation of the evaluation unit 110E into the resting behavior suggestion model 242B on the display 163. The presentation unit 110B may also present resting behaviors selected on a rule basis. For example, the presentation unit 110B suggests resting behaviors classified according to the evaluation of emotional state.

[0062] Furthermore, the presentation unit 110B has a function to present the reflection content. In this embodiment, the presentation unit 110B displays the reflection content generated by inputting a predetermined prompt sentence into the large-scale language model 243, which generates answers by referring to user information stored in the user database 241, on the display 163.

[0063] The plan acquisition unit 110C has the function of acquiring work plans. In this embodiment, the plan acquisition unit 110C acquires work plan data from the central server 200. The plan acquisition unit 110C may acquire work plan data entered into the wearable terminal 100, or it may acquire work plan data entered into the auxiliary terminal 300 from the auxiliary terminal 300.

[0064] The setting unit 110D has a function to set rest timings. The setting unit 110D sets rest timings at predetermined intervals according to the work plan acquired by the plan acquisition unit 110C. The setting unit 110D also notifies the user of rest at the set timing. In this embodiment, the setting unit 110D notifies the user of rest 25 minutes after the start of work. 25 minutes after the start of work is an example of a "predetermined time".

[0065] The evaluation unit 110E has a function to evaluate the user's emotional state. In this embodiment, the evaluation unit 110E evaluates the user's emotional state based on the heart rate stored in the heart rate data 141 and the heart rate variability acquired by the heart rate variability acquisition unit 110A. Emotional states include, for example, euphoria, anger, and sadness. Emotional states are examples of "psychological states."

[0066] The performance reception unit 110F has the function of receiving performance record data. In this embodiment, the performance reception unit 110F receives performance record data through voice input from the voice sensor 162 and contact operations on the touch panel provided on the display 163. The performance reception unit 110F may also be configured to receive performance record data from the auxiliary terminal 300.

[0067] The selection receiving unit 110G accepts a selection of whether or not to perform alpha wave promotion activity. In this embodiment, the selection receiving unit 110G accepts a selection of whether or not to perform alpha wave promotion activity based on voice input from the voice sensor 162 and contact operations on the touch panel of the display 163.

[0068] The recording unit 110H has a function to record the user's responses. In this embodiment, the recording unit 110H stores the data of the questionnaire answered by the user in the response data 143.

[0069] <effect> Next, we will explain the process of suggesting rest to the user based on heart rate variability, using Figures 7 to 11.

[0070] (Rest suggestion processing) Figure 7 is a flowchart showing an example of the rest suggestion process according to the first embodiment. In this embodiment, the rest suggestion process is performed by the CPU 110 of the wearable terminal 100 reading the suggestion program 121 from the ROM 120 or storage 140, loading it into the RAM 130, and executing it. The rest suggestion process is a process that is repeatedly executed until a predetermined work time defined in the work plan has elapsed. The rest suggestion process proposes improvement actions during short rests and proposes rest actions during long rests. Here, a short rest is a rest of about 1 to 10 minutes set between tasks. A long rest is a rest of about 10 to 25 minutes set after multiple short rests have been set.

[0071] In step S100 of Figure 7, the CPU 110 acquires the work plan. Specifically, the CPU 110 acquires work plan data indicating the work plan from the central server 200.

[0072] In step S101, the CPU 110 sets the timing for a short rest. The CPU 110 sets a schedule to take a short rest of a predetermined amount of time (for example, 5 minutes) when 25 minutes of work have elapsed, according to the work plan data acquired in step S100.

[0073] In step S102, the CPU 110 notifies the user to take a short rest at the set timing. The CPU 110 notifies the user to take a short rest at the timing set in step S101. For example, the CPU 110 displays a message on the display 163 such as, "Let's breathe slowly using diaphragmatic breathing through our nose."

[0074] In step S103, the CPU 110 acquires heart rate variability. The CPU 110 acquires heart rate variability from the heart rate measured during a predetermined period (for example, 1 minute) from the time when a short rest notification was given in step S102.

[0075] In step S104, the CPU 110 determines whether the heart rate variability is good or not. If the CPU 110 determines that the heart rate variability is good (step S104: YES), it proceeds to step S106. On the other hand, if the CPU 110 determines that the heart rate variability is not good (step S104: NO), it proceeds to step S105. If the CPU 110 determines that the heart rate variability is good, it displays the message "Good! Feel free to relax." on the display 163. Here, for example, the CPU 110 determines that the heart rate variability is good if it is 40ms or more, and not good if it is less than 40ms.

[0076] In step S105, the CPU 110 executes the improvement action suggestion process described later.

