Information presentation method and information processing system
The system addresses the lack of personalized fatigue recovery methods by using muscle stiffness, autonomic nervous system, and stress hormone data to recommend tailored recovery techniques, achieving up to 38% improvement in fatigue reduction.
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- EZAKI GLICO CO LTD
- Filing Date
- 2025-09-30
- Publication Date
- 2026-06-04
AI Technical Summary
Conventional systems fail to provide a fatigue recovery method tailored to the user's specific fatigue state among multiple recovery methods.
An information presentation method and system that acquires multiple types of biological information, including muscle stiffness, autonomic nervous system activity, and stress-related hormones, to recommend appropriate fatigue recovery methods such as low-intensity exercise, acupressure, or massage, based on predefined thresholds.
Effectively recommends personalized fatigue recovery methods that address muscle stiffness, autonomic nervous system activity, and stress-related hormone levels, enhancing fatigue recovery by 13-38% based on user-specific biometric data.
Smart Images
Figure JP2025034795_04062026_PF_FP_ABST
Abstract
Description
Information Presentation Method, Information Processing System
[0001] One example of the present invention relates to an information presentation method and an information processing system that classify a user's fatigue state and present a fatigue recovery method.
[0002] Conventionally, a system has been proposed that presents information about facilities that provide services for recovering fatigue when the user's fatigue level exceeds a threshold (for example, Patent Document 1).
[0003] Japanese Patent Application Laid-Open No. 2015-195014
[0004] However, the above conventional technology does not propose a fatigue recovery method suitable for the user's fatigue state from among a plurality of fatigue recovery methods.
[0005] Therefore, one object of the present invention is to provide an information presentation method and an information processing system that propose a fatigue recovery method suitable for the user's fatigue state from among a plurality of fatigue recovery methods.
[0006] In order to solve the above problems, the present invention adopts the following configuration.
[0007] (First Configuration) The information presentation method according to the first configuration of the present invention includes a first acquisition step of acquiring first type of biological information related to the user's fatigue, and at least based on the first type of biological information, a recommendation step of recommending any one of a plurality of fatigue recovery methods including a first fatigue recovery method and a second fatigue recovery method.
[0008] According to the above, an appropriate fatigue recovery method can be recommended based on the user's biological information.
[0009] (Second Configuration) The second configuration may further include a second acquisition step of acquiring a second type of biological information different from the first type of biological information regarding the user's fatigue in the first configuration. In the recommendation step, any one of the plurality of fatigue recovery methods may be recommended based on the first type of biological information and the second type of biological information.
[0010] Based on the above, it is possible to recommend an appropriate fatigue recovery method based on the first type of biometric information and the second type of biometric information.
[0011] (Third configuration) The third configuration is that in the second configuration, the first type of biological information is information relating to muscle stiffness, and the second type of biological information is information relating to the autonomic nervous system.
[0012] Based on the above, it is possible to recommend appropriate fatigue recovery methods based on information regarding muscle stiffness and autonomic nervous system function.
[0013] (Fourth configuration) The fourth configuration is that, in the second configuration, the first type of biological information is information relating to muscle stiffness, and the second type of biological information is information relating to stress hormones.
[0014] Based on the above, it is possible to recommend appropriate fatigue recovery methods based on information regarding muscle stiffness and stress-related hormones.
[0015] (Fifth configuration) The fifth configuration may further include a third acquisition step in the second configuration for acquiring a third type of biometric information relating to the user's fatigue. In the recommendation step, any of the plurality of fatigue recovery methods may be recommended based on the first type of biometric information, the second type of biometric information, and the third type of biometric information. The first type of biometric information may be information relating to muscle stiffness, the second type of biometric information may be information relating to the autonomic nervous system, and the third type of biometric information may be information relating to stress-related hormones.
[0016] Based on the above, it is possible to recommend appropriate fatigue recovery methods based on information regarding muscle stiffness, autonomic nervous system function, and stress-related hormones.
