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

Abstract

This information processing method is executed by a computer and comprises: acquiring measurement data of a wearable device for measuring a user's HR, HRV, and sleep; and extracting a representative HRV for a day from the measurement data, the representative HRV being an HRV measured during the user's sleep and corresponding to a time when the user's sleep phase is a designated sleep phase.

Description

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

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

[0002] Various techniques have been proposed that focus on the relationship between sleep and heart rate variability (HRV). For example, Patent Literature 1 discloses a technique for visualizing sleep phases and HRV by overlapping them.

[0003] Japanese Patent Application Laid-Open No. 2020-168214

[0004] HRV can also be related to stress. In recent years, it has become easy to measure HR (heart rate), HRV, sleep phases, etc. using wearable devices. If it were possible to understand a user's stress from the HRV measured by a wearable device, this would be a useful technology.

[0005] One aspect of the present disclosure is to grasp the stress of a user.

[0006] An information processing method according to one aspect of the present disclosure is an information processing method executed by a computer, and includes acquiring measurement data from a wearable device that measures a user's HR, HRV, and sleep, and extracting a representative HRV for one day from the measurement data, wherein the representative HRV is an HRV measured while the user is sleeping and is an HRV when the user's sleep phase is a specified sleep phase.

[0007] An information processing device according to one aspect of the present disclosure includes an acquisition unit that acquires measurement data from a wearable device that measures a user's HR, HRV, and sleep, and an extraction unit that extracts a representative HRV for one day from the measurement data, where the representative HRV is an HRV measured while the user is sleeping and is an HRV when the user's sleep phase is a specified sleep phase.

[0008] An information processing system according to one aspect of the present disclosure includes a wearable device that measures a user's HR, HRV, and sleep, an information processing device that extracts a representative HRV for one day from the measurement data of the wearable device, and a terminal device that presents the user with stress information generated based on the representative HRV to the user, where the representative HRV is an HRV measured while the user is asleep and is the HRV when the user's sleep phase is a specified sleep phase.

[0009] 1 is a diagram showing an example of a schematic configuration of an information processing system 100 according to an embodiment. FIG. 1 is a diagram showing examples of an HR history 401, an HRV history 402, and a sleep history 403. FIG. 1 is a diagram showing an example of extraction of a representative HRV. FIG. 2 is a diagram showing an example of extraction of a representative HRV. FIG. 3 is a diagram showing an example of a representative HRV transition 404. FIG. 4 is a diagram showing an example of generation of stress information. FIG. 5 is a diagram showing an example of threshold determination for a ratio r. FIG. 6 is a diagram showing a specific example of stress information. FIG. 7 is a diagram showing an example of presentation of stress information. FIG. 8 is a diagram showing an example of presentation of stress information. FIG. 9 is a flowchart showing an example of processing (information processing method) executed in the information processing system 100. FIG. 10 is a flowchart showing an example of processing (information processing method) executed in the information processing system 100. FIG. 11 is a diagram showing an example of hardware configuration of a device, etc. FIG. 12 is a diagram showing an example of functional blocks of an information processing system 100 according to an application example.

[0010] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the following embodiments, the same elements are designated by the same reference numerals, and redundant description will be omitted.

[0011] The present disclosure will be described in the following order: 0. Introduction 1. Embodiment 2. Modification 3. Example of Hardware Configuration 4. Application Example 5. Conclusion

[0012] 0. Introduction It is said that there is a correlation between worsening (increasing) stress and a decrease in HRV. Measuring HRV may enable us to understand stress. Previously, measuring HRV required the use of an electrocardiograph or other device, but in recent years, it has become easier to do so using wearable devices. It is also possible to measure sleep phases.

[0013] On the other hand, wearable devices that use non-contact sensors, for example, have the problem that measurements are not always accurate (measurement timing cannot be freely controlled) due to factors such as measurement algorithms. There is also the problem that absolute value evaluation is difficult due to individual differences in HRV. Even for the same person, HRV fluctuates throughout the day. For example, HRV may be higher at night or vary depending on the sleep phase (sleep stage). It has also been reported that HRV reaches its lowest point at a certain time in the early morning, and that this does not change depending on age, season, etc.

[0014] According to the disclosed technology, the stress of a wearable device user is appropriately grasped. As will be explained in detail later, among the HRVs measured by the wearable device, an HRV measured under the same conditions is extracted as a representative HRV for that day. The user's stress is grasped based on the progress of the representative HRV. The grasped stress is visualized and presented to the user. There is no need to control the timing of HRV measurement by the wearable device.

[0015] 1. Embodiment Fig. 1 is a diagram illustrating an example of a schematic configuration of an information processing system 100 according to an embodiment. A user of the information processing system 100 is referred to as user 1 and illustrated. The information processing system 100, for example, grasps the stress of user 1 and presents the grasped stress. Such an information processing system 100 can also be called a support system for supporting the health care of user 1.

[0016] The information processing system 100 includes a terminal device 2, a wearable device 3, and an information processing device 4. These elements are configured to be able to communicate with each other so that at least data measured by the wearable device 3 can be used by the information processing device 4 and information generated by the information processing device 4 can be used by the terminal device 2.

[0017] The terminal device 2 is a device used by the user 1 and may also be called a user terminal. An example of the terminal device 2 is a mobile terminal device such as a smartphone, but is not limited to this. Fig. 1 also shows functional blocks of the terminal device 2. The terminal device 2 includes a storage unit 20, a UI unit 21, and a processing unit 22.

[0018] The storage unit 20 stores information used by the terminal device 2. An example of the information stored in the storage unit 20 is a program 200. The program 200 is a program (software) for causing a computer to function as the terminal device 2, and more specifically, is an application program (application software) for causing the processing unit 22 to execute various processes. For example, a memory or the like of the terminal device 2 corresponds to the storage unit 20.

[0019] It should be noted that "information" may be interpreted as meaning "data," and these may be interpreted as appropriate within the scope of no contradiction.

[0020] The UI unit 21 is a user interface unit that accepts operations (user operations) of the terminal device 2 by the user 1 and presents information to the user 1. The presentation may include displaying information, outputting sound, etc. For example, the operation panel, display, speaker, etc. of the terminal device 2 correspond to the UI unit 21.

[0021] The processing unit 22 executes various processes necessary for the operation of the terminal device 2. The processing unit 22 can also be called a control unit that controls the entire terminal device 2 by controlling other elements within the terminal device 2. For example, a processor or the like of the terminal device 2 corresponds to the processing unit 22.

[0022] The wearable device 3 is a device used by the user 1. An example of the wearable device 3 is a wristband-type device (also called a smart watch, etc.), but is not limited to this. Note that the term "device" may be interpreted as meaning "apparatus," and these terms may be interpreted interchangeably as appropriate within the scope of no contradiction.

[0023] The wearable device 3 measures the vital signs of the user 1. Various known vital sign measurement technologies, including non-contact measurement technologies, may be used. Measurement may be understood to include detection, calculation, and the like. The wearable device 3 stores the measurement results as measurement data 30. The measurement data 30 may include all data measured so far, or may include only data from the most recent period up to a certain period in the past.