[0077] In step S106, the CPU 110 accepts the questionnaire. Specifically, the CPU 110 accepts the questionnaire after a predetermined time set in step S101 has elapsed. The CPU 110 receives the user's responses to questionnaires, such as an evaluation of the alpha wave-promoting activities performed and actions taken during short rest periods, and stores them in the response data 143.

[0078] In step S107, the CPU 110 receives the performance record. Specifically, the CPU 110 receives the user's progress on the work plan via voice from the voice sensor 162 and receives the user's emotional state displayed on the display 163 via the touch panel. The CPU 110 receives the performance record by, for example, displaying the screen shown in Figure 8A (described later) on the display 163. Alternatively, the CPU 110 may display the screen shown in Figure 8B (described later) on the display of the auxiliary terminal 300 and receive the performance record data from the auxiliary terminal 300. Similarly, the information received by the wearable terminal 100 may be received from the auxiliary terminal 300.

[0079] In step S108, the CPU 110 determines whether the number of short rests has reached a predetermined number. For example, the CPU 110 determines whether the number of short rests has reached four. If the CPU 110 determines that the number of short rests has reached the predetermined number (step S108: YES), it proceeds to step S109. On the other hand, if the CPU 110 determines that the number of short rests has not reached the predetermined number (step S108: NO), it returns to step S102.

[0080] In step S109, the CPU 110 issues a notification for a long rest. Specifically, the CPU 110 issues a notification for a long rest after a predetermined amount of work time has elapsed. For example, after a predetermined number of short breaks have been taken and a predetermined amount of work time has elapsed, the CPU 110 displays the message "Take a long rest" on the display 163.

[0081] (display screen) Figure 8A is an example of the display screen of the wearable terminal 100 according to the first embodiment. As shown in Figure 8A, the display 163 of the wearable terminal 100 displays a notification area G10, an emotion selection area G11, an emotion intensity specification area G12, and a recording button G13. The entire display screen of the display 163 is displayed with a light red background color.

[0082] The notification area G10 displays the instructions. For example, the notification area G10 displays the message, "Please record your achievements."

[0083] The emotion selection area G11 displays icons that allow users to select an emotion. Users can select the icon that best represents their current emotion by tapping on the displayed icons. In this embodiment, five levels of emotional states are displayed, ranging from icons representing positive emotions to icons representing negative emotions.

[0084] The emotion level specification area G12 displays a slider bar that allows the user to specify the degree of emotion numerically. The user can specify the number that best represents their emotional level by sliding the displayed slider bar. In this embodiment, the user can specify a number in the range of 1 to 100.

[0085] The recording button G13 is used to start recording. Users can record audio by tapping the displayed recording button. After the recording button G13 is pressed, it may be changed to a stop button to stop recording.

[0086] (modified version) Figure 8B shows an example of the display screen of the auxiliary terminal 300 according to a modified version of the first embodiment. In this modified version, performance records entered from the auxiliary terminal 300 are received. As shown in Figure 8B, the display of the auxiliary terminal 300 shows an emotion selection area G11, an emotion intensity specification area G12, a recording button G13, a notification area G20, a heart rate variability display area G21, and a text input area G22. Note that the emotion selection area G11, the emotion intensity specification area G12, and the recording button G13 are the same as in Figure 8A, so a detailed explanation is omitted.

[0087] In the notification area G20 of Figure 8B, the content of the instruction is displayed. For example, the message "Take a break" is displayed in the notification area G20, superimposed on a red background.

[0088] The heart rate variability display area G21 shows the measured value of the user's heart rate variability, along with a circular outline around the heart rate variability. As shown in Figure 8B, the heart rate variability display area G21 shows "19" as the value of the heart rate variability, and a red circular outline is displayed around "19".

[0089] The text input area G22 is an area where text can be entered. Users can enter text from a text input screen that appears when they tap the text input area G22. The entered text may also be displayed in the text input area G22. In addition, the text input area G22 may be used to input text that is a transcription of the user's recorded voice.

[0090] As shown above, the background color of the notification area G20 is displayed in red, and the circular outline of the heart rate variability display area G21 is displayed in red, indicating that a heart rate variability value of "19" is a value that requires attention. In this embodiment, a heart rate variability value of "19" is evaluated as "quite unwell." Furthermore, the background color of the notification area G20 and the color used for the circular outline of the heart rate variability display area G21 may be changed depending on the heart rate variability value or the notification message. Examples of colors that can be used include blue, yellow, green, and white.

[0091] In step S110 of Figure 7, the CPU 110 executes the rest action suggestion process described later. Then, the CPU 110 returns to step S101. Note that the CPU 110 may terminate the rest suggestion process when a predetermined work time has elapsed and proceed to the reflection process described later (see Figure 13).