[0017] (Sixth configuration) In the fifth configuration, the first fatigue recovery method may include low-intensity exercise, and the second fatigue recovery method may include acupressure or massage. In the recommendation step, if the first type of biological information is greater than or equal to a first threshold and less than a second threshold, the first fatigue recovery method is recommended; if the first type of biological information is greater than or equal to the second threshold, the first fatigue recovery method and / or the second fatigue recovery method is recommended; if the second type of biological information is less than or equal to a third threshold, the second fatigue recovery method is recommended; and if the third type of biological information is greater than or equal to a fourth threshold, the second fatigue recovery method is recommended.
[0018] Based on the above, appropriate fatigue recovery methods can be recommended based on thresholds for muscle stiffness, autonomic nervous system activity, and stress-related hormones.
[0019] (Seventh configuration) In the seventh configuration, the first type of biological information may be information relating to muscle stiffness, and the second type of biological information may be information relating to the autonomic nervous system. The first fatigue recovery method may include low-intensity exercise, and the second fatigue recovery method may include acupressure or massage. In the recommendation step, if the first type of biological information is greater than or equal to a first threshold and less than a second threshold, the first fatigue recovery method may be recommended; if the first type of biological information is greater than or equal to the second threshold, the first fatigue recovery method and / or the second fatigue recovery method may be recommended; and if the second type of biological information is less than or equal to a third threshold, the second fatigue recovery method may be recommended.
[0020] Based on the above, appropriate fatigue recovery methods can be recommended based on the respective thresholds for muscle stiffness and autonomic nervous system information.
[0021] (Eighth configuration) In any of the first to fifth configurations, the first fatigue recovery method may include low-intensity exercise, and the second fatigue recovery method may include acupressure or massage.
[0022] Based on the above, low-intensity exercise and acupressure or massage can be recommended as methods for recovering from fatigue.
[0023] (The ninth configuration) The ninth configuration may be one of the first to fifth configurations, wherein the first fatigue recovery method includes low-intensity exercise and the intake of food or drink containing the first component, and the second fatigue recovery method may include acupressure or massage and the intake of food or drink containing the second component.
[0024] Based on the above, as a method of recovering from fatigue, we can recommend low-intensity exercise, acupressure or massage, and the consumption of appropriate foods and beverages.
[0025] (Tenth configuration) The information presentation method relating to the tenth configuration comprises a first acquisition step of acquiring a first type of biological information relating to the user's fatigue, and a classification step of classifying the user's fatigue state into one of a plurality of states based on at least the first type of biological information.
[0026] Based on the above, it is possible to classify the user's fatigue level.
[0027] Another invention may be an information processing system that performs the above-described information presentation method.
[0028] According to one example of the present invention, an appropriate fatigue recovery method can be recommended based on the user's biometric information.
[0029] Figure 1 shows an example of the functional configuration of Recommend System 1. Figure 2 shows the average change in muscle stiffness of multiple users before and after treatment of the fatigue recovery method. Figure 3 shows the average change in CVR-R value of multiple users before and after treatment of the fatigue recovery method. Figure 4 shows the average change in ccvTP of multiple users before and after treatment of the fatigue recovery method. Figure 5 shows the average change in cortisol value of multiple users before and after treatment of the fatigue recovery method. A flowchart showing an example of the processing performed in Recommend System 1 of this embodiment. A flowchart showing an example of the classification processing in step S2.
[0030] (Example of Recommendation System Configuration) The recommendation system 1 according to this embodiment will be described below with reference to the drawings. Figure 1 is a diagram showing an example of the functional configuration of the recommendation system 1. The recommendation system 1 acquires one or more biometric information of the user, classifies the user's fatigue state into one of several states based on the acquired biometric information, and recommends one of several fatigue recovery methods according to the classified fatigue state.