[0024] Examples of the vital signs of user 1 are user 1's HR (heart rate), HRV, sleep, etc., and such data is included in the measurement data 30.

[0025] HR indicates the heart rate of user 1, measured in bpm. HR data is described by associating the date and time with the HR. The date and time may refer to the date and time when the HR was measured (measurement date and time), and the same applies to HRV and sleep. Note that when the date does not need to be taken into consideration, for example, when referring to a time on the same day or a time within 24 hours, the time can identify the date and time, so the terms time and date and time may be interpreted as appropriate.

[0026] HRV indicates the heart rate variability of user 1, and is expressed in units of ms. HRV data is described by associating the date and time with the HRV.

[0027] Sleep indicates the sleep state of user 1. The sleep state indicates whether user 1 is asleep or awake (awake). The sleep state includes sleep phases. The sleep phases include multiple sleep phases, more specifically, for example, REM sleep phases, deep sleep phases, and core sleep phases. However, the sleep phases are not limited to these three types. There may be only two types of sleep phases, or four or more types. There may be multiple sleep phases that the wearable device 3 can distinguish and detect. Sleep data is described by associating date and time with sleep phases.

[0028] In the following description, the HR data, HRV data, and sleep data may be simply referred to as HR, HRV, and sleep.

[0029] The measurement data 30 is transmitted from the wearable device 3 to the information processing device 4. The timing of transmission may be determined arbitrarily. For example, the measurement data 30 may be transmitted in response to a request from the information processing device 4 or at a predetermined time. The measurement data 30 may also be transmitted via the terminal device 2.

[0030] The information processing device 4 processes the measurement data 30 of the wearable device 3 and generates content to be presented to the user 1. An example of the information processing device 4 is a server device, but is not limited to this. FIG. 1 also shows functional blocks of the information processing device 4. The information processing device 4 includes a storage unit 40, an acquisition unit 41, an extraction unit 42, and a generation unit 43.

[0031] The storage unit 40 stores information used by the information processing device 4. Examples of information stored in the storage unit 40 include a program 400, an HR history 401, an HRV history 402, a sleep history 403, and a representative HRV transition 404. The program 400 is a program (software) for causing a computer to function as the information processing device 4, and more specifically, is an application program (application software) for causing the acquisition unit 41, the extraction unit 42, and the generation unit 43 to execute processing. The HR history 401 and the like will be described later.

[0032] The acquisition unit 41 acquires the measurement data 30 from the wearable device 3. The measurement data 30 acquired by the acquisition unit 41 is stored (held, accumulated) in the storage unit 40 as an HR history 401, an HRV history 402, and a sleep history 403. The following description will also refer to FIG. 2 .

[0033] 2 is a diagram showing examples of an HR history 401, an HRV history 402, and a sleep history 403. In the diagram, numerical values ​​are schematically indicated as xxxx, etc. Sleep phases are schematically indicated as sleep phase f1, sleep phase f2, and sleep phase f3.

[0034] As shown in Fig. 2A, the HR history 401 describes the date and time in association with the HR. As shown in Fig. 2B, the HRV history 402 describes the date and time in association with the HRV. As shown in Fig. 2C, the sleep history 403 describes the date and time in association with the sleep phase. Note that the sleep history 403 may describe only the time when the sleep phase changed (transitioned), or may describe the sleep phase at regular intervals (e.g., 30 seconds).

[0035] 1 , for example, the HR history 401, HRV history 402, and sleep history 403 having the data structure described above are stored in the storage unit 40. Note that if the HR history 401, HRV history 402, and sleep history 403 already exist when the measurement data 30 is acquired, these history data may be overwritten (updated).

[0036] The extraction unit 42 extracts a representative HRV from the measurement data 30, more specifically, from the HRV history 402. The representative HRV is a value that represents the HRV for a day. The HRV measured under certain conditions is extracted as the representative HRV for each day. Specifically, the extraction unit 42 extracts the HRV measured while the user 1 was sleeping and when the sleep phase of the user 1 was a designated sleep phase as the representative HRV.

[0037] The designated sleep phase is one sleep phase designated from multiple sleep phases. Any sleep phase may be designated. For example, the longest sleep phase among the multiple sleep phases, i.e., the sleep phase that occupies the largest proportion (longest duration) of the user 1's sleep time, may be the designated sleep phase. An example of a designated sleep phase is a core sleep phase.

[0038] The representative HRV may be the HRV measured at the time closest to a specific time. The specific time may be a predetermined time (a fixed time) or a time identified based on the measurement data 30. The time closest to the specific time may be interpreted to include the same time as the specific time. An example of a time identified based on the measurement data 30 is the time when the lowest HR was measured. Specific descriptions will be provided with reference to FIGS. 3 and 4.

[0039] 3A, 3B, and 3C are diagrams showing an example of extraction of a representative HRV. An HR history 401, an HRV history 402, and a sleep history 403 are schematically shown in (A), (B), and (C) of FIG. 3. The horizontal axis of the graph indicates date and time (or time), and the vertical axis of the graph indicates HR, HRV, and sleep phase.

[0040] Minimum HR, HR ｍｉｎ Also, HR ｍｉｎ The date and time when the sleep time is measured is shown as date and time t10. Three types of sleep phases are shown as sleep phase f1, sleep phase f2, and sleep phase f3. Here, sleep phase f2 is assumed to be the designated sleep phase.

[0041] In this example, as shown in Figures 3A and 3B, the interval between HRV measurements by the wearable device 3 (Figure 1) is longer than the interval between HR measurements. It can also be said that the number of HRV measurements within the same period is smaller than the number of HR measurements. There is no need to control the timing of these measurements, and an appropriate representative HRV can still be extracted.

[0042] Specifically, as shown in FIG. 3B, the HRV history 402 includes HRVs measured at dates t8, t9, t11, and t12. ｍｉｎ The closest date and time to the date and time t10 at which the value was measured is date and time t11, and the next closest date and time is date and time t9.

[0043] 3C, the date and time t11 is sleep phase f1, and the date and time t9 is sleep phase f2. Because sleep phase f2 is the designated sleep phase, the extractor 42 (FIG. 1) extracts the HRV at date and time t9 as the representative HRV, rather than the HRV at date and time t11.

[0044] Another example will be described with reference to FIG. 4 using specific numerical examples.

[0045] 4A, 4B, and 4C show examples of representative HRV extraction. Specific examples of an HR history 401, an HRV history 402, and a sleep history 403 are shown in (A), (B), and (C) of FIG. 4. Parts that require particular attention are hatched.

[0046] In FIG. 4A, the date and time when HR is lowest (HR ｍｉｎ The date and time of this event (date and time) is 01:01:01 on December 17, 2023. The closest HRV measurement time to this time on this day is 01:00:01 in FIG. 4B, and the HRV is 54 (ms). However, as shown in FIG. 4C, this time is in sleep phase f1, not sleep phase f2 (a designated sleep phase), so the HRV at that time is not extracted as the representative HRV. Instead, the HRV measured between 02:00:01 and 06:01:00 in sleep phase f2, i.e., the HRV (20 ms) measured at 02:02:01 in FIG. 4B, is extracted as the representative HRV.