[0092] (Processing of improvement action proposals) Next, we will describe the improvement action suggestion process, which proposes improvement actions to improve the user's heart rate variability during short rest periods. Figure 9 is a flowchart showing an example of the improvement action suggestion process according to the first embodiment. The improvement action suggestion process in this embodiment is executed in step S105 (see Figure 7).

[0093] In step S200 of Figure 9, the CPU 110 proposes alpha wave promoting activities based on the electroencephalogram (EEG). Specifically, the CPU 110 inputs the heart rate variability acquired in step S103 (see Figure 7) into the improvement action suggestion model 242A stored in the learned model memory unit 242 of the center server 200. The CPU 110 then acquires the alpha wave promoting activities output from the improvement action suggestion model 242A. Finally, the CPU 110 displays the acquired alpha wave promoting activity suggestions on the display 163.

[0094] In step S201, the CPU 110 accepts a selection of whether or not to perform alpha wave promotion activities. The CPU 110 displays, for example, the screens shown in Figures 10(A) and 10(B) described later on the display 163. If the CPU 110 accepts a selection to perform alpha wave promotion activities (step S201: perform), it proceeds to step S202. On the other hand, if the CPU 110 accepts a selection not to perform alpha wave promotion activities (step S201: do not perform), it terminates the improvement action suggestion process and proceeds to step S106 (see Figure 7).

[0095] (display screen) Figure 10(A) shows an example of the display screen that appears when the measured heart rate variability is "20ms or more and less than 30ms". The display 163 of the wearable device 100 shows the notification area G30 and the selection area G31. The entire display screen of the display 163 is shown with a yellow background.

[0096] The G30 notification area displays messages suggesting activities to promote alpha waves. For example, the G30 notification area displays the message, "Let's breathe slowly using diaphragmatic breathing through our nose."

[0097] The selection area G31 displays an "Execute" button G32 for selecting to perform alpha wave promotion activities and a "Skip" button G33 for selecting not to perform alpha wave promotion activities. The word "Breathing" is displayed on the "Execute" button G32, and the user can select to perform alpha wave promotion activities by tapping the "Execute" button G32. The word "Skip" is displayed on the "Skip" button G33, and the user can select not to perform alpha wave promotion activities by tapping the "Skip" button G33.

[0098] Figure 10(B) shows an example of the display screen that appears when the measured heart rate variability is "less than 20ms". The differences from Figure 10(A) are explained below. The display screen of display 163 in Figure 10(B) is displayed with a light red background color throughout.

[0099] In the notification area of ​​the G30, one example message is displayed: "Close your eyes and breathe slowly using diaphragmatic breathing through your nose."

[0100] As shown in Figures 10(A) and 10(B), the suggested alpha wave facilitation activity is changed and displayed according to the measured heart rate variability. The background color is also changed according to the measured heart rate variability. By changing the background color according to the measured heart rate variability, the need for rest can be conveyed to the user, encouraging them to rest. The background color used for the display screen of display 163 may be changed according to the heart rate variability or the message being notified. Examples of colors used include light green, orange, light blue, and white.

[0101] In step S202 of Figure 9, the CPU 110 acquires heart rate variability after a predetermined time has elapsed. Specifically, the CPU 110 acquires heart rate variability after a time set for each type of alpha wave facilitation activity has elapsed. For example, the CPU 110 acquires heart rate variability after 1 minute has elapsed since starting nasal abdominal breathing.

[0102] In step S203, the CPU 110 determines whether the heart rate variability is good or not. If the CPU 110 determines that the heart rate variability is good (step S203: YES), it terminates the improvement action suggestion process and proceeds to step S106 (see Figure 7). On the other hand, if the CPU 110 determines that the heart rate variability is not good (step S203: NO), it proceeds to step S204. If the CPU 110 determines that the heart rate variability is good, it displays the message "It's good! Enjoy your free time." on the display 163.

[0103] In step S204, the CPU 110 determines whether the number of proposed alpha wave promoting activities has reached a predetermined number. The CPU 110 determines whether the number of times set for each type of alpha wave promoting activity (for example, 3 times) has been reached. If the CPU 110 determines that the number of proposed alpha wave promoting activities has reached the predetermined number (step S204: YES), it terminates the improvement action proposal process and proceeds to step S106 (see Figure 7). On the other hand, if the CPU 110 determines that the number of proposed alpha wave promoting activities has not reached the predetermined number (step S204: NO), it returns to step S200. The predetermined number may be set based on past data, expert knowledge, etc.

[0104] (Processing of suggested resting behavior) Next, we will describe the rest behavior suggestion process that proposes rest behaviors in accordance with the user's heart rate variability during long rest periods. Figure 11 is a flowchart showing an example of the rest behavior suggestion process according to the first embodiment. The rest behavior suggestion process in this embodiment is executed in step S110 (see Figure 7).