[0031] As shown in Figure 1, the recommendation system 1 comprises an information acquisition unit 10, a classification unit 11, and a recommendation unit 12. The recommendation system 1 also comprises, for example, a processor such as a CPU or GPU, memory (e.g., DRAM, SRAM), auxiliary storage device (e.g., HDD or SSD), input device (e.g., keyboard or pointing device), and output device (e.g., monitor). A predetermined information processing program is stored in the auxiliary storage device. The processor executes the predetermined information processing program using the memory, thereby realizing each part shown in Figure 1.
[0032] The information acquisition unit 10 acquires first to third biometric information as the user's biometric information. Specifically, the information acquisition unit 10 acquires first to third biometric information entered using an input device. For example, the recommendation system 1 may display a predetermined input screen, and the user may input first to third biometric information on that input screen, thereby acquiring first to third biometric information. Alternatively, a measuring instrument for measuring biometric information may be connected to the recommendation system 1, and each biometric information may be acquired based on data output from each measuring instrument.
[0033] The first biological information is, for example, information regarding muscle stiffness. The information regarding muscle stiffness is the muscle stiffness of a predetermined part of the user measured by a predetermined muscle stiffness measuring device. The predetermined muscle stiffness measuring device is a device that outputs a muscle stiffness value indicating the stiffness of the user's muscles. For example, the predetermined muscle stiffness measuring device comprises a measuring probe and a spring, and is configured such that when force is applied to the measuring probe, the measuring probe moves and the spring compresses. The predetermined muscle stiffness measuring device outputs a muscle stiffness value corresponding to the amount of movement of the measuring probe when the measuring probe is pressed against the predetermined part of the user. The predetermined muscle stiffness measuring device outputs a numerical value, for example, from 0 to 100, as the muscle stiffness value. For example, the "TDM-Z2" manufactured by Triall Co., Ltd. may be used as the predetermined muscle stiffness measuring device. The predetermined part of the user's body where the muscle stiffness is measured is, for example, the user's shoulder, and may be near the midpoint between the acromion and the 7th cervical vertebra. Note that the predetermined part is not limited to the shoulder. For example, the user's back, lower back, buttocks, thighs, calves, lower legs, etc., may be designated as specific areas, and muscle stiffness may be measured in these areas. The designated areas may also be determined depending on the type of fatigue to be measured.
[0034] Furthermore, the second type of biometric information is, for example, information about the user's autonomic nervous system. This information is obtained based on pulse waves and electrocardiograms (ECGs), and includes values such as TP (Total Power), LF, HF, LF / HF, CVR-R, and ccvTP. TP is a value representing the activity level of autonomic nervous system function, LF / HF is a value related to the balance between the sympathetic and parasympathetic nervous systems, CVR-R is an index related to parasympathetic nervous system activity, and ccvTP is a value obtained by correcting TP for heart rate.
[0035] Furthermore, the third biological information is, for example, information on stress-related hormones, and is a value that changes due to exercise or stress (mental stress or physical pain). In this embodiment, the information on stress-related hormones is the cortisol level. Cortisol is a type of hormone secreted from the adrenal cortex and is found, for example, in saliva. Cortisol levels rise in response to acute stress, for example. Alternatively, the value of amylase contained in saliva may be obtained as information on stress-related hormones. In addition, values of adrenocorticotropic hormone, prolactin, vasopressin, oxytocin, renin, adrenaline, noradrenaline, etc. may be obtained as information on stress-related hormones.
[0036] The classification unit 11 classifies the user's fatigue state into one of several states based on the first to third biological information acquired by the information acquisition unit 10. In this embodiment, the classification unit 11 classifies the user's fatigue state into states 0 to 3.
[0037] The recommendation unit 12 recommends one of several fatigue recovery methods, including a first fatigue recovery method and a second fatigue recovery method, according to the classified fatigue state. The fatigue recovery method recommended by the recommendation unit 12 is output as an image, text, audio, etc.
[0038] The first fatigue recovery method is, for example, low-intensity exercise such as yoga or stretching. Specifically, the first fatigue recovery method includes multiple low-intensity exercises. For example, the first fatigue recovery method may include exercises such as raising both arms, rotating the shoulders, stretching the sides of the torso, bending the knees, and bending forward while the user is standing.