[0047] Returning to Fig. 1, for example, the extraction unit 42 extracts a total representative HRV as described above. The extracted representative HRV is stored in the storage unit 40. The representative HRV extracted for each of the multiple days is stored in the storage unit 40 as a representative HRV trend 404. The following description will also refer to Fig. 5.

[0048] 5 is a diagram showing an example of the representative HRV trend 404. The representative HRV trend 404 describes the date and the representative HRV (bpm) in association with each other. The year and month of the date are shown schematically as xxxx, etc. The representative HRV is shown schematically as xxx.

[0049] 1, for example, representative HRV trend 404 having the data structure described above is stored in storage unit 40. If representative HRV trend 404 already exists when the representative HRV is extracted, that data may be overwritten (updated).

[0050] The generation unit 43 generates content to be presented to the user 1. The content may be interpreted as data or information, and may be appropriately interpreted as data or information.

[0051] The content includes stress information. The stress information is information related to the stress of the user 1, and more specifically, is information for visualizing and presenting the stress of the user 1. The content generated by the generation unit 43 is transmitted from the information processing device 4 to the terminal device 2 and presented to the user 1 by the UI unit 21 of the terminal device 2.

[0052] Various other contents other than stress information may also be selectively presented, for example. Examples of other contents include content for alleviating stress, specifically, content such as sleep CBT, mindfulness sessions, and behavioral activation. These are well known in the field of sleep therapy, and therefore detailed description will be omitted. Note that these contents may be generated in advance and stored in the information processing device 4 (for example, stored in the storage unit 40). In the case of content that involves interactive work, generation processing by the generation unit 43 may be involved.

[0053] The generation of stress information from among the content will be further described. Generator 43 generates stress information for user 1 based on the representative HRV, more specifically, representative HRV transition 404. The description will also refer to FIGS. 6 to 8.

[0054] FIG. 6 is a diagram showing an example of generating stress information. A reference value and a most recent value of the representative HRV are calculated based on the representative HRV transition 404. Examples of the reference value and most recent value of the representative HRV are the long-term average value and short-term average value of the representative HRV. The long-term average value is the average value of the representative HRV over a relatively long period up to the most recent day (e.g., the current day). The short-term average value is the average value of the representative HRV over a relatively short period up to the most recent day. The specific number of days in the long-term and short-term periods is not particularly limited, but the long-term period may be, for example, approximately 3 to 30 days, and the short-term period may be one or more days shorter than the long-term period. Hereinafter, the reference value and most recent value of the representative HRV are assumed to be the long-term average value and short-term average value.

[0055] The calculated long-term average value and short-term average value are compared. An example of the comparison result is their ratio. The ratio of the short-term average value to the long-term average value is referred to as ratio r. Ratio r can also be called an index showing an increase or decrease in representative HRV. An example of a comparison result other than ratio r is the difference between the long-term average value and the short-term average value. However, unless otherwise specified, the comparison result will be considered to be ratio r.

[0056] As described above, the ratio r indicates the recent trend of increase or decrease in the HRV of user 1. As mentioned at the beginning, there is a correlation between worsening stress and a decrease in HRV. That is, a decrease in HRV may indicate worsening stress. Conversely, an increase in HRV may indicate an improvement in stress. Therefore, the stress trend of user 1 can be grasped (determined, estimated, etc.) based on the ratio r. As an example, a threshold determination for the ratio r may be used. This description will also refer to FIG. 7 .

[0057] 7 is a diagram showing an example of threshold determination for ratio r. In this example, two thresholds are used. The first threshold is threshold r ｔｈ１ The second threshold is called a threshold r ｔｈ２ The threshold value r ｔｈ１ is a value smaller than 1. The threshold r ｔｈ２ is a value greater than one.

[0058] The ratio r is the threshold r ｔｈ１ In the following case (r≦rｔｈ１ ), it is determined that stress is on the rise. ｔｈ２ In the above case (r ｔｈ２ <r), it is determined that stress is improving (decreasing). ｔｈ１ is greater than the threshold r ｔｈ２ If it is smaller than (r ｔｈ１ <r<r ｔｈ２ ), stress is judged to remain the same (flat).

[0059] For example, the tendency of stress is grasped based on the threshold judgment for the ratio r as described above. ｔｈ１ The stress information may be generated to indicate that stress is on the rise in the following cases: ｔｈ２ In the above cases, the stress information may be generated to indicate that the stress is improving. ｔｈ１ is greater than the threshold r ｔｈ２ If the stress is less than 0.001, the stress may be generated to indicate that the stress is to remain the same.

[0060] The above threshold r ｔｈ１ and threshold r ｔｈ２ may be a value that is infinitely close to 1. In this case, the stress information may be generated simply so that if the ratio r is greater than 1, it indicates that stress is on the rise, and if the ratio r is less than 1, it indicates that stress is on the rise.

[0061] Note that threshold determination may also be performed in the same manner when the difference value (ms) between the long-term average value and the short-term average value is used as the comparison result, rather than the ratio r between them.

[0062] A more specific example of the stress information will be described with reference to FIG.

[0063] 8 is a diagram showing a specific example of stress information. Various information such as a representative HRV transition 404, a short-term average value, a long-term average value, a ratio r, and a stress trend can constitute the stress information.

[0064] 8 shows specific dates and times and representative HRV values ​​of the representative HRV transition 404. The long-term average value and short-term average value of the representative HRV are average values ​​over three days and two days. ｔｈ１ and threshold r ｔｈ２ are 0.9 and 1.1.

[0065] The ratio r is calculated for each day from January 3 to January 5, for which valid data is available (no data shortage) for both the long-term average value and the short-term average value. The ratio r for January 3 is 0.75, and the threshold value r ｔｈ１ The ratio r on January 4th is 1.2, which is smaller than the threshold r ｔｈ２ The ratio r on January 5th is 1.33, which is greater than the threshold r ｔｈ２ Stress shows a tendency to improve.

[0066] Returning to FIG. 1 , for example, stress information such as that described above is generated by the generation unit 43. The generated stress information is transmitted as one piece of content from the information processing device 4 to the terminal device 2. The UI unit 21 of the terminal device 2 presents the stress information. Information can be presented in various ways. An example will be described with reference to FIG. 9 .

[0067] 9 is a diagram showing an example of presentation of stress information. The UI unit 21 of the terminal device 2 visualizes and presents (displays) the stress information. In the example shown in FIG. 9, the stress information includes a graph showing the transition of the representative HRV, a long-term average value of the representative HRV, a short-term average value of the representative HRV, and a stress trend. For example, such stress information can be used to visualize the stress of user 1 and present it to user 1.

[0068] Various presentation modes other than that shown in Fig. 9 may be used. An example of another mode is a heat map. This will be described with reference to Figs. 10 and 11 .