[0105] In step S300 of Figure 11, the CPU 110 acquires heart rate and heart rate variability. Specifically, the CPU 110 acquires the most recent heart rate from the heart rate data 141 stored in the storage 140 and the most recent heart rate variability from the heart rate variability data 142. Alternatively, the CPU 110 may measure the heart rate for a predetermined period of time and acquire heart rate variability from the measured heart rate. The acquired heart rate and heart rate variability are examples of "measured values".

[0106] In step S301, the CPU 110 evaluates the emotional state based on heart rate and heart rate variability. Here, the emotional state indicates the emotional tendencies associated with the physical state (e.g., balance of the autonomic nervous system). Specifically, the CPU 110 evaluates the user's emotional state based on the heart rate and heart rate variability acquired in step S300.

[0107] In this embodiment, the user's emotional state is evaluated based on the values ​​of heart rate and heart rate variability. For example, if the user's average monthly heart rate variability is 50 ms or more, it is determined as shown in Table 2 below.

[0108] [Table 2]

[0109] For example, if the user's heart rate is "95" and the heart rate variability is "28", the CPU110 will assess the user's emotional state as "sad."

[0110] In step S302, the CPU 110 proposes resting behaviors based on the evaluation results. Specifically, the CPU 110 inputs the evaluation results from step S301 into the resting behavior proposal model 242B stored in the trained model memory unit 242 of the center server 200. The CPU 110 then retrieves the resting behaviors output from the resting behavior proposal model 242B. The CPU 110 then displays the retrieved resting behavior proposals on the display 163. For example, the CPU 110 displays the screens shown in Figures 12(A) to 12(D), which will be described later, on the display 163.

[0111] (display screen) Figures 12(A) to 12(D) are examples of display screens shown on the display 163 of the wearable device 100 based on the evaluation results.

[0112] Figure 12(A) shows an example of a display screen that appears when the emotional state is assessed as "good." The background color of the display screen on display 163 is light green. Display 163 also displays the message, "Good! Take a 10-minute break." This demonstrates that the system is suggesting that the user take a break at their own discretion.

[0113] Figure 12(B) shows an example of a display screen that appears when the emotional state is assessed as "exhilaration, excitement, and high tension." The background color of the display screen on display 163 is orange. Display 163 also displays the message, "Let's meditate for 10 minutes." This demonstrates that the system suggests a restful activity to help the user calm down.

[0114] Figure 12(C) shows an example of a display screen that appears when the emotional state is assessed as "resentment, anger, or rage." The background color of the display screen on display 163 is light red. Display 163 also displays the message, "Let's sit in meditation for 25 minutes." This demonstrates that the system suggests a restful activity that will help the user calm down.

[0115] Figure 12(D) shows an example of a display screen that appears when the emotional state is assessed as "sadness, disappointment, loneliness, and disillusionment." The background color of the display screen on display 163 is light blue. Display 163 also displays the message "Let's record your emotions" and a recording button G13. This demonstrates that the system suggests actions that allow the user to express their emotions.

[0116] As shown in Figures 12(A) to 12(C), by displaying different messages depending on the evaluation results, it is possible to suggest resting behaviors that are in line with the user's emotional state. Alternatively, as shown in Figure 12(D), the system may ask the user to express their emotions instead of resting.

[0117] In step S303 of Figure 11, the CPU 110 receives the questionnaire. The CPU 110 receives the user's responses to the questionnaire, such as their evaluation of the resting behavior they performed and their behavior during long rest periods, and stores them in the response data 143. Then, the CPU 110 finishes the resting behavior suggestion process and returns to step S102 (see Figure 7).

[0118] (Review process) Next, we will describe the review process that accepts daily reflections when a predetermined amount of work time has elapsed. Figure 13 is a flowchart showing an example of the flow of the review process according to the first embodiment.

[0119] In step S400 of Figure 13, the CPU 110 displays the heart rate variability and emotion history. Specifically, the CPU 110 displays the heart rate variability stored in the heart rate variability data 142 and the user's emotion history recorded in the performance record data 145 on the display 163. For example, the CPU 110 displays the screen shown in Figure 14, which will be described later, on the display 163.

[0120] Figure 14 shows an example of the display screen of the wearable terminal 100 according to the first embodiment. As shown in Figure 14, a time-series graph of heart rate variability G40 and a plurality of emotion icons G41 are displayed.

[0121] Time-series graph G40 is a graph that connects the heart rate variability values ​​stored in heart rate variability data 142 along a time-series timeline.

[0122] The emotion icon G41 is an icon that indicates the state of emotion. In this embodiment, the emotion icon G41, which indicates the user's emotional state at the time the performance record was recorded, is displayed along the trajectory of the time-series graph G40.