[0039] Furthermore, the first fatigue recovery method may include the user consuming food or drink containing the first component after performing multiple low-intensity exercises, during low-intensity exercises, or before performing low-intensity exercises. For example, the first component may be citric acid, branched-chain amino acids (valine, leucine, isoleucine), B vitamins (niacin, pantothenic acid, vitamin B1, vitamin B2, vitamin B6, vitamin B12, folic acid), vitamin C, etc.
[0040] The second method of fatigue recovery is, for example, acupressure or massage. The second method of fatigue recovery includes multiple treatments, including gentle acupressure or massage from the head to the shoulders. For example, the second method of fatigue recovery may include multiple types of facial massage, multiple types of head massage, multiple types of neck and shoulder massage, etc.
[0041] Furthermore, the second fatigue recovery method may include the user consuming food or drink containing the second component after, during, or before receiving acupressure or massage treatment. For example, the second component may be glycosylated hesperidin.
[0042] Next, we will explain in detail the classification of users' fatigue states and the recommendation of fatigue recovery methods according to those classifications. The applicant obtained biological information from a large number of subjects before and after the implementation of multiple fatigue recovery methods (a few minutes to several tens of minutes after the completion of the method), and investigated how the biological information changed before and after the implementation of the fatigue recovery method.
[0043] Figure 2 shows the average change in muscle stiffness of multiple users before and after treatment with the first fatigue recovery method. Figure 3 shows the average change in muscle stiffness of multiple users before and after treatment with the second fatigue recovery method. In Figures 2 and 3, multiple subjects are divided into three groups: one with relatively low muscle stiffness before treatment, one with moderate muscle stiffness before treatment, and one with relatively high muscle stiffness before treatment. The change in muscle stiffness before and after treatment with the first and second fatigue recovery methods is shown for each group.
[0044] As shown in Fig. 2, for the group of people with relatively low muscle hardness before the treatment, no decrease in muscle hardness was observed before and after the treatment of the first fatigue recovery method. On the contrary, the muscle hardness increased. On the other hand, the average of the muscle hardness before the treatment of the group of people with medium muscle hardness before the treatment was approximately "20 - 21", and the average of the muscle hardness after the treatment of the first fatigue recovery method for the people in this group was approximately "17 - 18". That is, for the group of people with medium muscle hardness before the treatment, the muscle hardness decreased by about 15% after the treatment of the first fatigue recovery method. Also, the average of the muscle hardness before the treatment of the group of people with relatively high muscle hardness before the treatment was approximately "30 - 31", and the average of the muscle hardness after the treatment of the first fatigue recovery method for the people in this group was approximately "26 - 27". That is, for the group of people with relatively high muscle hardness before the treatment, the muscle hardness decreased by about 13% after the treatment of the first fatigue recovery method. From this, it can be said that the first fatigue recovery method is effective for people with medium or higher muscle hardness.
[0045] Also, as shown in Fig. 3, for the group of people with relatively low muscle hardness before the treatment, no decrease in muscle hardness was observed before and after the treatment of the second fatigue recovery method. On the contrary, the muscle hardness increased. Also, for the group of people with medium muscle hardness before the treatment, there was no change in muscle hardness before and after the treatment of the second fatigue recovery method. On the other hand, the average of the muscle hardness before the treatment of the group of people with relatively high muscle hardness before the treatment was approximately "28 - 29", and the average of the muscle hardness after the treatment of the second fatigue recovery method for the people in this group was approximately "23 - 24". That is, for the group of people with relatively high muscle hardness before the treatment, the muscle hardness decreased by about 18% after the treatment of the second fatigue recovery method. From this, it can be said that the second fatigue recovery method is effective for people with high muscle hardness.