[0069] 10 and 11 are diagrams showing examples of presentation of stress information. In the example shown in Fig. 10, the transition of the representative HRV is shown in a heat map. In the example shown in Fig. 11, the transition of the ratio r is shown in a heat map. Stress information may be presented in such a manner.

[0070] 12 to 14 are flowcharts showing examples of processing (information processing methods) executed in the information processing system 100. Descriptions of content that overlap with those described above will be omitted where appropriate.

[0071] FIG. 12 shows a process flow for acquiring and storing measurement data 30 of the wearable device 3.

[0072] In step S1 , the acquisition unit 41 of the information processing device 4 acquires the measurement data 30 of the wearable device 3 .

[0073] In step S2, the acquisition unit 41 of the information processing device 4 determines whether new data is present. For example, if the measurement data 30 acquired in the previous step S1 includes data that has not been acquired before, in other words, data that is not included in the HR history 401, the HRV history 402, and the sleep history 403, it is determined that new data is present. If new data is present (step S2: Yes), the process proceeds to step S3. If not (step S2: No), the process of the flowchart ends.

[0074] In step S3, the acquisition unit 41 of the information processing device 4 stores new data from the measurement data 30 acquired in the previous step S1 in the storage unit 40. Specifically, the new HR, HRV, and sleep data are added to the HR history 401, HRV history 402, and sleep history 403. The processing of the flowchart then ends.

[0075] FIG. 13 shows a process flow for extracting a representative HRV.

[0076] In step S11, the extraction unit 42 of the information processing device 4 identifies a sleep time period and a sleep phase. Based on the sleep history 403, a sleep time period indicating a time period when the user 1 was sleeping and a sleep phase at each time during that time period (during sleep) are identified.

[0077] In step S12, the extraction unit 42 of the information processing device 4 determines one time during sleep. The time to be determined may be a predetermined time (a fixed time), or may be a time when HR is minimum during sleep (HR ｍｉｎ The time may be the time of day.

[0078] In step S13, the extraction unit 42 of the information processing device 4 extracts the HRV during sleep at the time closest to the determined time. From the HRV history 402, the HRV at the time closest to the time determined in the previous step S12 is extracted.

[0079] In step S14, the extraction unit 42 of the information processing device 4 determines whether the extracted HRV is the HRV during the designated sleep phase. This determination is made, for example, by referring to the sleep history 403. If the extracted HRV is the HRV during the designated sleep phase (step S14: Yes), the process proceeds to step S16. If not (step S14: No), the process proceeds to step S15.

[0080] In step S15, the extraction unit 42 of the information processing device 4 extracts the next closest HRV during sleep. Based on the HRV history 402, the next closest HRV during sleep to the time determined in step S12 after the HRV extracted in step S13 (or this previous step S15) is extracted. Then, step S14 is executed again.

[0081] By repeatedly executing the processes of steps S14 and S15, the HRV measured while the user 1 is sleeping and when the sleep phase of the user 1 is the designated sleep phase is extracted.

[0082] In step S16, the extraction unit 42 of the information processing device 4 stores the extracted HRV as a representative HRV. Specifically, the HRV extracted in the previous step S13 or step S15 is added as the representative HRV for that day to the representative HRV transition 404 stored in the storage unit 40. The processing of the flowchart then ends.

[0083] FIG. 14 shows a processing flow for presenting stress information.

[0084] In steps S21 and S22, the generation unit 43 of the information processing device 4 calculates the long-term average value and the short-term average value of the representative HRV. Note that the order of execution of the process of step S21 and the process of step S22 is not particularly limited, and they may be executed in parallel (simultaneously).

[0085] In step S23, the generation unit 43 of the information processing device 4 compares the calculation results. Specifically, the ratio r of the short-term average value calculated in the previous step S22 to the long-term average value calculated in the previous step S21 is compared with the threshold value r ｔｈ１ and threshold r ｔｈ２ The ratio r is used to determine the threshold value r ｔｈ１ In the following case (r≦r ｔｈ１ ), the process proceeds to step S24. ｔｈ１ is greater than the threshold r ｔｈ２ If it is smaller than (r ｔｈ１ <r<r ｔｈ２ ), the process proceeds to step S25. ｔｈ２ In the above case (r ｔｈ２ <r), the process proceeds to step S26.

[0086] In step S24, a message is displayed indicating that the stress level of user 1 is worsening. The generation unit 43 of the information processing device 4 generates stress information indicating that the stress level of user 1 is worsening. The generated stress information is transmitted from the information processing device 4 to the terminal device 2. The UI unit 21 of the terminal device 2 presents the stress information to user 1.

[0087] In step S25, a message is displayed indicating that the current level of stress is to be maintained. The generation unit 43 of the information processing device 4 generates stress information indicating that the stress tendency of the user 1 is to be maintained. The generated stress information is transmitted from the information processing device 4 to the terminal device 2. The UI unit 21 of the terminal device 2 presents the stress information to the user 1.

[0088] In step S26, a message is displayed indicating that the stress level of user 1 is improving. The generation unit 43 of the information processing device 4 generates stress information indicating that the stress level of user 1 is improving. The generated stress information is transmitted from the information processing device 4 to the terminal device 2. The UI unit 21 of the terminal device 2 presents the stress information to user 1.

[0089] After the processing of step S24, step S25, or step S26 is completed, the processing of the flowchart ends.

[0090] For example, as described above, it is possible to understand the stress of user 1 from the measurement data 30 of wearable device 3, and visualize and present stress information to user 1. As mentioned at the beginning, HRV easily fluctuates depending on daytime activities, etc. Furthermore, it easily fluctuates not only depending on whether the user is asleep or awake, but also on the state of sleep, such as the sleep phase. According to this embodiment, an HRV measured under similar conditions is extracted as a representative HRV, thereby increasing the possibility of appropriately understanding user 1's stress.

[0091] Furthermore, based on the comparison result (e.g., ratio r) between the long-term average value and the short-term average value of the representative HRV, it is possible to grasp the stress trend of user 1. By visualizing the trend and presenting it to user 1, user 1 can easily grasp his or her own stress trend.

[0092] According to the method of the present embodiment, there is no need to control the timing of measurements by the wearable device 3, and such control is not even necessary. Even from measurement data 30 whose acquisition time is unknown, an appropriate representative HRV can be extracted by filtering, and the stress of the user 1 can be appropriately understood.

[0093] The subject of the various processes executed in the information processing system 100 may be appropriately interpreted as a computer, a processor, etc. This is because the terminal device 2, the wearable device 3, and the information processing device 4 are all configured to include a computer, and the processes in each can be said to be executed by a computer. The various processes can also be called information processing methods executed by a computer, a processor, etc., or can also be called operation methods of various devices (or equipment), such as the terminal device 2, the wearable device 3, and the information processing device 4.