[0123] In this way, the time-series graph G40 and multiple emotion icons G41 are displayed, allowing the user to see at a glance the changes in heart rate variability and emotional state throughout the day. Furthermore, the user can observe the relationship between changes in heart rate variability and changes in emotional state, and use this to improve their own emotional state.

[0124] In step S401 of Figure 13, the CPU 110 obtains the reflection content generated based on the user's information. Specifically, the CPU 110 obtains the reflection content generated by inputting a prompt sentence into the large-scale language model 243. For example, the CPU 110 obtains the generated text by inputting a prompt sentence such as, "Please infer the reflection content (gratitude, praise, improvement) based on the work plan, measurement results, performance records, emotion input, etc." into the large-scale language model 243. Note that the prompt sentence does not necessarily have to include the work plan.

[0125] In step S402, the CPU 110 displays an input field containing the acquired reflection content. Specifically, the CPU 110 displays an input field on the display 163 containing the reflection content acquired in step S401. In this embodiment, the reflection content consists of three items: "Things to be grateful for," "Things to praise oneself for," and "Things to improve." However, the reflection content is not limited to the above items. The user can also edit the contents of the input field.

[0126] In step S403, the CPU 110 receives the reflection content. Specifically, the CPU 110 receives the reflection content entered in the input field displayed in step S402. Then, the CPU 110 terminates the reflection process.

[0127] (Summary of the first embodiment) The wearable terminal 100 of the first embodiment presents alpha wave promoting activities based on acquired heart rate variability. Therefore, the wearable terminal 100 of this embodiment can improve the user's physical condition by prompting actions that are appropriate to the user's state. In other words, it can improve irregular breathing caused by sympathetic nervous system dominance due to over-concentration, etc., and regulate the user's physical condition (for example, the balance of the autonomic nervous system).

[0128] The wearable terminal 100 of the first embodiment acquires heart rate variability measured at rest intervals set at predetermined time intervals according to the acquired work plan. Therefore, the wearable terminal 100 of this embodiment can encourage the user to take planned rests and improve the user's physical condition before the user feels unwell.

[0129] The wearable terminal 100 of the first embodiment presents resting behaviors based on an evaluation of the user's emotional state in response to heart rate and heart rate variability. Therefore, the wearable terminal 100 of this embodiment can effectively improve the user's physical condition.

[0130] The wearable terminal 100 of the first embodiment presents rest behaviors output from a rest behavior suggestion model 242B that has been trained using training data including user questionnaire responses stored in response data 143. Therefore, the wearable terminal 100 of this embodiment can improve the accuracy of rest behavior suggestions.

[0131] In the first embodiment, the wearable terminal 100 presents the reflection content generated by the large-scale language model 243 when it receives the user's reflection content, based on prompt text including work plans, measurement results, performance records, and emotion input. Therefore, with the wearable terminal 100 of this embodiment, the user can understand the state of their own body from an objective perspective, and it is possible to encourage improvements in the user's physical state, thought patterns, and behavioral patterns.

[0132] In the first embodiment, the wearable terminal 100 presents alpha wave promoting activities output from an improvement action suggestion model 242A, which has been trained using training data including user questionnaire responses stored in response data 143. Therefore, the wearable terminal 100 of this embodiment can improve the accuracy of suggesting alpha wave promoting activities.

[0133] The wearable terminal 100 of the first embodiment accepts the user's choice whether or not to perform an alpha wave promoting activity when it presents such an activity. Therefore, according to the wearable terminal 100 of this embodiment, if the presented alpha wave promoting activity does not suit the user's situation or preferences, the user can choose not to perform the presented alpha wave promoting activity.

[0134] In the first embodiment, if the heart rate variability is not judged to be satisfactory as a result of the presented alpha wave promoting activity, the wearable terminal 100 continues to present the alpha wave promoting activity until a predetermined number of times set for each activity is reached. Therefore, according to the wearable terminal 100 of this embodiment, it is possible to effectively implement the alpha wave promoting activity while preventing it from being continued indefinitely, thereby preventing pressure on the user's work time.

[0135] The wearable terminal 100 of the first embodiment presents alpha wave promoting activities whose alpha wave measurement results are higher than other alpha wave promoting activities, which are determined by measuring the user's brainwaves while performing each alpha wave promoting activity in advance. Therefore, the wearable terminal 100 of this embodiment can suggest alpha wave promoting activities that are effective for the user.

[0136] [Second Embodiment] In the second embodiment, the wearable terminal 100 evaluates the user's emotional state using past records when suggesting resting behavior. Furthermore, the wearable terminal 100 provides coaching according to the user's emotional state. The differences from the first embodiment are described below.

[0137] The presentation unit 110B has the function of presenting questions. The presentation unit 110B presents questions that correspond to the user's emotional state.