[0094] 2. Modifications The disclosed technology is not limited to the above-described embodiment. For example, some or all of the functions of the information processing device 4 may be incorporated into the terminal device 2. The processing unit 22 of the terminal device 2 may function as some or all of the acquisition unit 41, extraction unit 42, and generation unit 43 of the information processing device 4. The storage unit 20 of the terminal device 2 may function as some or all of the storage unit 40 of the information processing device 4. Some of the functions of the terminal device 2 and the information processing device 4 may be incorporated into a device other than the terminal device 2 and the information processing device 4, and used as a component of the information processing system 100.

[0095] 15 is a diagram showing an example of the hardware configuration of a device, etc. The terminal device 2 or the information processing device 4 described above is realized by, for example, a computer 1000 shown in FIG.

[0096] The computer 1000 includes a CPU 1100, a RAM 1200, a ROM (Read Only Memory) 1300, a HDD (Hard Disk Drive) 1400, a communication interface 1500, and an input / output interface 1600. The components of the computer 1000 are connected to each other via a bus 1050.

[0097] The CPU 1100 operates based on programs stored in the ROM 1300 or the HDD 1400 and controls each component. For example, the CPU 1100 loads the programs stored in the ROM 1300 or the HDD 1400 into the RAM 1200 and executes processing corresponding to the various programs. Examples of the programs are the programs 200 and 400 described above with reference to FIG. 1.

[0098] The ROM 1300 stores boot programs such as a Basic Input Output System (BIOS) executed by the CPU 1100 when the computer 1000 is started, and programs that depend on the hardware of the computer 1000 .

[0099] HDD 1400 is a computer-readable recording medium that non-temporarily records programs executed by CPU 1100 and data used by such programs. Specifically, HDD 1400 is a recording medium that records a program for the information processing method according to the present disclosure, which is an example of program data 1450.

[0100] The communication interface 1500 is an interface for connecting the computer 1000 to an external network 1550 (e.g., the Internet). For example, the CPU 1100 receives data from other devices and transmits data generated by the CPU 1100 to other devices via the communication interface 1500.

[0101] The input / output interface 1600 is an interface for connecting the input / output device 1650 and the computer 1000. For example, the CPU receives data from input devices such as a keyboard or a mouse via the input / output interface 1600. The CPU 1100 also transmits data to output devices such as a display, a speaker, or a printer via the input / output interface 1600. The input / output interface 1600 may also function as a media interface for reading programs and the like recorded on a predetermined computer-readable recording medium. Examples of the medium include optical recording media such as a DVD (Digital Versatile Disc) or a PD (Phase Change Rewritable Disc), magneto-optical recording media such as an MO (Magneto-Optical Disc), tape media, magnetic recording media, or semiconductor memory.

[0102] When the computer 1000 functions as the terminal device 2 or the information processing device 4 described above, the CPU 1100 of the computer 1000 executes a program loaded on the RAM 1200 to realize the functions of the processing unit 22, or the acquisition unit 41, extraction unit 42, and generation unit 43. The program may be stored in the HDD 1400. The CPU 1100 reads and executes the program data 1450 from the HDD 1400, but as another example, the CPU 1100 may acquire the program from another device via an external network 1550.

[0103] Each of the above components may be configured using general-purpose materials or may be configured using hardware specialized for the function of each component. Such configurations may be changed as appropriate depending on the technical level at the time of implementation.

[0104] 4. Application Examples The information processing system 100 may be modified, expanded, or the like to include various technologies other than those described above. For example, user 1 ( FIG. 1 ) may be a patient with a disease. Examples of diseases include neurological diseases, muscular degenerative diseases, cardiovascular diseases, psychiatric diseases, diabetes, immune / allergic diseases, and diseases specific to the elderly. More specifically, these diseases include Parkinson's disease, muscular dystrophy, cerebellar degenerative diseases, amyotrophic lateral sclerosis (ALS), arrhythmia, heart failure, high blood pressure, depression, dementia, sleep disorders, asthma, hay fever, sarcopenia / frailty, and the like. It is also possible to provide an information processing system 100 that is useful for the healthcare of such user 1. An example will be described with reference to FIG. 16 .

[0105] 16 is a diagram showing an example of functional blocks of an information processing system 100 according to an application example. The information processing system 100 includes an information acquisition device 51, an information processing device 52, and one or more terminals. Examples of the terminals include a medical professional terminal 53, a community member terminal 54, a life insurance / health insurance terminal 55, a product / service provider terminal 56, and an analysis terminal 57. These devices and terminals are configured to be able to communicate via a network.

[0106] The information acquisition device 51 includes a sensor 511 and a user terminal 512. The sensor 511 and the user terminal 512 may correspond to the wearable device 3 and the terminal device 2 (FIG. 1) described above. The sensing data of the sensor 511 may correspond to the measurement data 30 (FIG. 1).

[0107] The sensing data may include data other than the aforementioned HR, HRV, and sleep data. Examples of other sensing data include electroencephalogram (EEG), electrocardiogram (ECG), blood flow, blood pressure, respiration, lung capacity, electromyography (EMG), body temperature, blood glucose level, weight, vibration, impact, and sweat data. Various other information may also be included in the sensing data. For example, the results of a user operation may be included in the sensing data. Examples of the operation results include operation time, operation time (operation timing), input voice, and input document. Furthermore, medication / meal information related to medication and meals of user 1 ( FIG. 1 ) may also be included in the sensing data. Medication / meal information may be input and acquired by user operation, or detected by a sensor installed in a medicine box, tray, or the like. Health checkup results of user 1 may also be included in the sensing data.

[0108] In the information acquisition device 51 , sensing data from a sensor 511 is collected by a user terminal 512 .

[0109] The user terminal 512 includes a communication unit 513, a user interface unit 514, a processing unit 515, and a storage unit 516. An example of information stored in the storage unit 516 is an application program 5161 (application software). Execution of the application program 5161 provides an application for use by the user 1. One example of an application is an application that presents the various types of content described above, and in this case, the application program 5161 may correspond to the program 200 in FIG. 1 described above. Another example of an application is a rehabilitation application. For example, a rehabilitation menu showing rehabilitation content, etc. is presented (displayed, etc.) by the user interface unit 514. The user 1 performs rehabilitation according to the presented rehabilitation menu.

[0110] The communication unit 513 communicates with other devices, etc. For example, the communication unit 513 receives sensing data from the sensor 511 and receives result information (described later) from the information processing device 52. The communication unit 513 also transmits the sensing data to the information processing device 52.

[0111] The user interface unit 514 accepts operations of the user terminal 512 by the user 1 and presents information to the user 1. The user interface unit 514 may correspond to the UI unit 21 described above.

[0112] The processing unit 515 functions as a control unit that controls each element of the user terminal 512 and executes various processes. For example, the processing unit 515 executes an application program 5161. This provides the various applications described above.

[0113] The information processing device 52 receives and processes the sensing data from the information acquisition device 51. The information processing device 52 includes a communication unit 521, an estimation unit 522, a storage unit 523, a recommendation unit 524, and a processing unit 525. Examples of information stored in the storage unit 523 include patient information 5231, community information 5232, an algorithm DB 5233, a trained model 5234, recommendation information 5235, and anonymously processed information 5236.

[0114] The patient information 5231 includes information about user 1, who is a patient. Examples of the patient information 5251 include disease information, diagnosis information, medical records, checkup information, medication information, hospital visit history information, rehabilitation history information, etc., of user 1. The disease information includes the name of user 1's disease, etc. The diagnosis information, medical records, checkup information, medication information, and hospital visit history information are information about user 1's diagnosis, medical treatment, checkup, medication, and hospital visit history, and are provided, for example, from outside the information processing device 52 (such as the medical professional terminal 53). The rehabilitation history information is information about past rehabilitation that user 1 has undergone, and is obtained, for example, by the rehabilitation application described above and transmitted from the user terminal 512 to the information processing device 52.

[0115] The community information 5232 is information about the community to which the user 1 belongs, and includes information about the members and the community member terminals 54 .

[0116] The algorithm DB 5233, the trained model 5234, the recommendation information 5235, and the anonymously processed information 5236 will be described later.

[0117] The communication unit 521 communicates with other devices, etc. For example, the communication unit 521 receives sensing data from the user terminal 512.

[0118] The estimation unit 522 performs estimation processing based on the sensing data. For example, the estimation unit 522 calculates various indices that can be used for estimation based on the sensing data. Examples of indices include the representative HRV, ratio r, and stress tendency described above. Other indices that may be calculated include indices related to the user 1's physical function, behavior, disease, and emotions. Various algorithms may be used to calculate the indices. The algorithm may be a trained model generated by machine learning using training data. The algorithm may be designed to output an index when sensing data is input, or may be designed to output an index when feature quantities obtained from the sensing data are input. The feature quantities may be calculated by the estimation unit 522 based on the sensing data. Calculation of feature quantities may be interpreted to include generation, extraction, and the like of feature quantities.

[0119] Information including the estimation results of the estimation unit 522 is referred to as "result information" and is illustrated. Examples of result information include the stress information mentioned above, as well as content such as sleep CBT, mindfulness sessions, and behavioral activation. The communication unit 521 transmits the result information to other devices and terminals, in this example, the user terminal 512, the medical professional terminal 53, the community member terminal 54, and the life insurance / health insurance terminal 55. The recommendation unit 524 and processing unit 525 of the information processing device 52 will be described later.

[0120] In the user terminal 512, the result information is presented by the user interface unit 514. The user 1 can know various indicators related to his / her stress state, physical function, behavior, disease, etc. Individual content can also be presented based on the estimation results.

[0121] The medical staff terminal 53 is a terminal used by a medical staff C. Examples of the medical staff C include doctors, nurses, pharmacists, physical therapists, and caregivers. The medical staff terminal 53 includes a communication unit 531, a user interface unit 532, and a storage unit 533. An example of information stored in the storage unit 533 is medical information 5331. The medical information 5331 includes, for example, medical record information of the user 1, and is used for diagnosing the user 1 by the medical staff using the medical staff terminal 53.

[0122] The communication unit 531 communicates with other devices, etc. For example, the communication unit 531 receives result information from the information processing device 52.

[0123] The user interface unit 532 accepts operations of the medical worker terminal 53 by the medical worker and presents information to the medical worker. For example, result information from the information processing device 52 is presented, and the medical information 5331 is updated accordingly, such as by adding medical record information. Intervention by the medical worker is also possible. For example, individual content such as a rehabilitation menu customized by the medical worker to suit the user 1 is generated. The content is transmitted to the user terminal 512 by the communication unit 531 and presented to the user 1.

[0124] The community member terminal 54 is a terminal used by members. Members belong to the same community as user 1, and are, for example, family members of user 1, other patients with the same disease as user 1, etc. The community member terminal 54 includes a communication unit 541 and a user interface unit 542. The communication unit 541 communicates with other devices, etc. For example, the communication unit 541 receives result information from the information processing device 52.

[0125] The user interface unit 542 accepts operations of the community member terminal 54 by members and presents information to members. For example, result information from the information processing device 52 is presented and shared with members. Individual content based on the estimation results can also be presented.

[0126] Life insurance / health insurance terminal 55 is a terminal used by insurance companies, health insurance companies, etc. Life insurance / health insurance terminal 55 includes a communication unit 551, an analysis unit 552, and a memory unit 553. An example of information stored in memory unit 553 is customer / employee information 5531. Customer / employee information 5531 includes information regarding user 1's life insurance, health insurance, etc.

[0127] The communication unit 551 receives the result information from the information processing device 52. The analysis unit 552 analyzes the result information and identifies (calculates, etc.) insurance premiums and rewards. The identification may involve the work, judgment, etc. of employees of the life insurance company or health insurance company. Insurance premiums may be reduced or changed to a limited plan, etc. The communication unit 551 transmits the identified insurance premiums and recommended insurance premium / reward information to the user terminal 512. The insurance premium / reward information is presented by the user interface unit 514 of the user terminal 512.

[0128] The product / service provider terminal 56 is a terminal used by a company or the like that provides a product / service. Examples of products include wheelchairs, walking aids, rehabilitation equipment, health foods, health equipment, etc. An example of a service is a health application that can be executed on the user terminal 512.

[0129] The product / service provider terminal 56 includes a communication unit 561 and a user interface unit 562. For example, product / service information that associates the result information with products and services is input or generated via the user interface unit 562. For example, a condition indicating the association between an index shown in the result information and a product or service is input, and information including this condition is generated as product / service information. The communication unit 561 transmits the product / service information to the information processing device 52.

[0130] The communication unit 521 of the information processing device 52 receives product / service information from the product / service provider terminal 56 .

[0131] The recommendation unit 524 and processing unit 525 of the information processing device 52 will be described. The recommendation unit 524 generates recommendation information 5235 including information on products and services to be recommended to the user 1 based on the product / service information from the product / service provider terminal 56. For example, based on the conditions input at the product / service provider terminal 56, products and services corresponding to the result information are determined, and recommendation information 5235 recommending them is generated. The stress information described above, as well as content such as sleep CBT, mindfulness sessions, and behavioral activation, may be generated as recommendation information 5235. The communication unit 521 transmits the recommendation information 5235 to the user terminal 512 and the community member terminal 54. The recommendation information 5235 is presented by the user interface unit 514 of the user terminal 512 or by the user interface unit 542 of the community member terminal 54.

[0132] The processing unit 525 anonymizes the result information to generate anonymous processed information 5236. The anonymous processed information 5236 describes the anonymized personal information and the result information in association with each other.

[0133] The communication unit 521 of the information processing device 52 transmits the anonymously processed information 5236 to the analysis terminal 57 .

[0134] The analysis terminal 57 is a terminal used by, for example, a company that provides the above-mentioned products / services, a pharmaceutical company that conducts clinical development, etc. The analysis terminal 57 includes a communication unit 571, an analysis unit 572, and a user interface unit 573.