[0138] Furthermore, the presentation unit 110B has a function to present the psychological state. In this embodiment, the presentation unit 110B displays the reflection content generated by inputting predetermined prompt sentences into a large-scale language model 243 that generates answers by referring to user information stored in the user database 241 on the display 163.

[0139] The evaluation unit 110E evaluates the user's emotional state based on heart rate and heart rate variability, as well as performance records. In this embodiment, the evaluation unit 110E estimates the user's emotional state from the voice, text, and degree of emotion received by the performance reception unit 110F. Known techniques may be used to estimate the emotional state from the voice and text.

[0140] The recording unit 110H has the function of recording the answer to a question. In this embodiment, the recording unit 110H stores the user's answer to the question presented by the presentation unit 110B in the answer data 143.

[0141] (Processing of suggested resting behavior) Figure 15 shows an example of the rest action suggestion process according to the second embodiment. The rest action suggestion process in this embodiment is the process executed in step S110 (see Figure 7).

[0142] In step S500 of Figure 15, the CPU 110 estimates the user's emotions from the performance records. Specifically, the CPU 110 estimates the user's emotions from the performance records stored in the performance record data 145.

[0143] In step S501, the CPU 110 acquires the heart rate and heart rate variability. Step S501 is the same process as step S300 (see Figure 11), so a detailed explanation is omitted.

[0144] In step S502, the CPU 110 evaluates the user's emotional state based on their emotions, heart rate, and heart rate variability. Specifically, the CPU 110 evaluates whether the user's emotions estimated in step S500 match the emotional state based on heart rate and heart rate variability. For example, if the estimated user's emotions match the emotional state based on heart rate and heart rate variability, the CPU 110 evaluates the matching emotions as the user's true emotions. If the estimated user's emotions do not match the emotional state based on heart rate and heart rate variability, the CPU 110 evaluates that the user may be experiencing cognitive distortion. If the CPU 110 evaluates that the user may be experiencing cognitive distortion, it may perform the same processing as when the emotional state is evaluated as "anger." This helps prevent the user from experiencing cognitive distortion, failing to recognize stress, and potentially leading to overwork or burnout.

[0145] In step S503, the CPU 110 determines whether the user's emotional intensity is above a threshold. Specifically, the CPU 110 determines whether the most recent emotional intensity of the user recorded in the performance record data 145 is above a threshold (for example, 70 or higher). If the CPU 110 determines that the user's emotional intensity is above the threshold (step S503: YES), it proceeds to step S504. On the other hand, if the CPU 110 determines that the user's emotional intensity is not above the threshold (step S503: NO), it proceeds to step S505.

[0146] In step S504, the CPU 110 performs the query processing described later. Then, the CPU 110 proceeds to step S505.

[0147] In step S505, the CPU 110 proposes resting behaviors based on the evaluation results. For example, the CPU 110 proposes resting behaviors as shown in Table 3 below.

[0148] [Table 3]

[0149] For example, if CPU110 assesses the user's emotional state as "anger" and the intensity of that emotion is "65", it will display the message "Take a deep breath."

[0150] In step S506, the CPU 110 accepts the questionnaire. Step S506 is the same process as step S303 (see Figure 11), so a detailed explanation is omitted. Then, the CPU 110 finishes the rest behavior suggestion process and returns to step S102 (see Figure 7).

[0151] Next, we will describe a questioning process that provides simple coaching to the user. Figure 16 is a flowchart showing an example of the flow of the questioning process according to the second embodiment. The questioning process is, for example, the process executed in step S504 (see Figure 15). Note that the questioning process may be executed at the user's selection.

[0152] In step S600 of Figure 16, the CPU 110 displays a question corresponding to the emotional state. Specifically, the CPU 110 displays a question on the display 163 that corresponds to the emotional state evaluated in step S502. For example, the CPU 110 displays a question like those shown in Table 4 below. In Table 4, a circle indicates that a question should be displayed, and a triangle indicates that a question may be displayed.

[0153] [Table 4]

[0154] For example, if the CPU110 assesses the user's emotional state as "sad," it will ask questions such as "What happened?" and "What were you thinking?", and if the emotional intensity is 90 or higher, it will ask "What actions did you take?".

[0155] In step S601, the CPU 110 records the response to the question. For example, the CPU 110 displays a recording button G13 on the display 163 and records the user's voice response.

[0156] In step S602, the CPU 110 obtains the user's psychological state, which is generated based on the user's information and the user's responses. Specifically, the CPU 110 obtains the psychological state by inputting a prompt statement into the large-scale language model 243. For example, the CPU 110 obtains a generated statement by inputting the prompt statement, "Please infer your current mental state based on your work plan, measurement results, performance records, emotion input, and responses to inquiries," into the large-scale language model 243. The prompt statement only needs to include at least the measurement results and the responses to inquiries.