[0135] The communication unit 571 receives the anonymously processed information 5236 from the information processing device 52. The analysis unit 572 performs data analysis based on the anonymously processed information 5236. The analysis may involve the work, judgment, etc. of company employees, etc. The user interface unit 573 presents information related to the data analysis, etc. Examples of analysis include analysis of user demographics for products such as health foods and health equipment, and data analysis for clinical development. The anonymously processed information 5236 can be utilized for various services, such as marketing analysis by manufacturers, analysis of the proportion, age, gender, etc. of users with specific symptoms, and symptom monitoring for patients taking specific medications.

[0136] In the information processing system 100, stress information of user 1 can be visualized and presented not only on the terminal device 2 but also on the medical staff terminal 53, the community member terminal 54, the life insurance / health insurance terminal 555, etc. This allows people other than user 1 to understand the stress of user 1. For example, if the stress level is on the rise, an alert or the like may also be presented.

[0137] <Modification of Application Example> In the above application example, a case has been described where sensing data from the sensor 511 is transmitted to the information processing device 52 via the user terminal 512. However, part or all of the sensing data from the sensor 511 may be transmitted directly from the sensor 511 to the information processing device 52 without going through the user terminal 512.

[0138] In the above embodiment, an example has been described in which functions and information related to various services at the life insurance / health insurance terminal 55, product / service provider terminal 56, and analysis terminal 57, such as the recommendation unit 524, processing unit 525, recommendation information 5235, and anonymously processed information 5236, are provided in the information processing device 52. However, these functions and information may also be provided in a server device or the like (service provider server device) managed by the user of the corresponding terminal. The life insurance / health insurance terminal 55, product / service provider terminal 56, and analysis terminal 57 communicate with the corresponding server device or the like to use its functions. This can reduce the processing load on the information processing device 52 and simplify functions to reduce costs.

[0139] Some of the functions of the terminal may be provided in a server device or the like managed by the user of the corresponding terminal. For example, the functions of the analysis unit 552 of the life insurance / health insurance terminal 55 may be provided in a server device or the like managed by an insurance company, health insurance company, etc. The life insurance / health insurance terminal 55 communicates with the server device or the like to use the functions. The functions of the analysis unit 572 of the analysis terminal 57 may be provided in a server device or the like managed by a pharmaceutical company, etc. The analysis terminal 57 communicates with the server device or the like to use the functions. This makes it possible to reduce the processing burden on the life insurance / health insurance terminal 55 and the analysis terminal 57, and to reduce costs by simplifying the functions.

[0140] 5. Summary The techniques described above can be specified, for example, as follows. One of the disclosed techniques is an information processing method executed by a computer. As described with reference to FIGS. 1 to 5, 12, and 13, the information processing method includes acquiring measurement data 30 from a wearable device 3 that measures the HR, HRV, and sleep of a user 1 (steps S1 to S3), and extracting a representative HRV for one day from the measurement data 30 (steps S13 to S16). The representative HRV is an HRV measured while the user 1 is asleep and when the user 1 is in a designated sleep phase.

[0141] According to the information processing method described above, the HRV measured while user 1 is asleep and when user 1 is in a designated sleep phase is extracted as the representative HRV. By using an appropriate HRV measured under certain conditions in this way, it is possible to grasp user 1's stress, as described above.

[0142] As described with reference to FIGS. 3, 4, 13, etc., the representative HRV may include the HRV measured at the time closest to the specific time. The specific time may be the time when the lowest HR (HR ｍｉｎ For example, the HRV measured under such conditions can be extracted as an appropriate representative HRV that can be used to understand the stress of user 1.

[0143] 3 and other figures, the interval between measurements of HRV by the wearable device 3 may be longer than the interval between measurements of HR. For example, even if the timing of measurements of HRV and HR by the wearable device 3 differs in this way, it is possible to filter the HRV and extract an appropriate representative HRV.

[0144] 1 and 3 , the sleep phase may include multiple sleep phases (e.g., sleep phases f1 to f3), and the designated sleep phase may include the longest sleep phase among the multiple sleep phases. Furthermore, the sleep phase may include a REM sleep phase, a deep sleep phase, and a core sleep phase, and the designated sleep phase may include a core sleep phase. For example, the HRV in such a designated sleep phase may be extracted as an appropriate representative HRV that can be used to understand the stress of user 1.

[0145] 6 to 11 and 14, the information processing method may include presenting stress information of user 1 generated based on the representative HRV to user 1 (steps S24 to S26), thereby enabling user 1 to understand his or her own stress.

[0146] As described with reference to Figures 6 to 8 and 14, the stress information may be generated based on the transition of the representative HRV (representative HRV transition 404). The stress information may include the stress tendency of user 1. The stress information may be generated based on the result of comparing the long-term average value and short-term average value of the representative HRV. The comparison result may include the ratio of the short-term average value to the long-term average value. For example, in this manner, stress information can be generated from the extracted representative HRV.

[0147] As described with reference to FIGS. 7 to 9 and 14, the stress information is calculated by dividing the ratio r by the threshold value r ｔｈ１ (First threshold) If r≦r ｔｈ１ ), which indicates that stress is on the rise, and the ratio r is the threshold r ｔｈ２ (second threshold) or more (r ｔｈ２ ≦r), which indicates that stress is improving, and the threshold r ｔｈ１ is a value less than 1 (r ｔｈ１ <1), threshold r ｔｈ２ may be greater than 1 (1<r ｔｈ２ ). In addition, the stress information is calculated by dividing the ratio r by the threshold r ｔｈ１ is greater than the threshold r ｔｈ２ If it is smaller than (rｔｈ１ <r<r ｔｈ２ ), or the stress level may be maintained at the current level. For example, by presenting such stress information, it is possible to grasp the stress trend of the user 1.

[0148] As described with reference to Figure 9 etc., the stress information may include a graph showing a change in the representative HRV over time. The stress information may include a long-term average value and a short-term average value of the representative HRV. As described with reference to Figure 10 etc., the stress information may include a heat map of the representative HRV. As described with reference to Figure 11 etc., the stress information may include a heat map of the ratio r of the short-term average value to the long-term average value of the representative HRV. For example, stress information can be visualized and presented in various ways such as these.

[0149] The information processing device 4 described with reference to Figures 1 to 5 is also one of the disclosed technologies. The information processing device 4 includes an acquisition unit 41 that acquires measurement data 30 from a wearable device 3 that measures the HR, HRV, and sleep of a user 1, and an extraction unit 42 that extracts a representative HRV for one day from the measurement data 30. The representative HRV is an HRV measured while the user 1 is sleeping and is an HRV when the user 1 is in a designated sleep phase. As described above, such an information processing device 4 can also be used to grasp the stress of the user 1.

[0150] The information processing system 100 described with reference to Figures 1 to 5 is also one of the disclosed technologies. The information processing system 100 includes a wearable device 3 that measures the HR, HRV, and sleep of a user 1, an information processing device 4 that extracts a representative HRV for one day from measurement data 30 of the wearable device 3, and a terminal device 2 that presents stress information of the user 1 generated based on the representative HRV to the user 1. The representative HRV is an HRV measured while the user 1 is sleeping and is the HRV when the user 1 is in a designated sleep phase. As described above, such an information processing system 100 can also be used to grasp the stress of the user 1.