[0157] In step S603, the CPU 110 displays the acquired psychological state. Specifically, the CPU 110 displays the generated sentence acquired in step S602 on the display 163. Then, the CPU 110 terminates the questioning process.

[0158] (Summary of the second embodiment) The wearable terminal 100 of the second embodiment evaluates the user's emotional state from the performance records stored in the performance record data 145, in addition to the user's heart rate and heart rate variability. Therefore, the wearable terminal 100 of this embodiment can capture the user's emotional state more accurately.

[0159] The wearable terminal 100 of the second embodiment presents the user's mental state, generated by the large-scale language model 243, by inputting prompt sentences including work plans, measurement results, performance records, emotion input, and answers to inquiries. Therefore, with the wearable terminal 100 of this embodiment, the user can objectively understand their own state and is able to improve their physical state, thought patterns, and behavioral patterns.

[0160] [Other embodiments] In the above embodiment, the case in which the center server 200 is equipped with a trained model and a large language model has been described, but it is not limited to this. The trained model and the large language model may be equipped in the wearable terminal 100 or the auxiliary terminal 300. For example, if the wearable terminal 100 is equipped with a trained model and a large language model, it does not need to communicate when performing the various processes described above, and it can function even if a communication environment is not available. In addition, instead of being equipped with a large language model, the wearable terminal 100 or the auxiliary terminal 300 may be equipped with a small language model (SLM) that has been trained by distilling the large language model 243.

[0161] The above embodiment described a case where the user checks the reflection content. However, it is not limited to this, and the user's supporter (e.g., counselor, coach, etc.) may also be able to check the user's reflection content, performance record data, etc. Supporters can view each user's reflection content, performance record data, etc. by communicating with the center server 200 from their own terminal. Therefore, with the rest suggestion system 10 of this embodiment, supporters can effectively support users.

[0162] Furthermore, the wearable device 100 may be configured to notify a caregiver if the user's physical condition is poor. For example, the wearable device 100 may notify a caregiver if the user's physical condition is assessed as "quite poor." Therefore, according to the wearable device 100 of this embodiment, a caregiver can be notified when the user needs assistance, enabling the user to receive assistance at an appropriate time.

[0163] In the above embodiment, the wearable terminal 100 set rest times according to the acquired work plan. However, it is not limited to this, and the wearable terminal 100 may set rest times at predetermined intervals from the start of work. Therefore, with the wearable terminal 100 of this embodiment, the user's physical condition can be monitored periodically, and the user's physical condition can be improved before the user feels unwell.

[0164] In the above embodiment, the wearable terminal 100 suggested a long rest, such as "25 minutes of Zen meditation," when the emotional state was evaluated as "rejection, anger, or rage," or when the emotional intensity was high. However, the wearable terminal 100 is not limited to this, and may, for example, suggest a rest activity with adjusted duration, such as "5 minutes of Zen meditation followed by 5 minutes of free time" or "10 minutes of Zen meditation," in accordance with the user's needs, so as not to disrupt the work schedule. With the wearable terminal 100 of this embodiment, since rest can be taken without disrupting the work schedule, the psychological burden on the user to adhere to the work schedule can be reduced.

[0165] In the above embodiment, the wearable terminal 100 accepted a selection of whether or not to perform an alpha wave promotion activity in step S201 (see Figure 9) of the improvement action suggestion process. However, the wearable terminal 100 of this embodiment may accept an alpha wave promotion activity selected from among multiple alpha wave promotion activities when accepting the implementation of an alpha wave promotion activity in step S201. Multiple alpha wave promotion activities are, for example, the actions shown in Table 3 above. The wearable terminal 100 may also present the alpha wave promotion activities in order from the one most suitable for the user. For example, the wearable terminal 100 first proposes an action that is set to maximize at least one of the user's alpha waves and theta waves. The proposed alpha wave promotion activities may also be displayed as a list on the auxiliary terminal 300. When the proposed alpha wave promotion activities are displayed as a list on the auxiliary terminal 300, they may be displayed from the top in order from the one most suitable for the user, or the most suitable alpha wave promotion activity may be highlighted. According to the wearable terminal 100 of this embodiment, the user can select an alpha wave promoting activity that suits the user's situation.

[0166] Furthermore, the configurations of the rest suggestion system 10, wearable terminal 100, central server 200, and auxiliary terminal 300 described in the above embodiment are examples and may be modified as needed without departing from the main purpose.

[0167] Furthermore, the program processing flow described in the above embodiment is just one example, and unnecessary steps may be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, in the above embodiment, each process that the CPU reads and executes the software (program) may be executed by various processors other than the CPU. Examples of processors in this case include PLDs (Programmable Logic Devices) such as FPGAs (Field-Programmable Gate Arrays) whose circuit configuration can be changed after manufacturing, and dedicated electrical circuits such as ASICs (Application Specific Integrated Circuits) which have a circuit configuration specifically designed to execute a particular process.

[0169] Furthermore, the operation of the processor in the above embodiment may not be performed by a single processor, but may be performed by multiple processors located in physically separate locations working together. Also, the order of the processor operations is not limited to the order described in the above embodiment, but may be changed as appropriate.

[0170] Furthermore, although the above embodiment describes a configuration in which the information processing program is pre-stored (installed) in ROM, the invention is not limited to this. The program may be provided in the form of a recording medium such as a CD-ROM (Compact Disc Read Only Memory), DVD-ROM (Digital Versatile Disc Read Only Memory), and USB (Universal Serial Bus) memory. Alternatively, the program may be provided in the form of a download from an external device via a network. This disclosure is also applicable to programs and program products. [Explanation of Symbols]

[0171] 10. Rest Suggestion System 100 wearable devices 110A Heart rate variability acquisition unit 110B Presentation section 110C Planning and Acquisition Department 110D Setting section 110E Evaluation Department 110F Performance Record Reception Department 110G Selection Reception Section 110H Recording Section 121 Proposed Program 140 storage 141 Heart rate data 142 Heart rate variability data 143 response data 144 Work Plan Data 145 Performance Record Data 161 Heart rate sensor 162 Voice Sensor 163 displays 200 Center Servers 221 Server Program 240 storage 241 User Database 242 Model Memory Unit 242A Improvement Action Proposal Model 242B Rest Behavior Proposal Model 243 Large-scale language models 300 auxiliary terminals

Claims

1. A heart rate variability acquisition unit that acquires the user's heart rate variability, A presentation unit that presents improvement actions to improve the acquired heart rate variability, An information processing device equipped with the following features.

2. It is equipped with a setting unit for setting rest timings at predetermined intervals, The heart rate variability acquisition unit acquires heart rate variability measured at the set rest timing. The information processing apparatus according to claim 1.

3. The system includes a planning unit that acquires a work plan which includes at least information regarding the time required for the work entered by the user. The setting unit sets the rest timing according to the acquired work plan. The information processing apparatus according to claim 2.

4. The system includes an evaluation unit that evaluates the user's psychological state after a predetermined number of rest periods have elapsed, based on measured values ​​that include at least one of the user's heart rate measured by a heart rate sensor and the user's acquired heart rate variability. The presentation unit presents resting actions to allow the user to rest, based on the evaluation by the evaluation unit. The information processing apparatus according to claim 2.

5. The display unit, when presenting the resting behavior, presents the resting behavior output from a trained model that has been trained to output the resting behavior by inputting the evaluation using training data including feedback information from the user. The information processing apparatus according to claim 4.

6. The system includes a performance reception unit that receives records of the work performed by the user from the user. The evaluation unit evaluates the user's emotions based on the measured values ​​and the performance records received by the performance reception unit. The information processing apparatus according to claim 4.

7. The aforementioned display unit is, The reflection content generated by inputting a prompt message instructing the AI ​​to estimate the reflection content based on the measured values, the performance records, and the user's emotions is presented when the reflection content for the work is received. The information processing apparatus according to claim 6.

8. The system includes a recording unit that records the user's responses to predetermined questions based on the user's psychological state. The display unit presents the user's mental state, which is generated by inputting a prompt message into the generating AI that instructs it to estimate the user's mental state based on the measured values ​​and the recorded answers. The information processing apparatus according to claim 4.

9. When presenting the improvement behavior, the presenting unit presents the improvement behavior output from a trained model that has been trained to output the improvement behavior by inputting the heart rate variability using training data including feedback information from the user. The information processing apparatus according to claim 1.

10. When presenting the aforementioned improvement action, the system includes a selection receiving unit that receives a selection from the user whether or not to perform the improvement action. The information processing apparatus according to claim 1.

11. The presentation unit presents a plurality of improvement actions when presenting the improvement actions. The selection receiving unit, when receiving a selection to implement the improvement action, receives the improvement action selected from among the multiple improvement actions. The information processing apparatus according to claim 10.

12. If the improvement action does not improve the heart rate variability, the display unit will display the improvement action until a predetermined number of times set for each improvement action is reached. The information processing apparatus according to claim 1.

13. The display unit presents the user's improvement behavior, which is set based on the measured brainwave information of the user. The information processing apparatus according to claim 1.

14. The user's heart rate variability is obtained, Based on the acquired heart rate variability, the system presents corrective actions to improve the heart rate variability. An information processing method in which a computer performs the processing.

15. The user's heart rate variability is obtained, Based on the acquired heart rate variability, the system presents corrective actions to improve the heart rate variability. An information processing program that causes a computer to perform a task.

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