[0151] The effects described in this disclosure are merely examples and are not limited to the disclosed contents. Other effects may also be obtained.

[0152] Although the embodiments of the present disclosure have been described above, the technical scope of the present disclosure is not limited to the above-described embodiments, and various modifications are possible within the scope of the gist of the present disclosure. Furthermore, components of different embodiments and modifications may be combined as appropriate.

[0153] Note that the present technology can also be configured as follows. (1) An information processing method executed by a computer, comprising: acquiring measurement data from a wearable device that measures a user's HR (heart rate), HRV (heart rate variability), and sleep; and extracting a representative HRV for one day from the measurement data, wherein the representative HRV is an HRV measured while the user is sleeping and when the user's sleep phase is a designated sleep phase. (2) The information processing method described in (1), wherein the representative HRV includes an HRV measured at a time closest to a specific time. (3) The information processing method described in (2), wherein the specific time includes a time when the lowest HR was measured. (4) The information processing method described in any of (1) to (3), wherein the interval between measurements of the HRV by the wearable device is longer than the interval between measurements of the HR. (5) The information processing method according to any one of (1) to (4), wherein the sleep phases include a plurality of sleep phases, and the designated sleep phase includes the longest sleep phase of the plurality of sleep phases. (6) The information processing method according to any one of (1) to (5), wherein the sleep phases include a REM sleep phase, a deep sleep phase, and a core sleep phase, and the designated sleep phase includes the core sleep phase. (7) The information processing method according to any one of (1) to (6), comprising presenting to the user stress information of the user generated based on the representative HRV. (8) The information processing method according to (7), wherein the stress information is generated based on a transition of the representative HRV. (9) The information processing method according to (8), wherein the stress information includes a stress trend of the user. (10) The information processing method according to (9), wherein the stress information is generated based on a result of comparing a long-term average value and a short-term average value of the representative HRV. (11) The information processing method according to (10), wherein the comparison result includes a ratio of the short-term average value to the long-term average value.(12) The information processing method according to (11), wherein the stress information indicates that stress is worsening when the ratio is equal to or less than a first threshold, and indicates that stress is improving when the ratio is equal to or greater than a second threshold, the first threshold being a value smaller than 1, and the second threshold being a value larger than 1. (13) The information processing method according to (12), wherein the stress information indicates that stress is maintaining its current state when the ratio is greater than the first threshold and smaller than the second threshold. (14) The information processing method according to any of (7) to (13), wherein the stress information includes a graph showing a change in the representative HRV over time. (15) The information processing method according to any of (7) to (14), wherein the stress information includes a long-term average value and a short-term average value of the representative HRV. (16) The information processing method according to any of (9) to (15), wherein the stress information includes a heat map of the representative HRV. (17) The information processing method according to any one of (9) to (16), wherein the stress information includes a heat map of a ratio of a short-term average value to a long-term average value of the representative HRV. (18) An information processing device comprising: an acquisition unit that acquires measurement data from a wearable device that measures a user's HR, HRV, and sleep; and an extraction unit that extracts a representative HRV for one day from the measurement data, wherein the representative HRV is an HRV measured while the user is sleeping and is the HRV when the user's sleep phase is a designated sleep phase. (19) An information processing system comprising: a wearable device that measures a user's HR, HRV, and sleep; an information processing device that extracts a representative HRV for one day from the measurement data of the wearable device; and a terminal device that presents the user with stress information of the user that has been generated based on the representative HRV, wherein the representative HRV is an HRV measured while the user is sleeping and is the HRV when the user's sleep phase is a designated sleep phase.

[0154] REFERENCE SIGNS LIST 100 Information processing system 1 User 2 Terminal device 20 Storage unit 200 Program 21 UI unit 22 Processing unit 3 Wearable device 30 Measurement data 4 Information processing device 40 Storage unit 400 Program 401 HR history 402 HRV history 403 Sleep history 404 Representative HRV transition 41 Acquisition unit 42 Extraction unit 43 Generation unit

Claims

1. An information processing method executed by a computer, comprising: acquiring measurement data from a wearable device that measures a user's HR (heart rate), HRV (heart rate variability), and sleep; and extracting a representative HRV for one day from the measurement data, wherein the representative HRV is an HRV measured while the user is sleeping and is an HRV when the user's sleep phase is a designated sleep phase.

2. The information processing method according to claim 1, wherein the representative HRV includes an HRV measured at a time closest to a specific time.

3. The information processing method according to claim 2, wherein the specific time includes the time when the lowest HR is measured.

4. The information processing method according to claim 1, wherein the interval between measurements of the HRV by the wearable device is longer than the interval between measurements of the HR.

5. The information processing method according to claim 1, wherein the sleep phase includes a plurality of sleep phases, and the designated sleep phase includes the longest sleep phase among the plurality of sleep phases.

6. The information processing method according to claim 1, wherein the sleep phases include a REM sleep phase, a deep sleep phase, and a core sleep phase, and the designated sleep phase includes the core sleep phase.

7. The information processing method according to claim 1, further comprising the step of presenting to the user stress information of the user generated based on the representative HRV.

8. The information processing method according to claim 7, wherein the stress information is generated based on a transition of the representative HRV.

9. The information processing method according to claim 8, wherein the stress information includes the user's stress tendency.

10. The information processing method according to claim 9, wherein the stress information is generated based on a comparison result between a long-term average value and a short-term average value of the representative HRV.

11. The information processing method according to claim 10, wherein the comparison result includes a ratio of the short-term average value to the long-term average value.

12. The information processing method of claim 11, wherein the stress information indicates that stress is worsening when the ratio is equal to or less than a first threshold, and indicates that stress is improving when the ratio is equal to or greater than a second threshold, the first threshold being a value smaller than 1, and the second threshold being a value larger than 1.

13. The information processing method according to claim 12, wherein the stress information indicates that stress is remaining the same when the ratio is greater than the first threshold and less than the second threshold.

14. The information processing method according to claim 7, wherein the stress information includes a graph showing a change in the representative HRV over time.

15. The information processing method according to claim 7, wherein the stress information includes a long-term average value and a short-term average value of the representative HRV.

16. The information processing method according to claim 9, wherein the stress information includes a heat map of the representative HRV.

17. The information processing method according to claim 9, wherein the stress information includes a heat map of the ratio of the short-term average value of the representative HRV to the long-term average value.

18. An information processing device comprising: an acquisition unit that acquires measurement data from a wearable device that measures a user's HR, HRV, and sleep; and an extraction unit that extracts a representative HRV for one day from the measurement data, wherein the representative HRV is the HRV measured while the user is sleeping and is the HRV when the user's sleep phase is a designated sleep phase.

19. An information processing system comprising: a wearable device that measures a user's HR, HRV, and sleep; an information processing device that extracts a representative HRV for one day from the measurement data of the wearable device; and a terminal device that presents the user with stress information of the user generated based on the representative HRV, wherein the representative HRV is the HRV measured while the user is sleeping and is the HRV when the user's sleep phase is a designated sleep phase.

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