[0046] Fig. 4 is a diagram showing the change in the average of the CVR - R values of multiple users before and after the treatment of the second fatigue recovery method. Fig. 5 is a diagram showing the change in the average of the ccvTP of multiple users before and after the treatment of the second fatigue recovery method.
[0047] In Fig. 4, a plurality of subjects are divided into a group with a relatively low TP value before the implementation of the second fatigue recovery method, a group with a medium TP value before the implementation, and a group with a relatively high TP value before the implementation. The changes in the CVR-R values before and after the implementation of the second fatigue recovery method are shown for each group. Also, in Fig. 5, a plurality of subjects are divided into a group with a relatively low TP value before the implementation of the second fatigue recovery method, a group with a medium TP value before the implementation, and a group with a relatively high TP value before the implementation. The changes in the ccvTP values before and after the implementation of the second fatigue recovery method are shown for each group.
[0048] As shown in Fig. 4, the average of the CVR-R values before the implementation for the group of people with a relatively low TP value before the implementation is approximately "2.3 - 2.4", and the average of the CVR-R values after the implementation of the second fatigue recovery method for the people in this group is approximately "3.8 - 3.9". That is, the CVR-R value has increased by approximately 65% after the implementation of the second fatigue recovery method. On the other hand, for the group of people with a medium or higher TP value before the implementation, there is almost no change in the CVR-R value before and after the implementation of the second fatigue recovery method.
[0049] Also, as shown in Fig. 5, the average of the ccvTP values before the implementation for the group of people with a relatively low TP value before the implementation is approximately "1.5 - 1.6", and the average of the ccvTP values after the implementation of the second fatigue recovery method for the people in this group is approximately "2.5 - 2.6". That is, the ccvTP value has increased by approximately 66% after the implementation of the second fatigue recovery method. On the other hand, for the group of people with a medium or higher TP value before the implementation, there is almost no change in the ccvTP value before and after the implementation of the second fatigue recovery method.
[0050] Regarding the first fatigue recovery method, no such changes were observed before and after the implementation.
[0051] From this, it can be said that the second fatigue recovery method is effective for people with a relatively low TP value.
[0052] Fig. 6 is a diagram showing the change in the average cortisol value of a plurality of users before and after the implementation of the second fatigue recovery method.
[0053] Figure 6 shows the changes in cortisol levels before and after treatment with the second fatigue recovery method for each group, divided into three groups: one with relatively low cortisol levels before treatment, one with moderate cortisol levels before treatment, and one with relatively high cortisol levels before treatment.
[0054] As shown in Figure 6, in the group with relatively low cortisol levels before treatment, there was almost no change in cortisol levels after treatment with the second fatigue recovery method. On the other hand, the average cortisol level before treatment for people with moderate cortisol levels was approximately "0.18 to 0.19 (μg / dL)", and the average cortisol level after treatment with the second fatigue recovery method for people in this group was approximately "0.14 to 0.15". In other words, cortisol levels decreased by approximately 22% after treatment with the second fatigue recovery method. Furthermore, the average cortisol level before treatment for people with relatively high cortisol levels before treatment was approximately "0.36 to 0.37", and the average cortisol level after treatment with the second fatigue recovery method for people in this group was approximately "0.22 to 0.23". In other words, cortisol levels decreased by approximately 38% after treatment with the second fatigue recovery method.
[0055] Regarding the first method of fatigue recovery, no such changes were observed before and after the treatment.
[0056] Therefore, it can be said that the second fatigue recovery method is effective for people with moderate or above-average cortisol levels.
[0057] Next, the details of the processing in the recommendation system 1 of this embodiment will be described. Figure 7 is a flowchart showing an example of the processing performed in the recommendation system 1 of this embodiment.
[0058] As shown in Figure 7, the information acquisition unit 10 of the recommendation system 1 first acquires first to third biological information (step S1). For example, the information acquisition unit 10 may acquire this information based on input to the user's input device, or it may acquire this information based on data output from a measuring instrument. Specifically, the information acquisition unit 10 acquires the muscle stiffness value as the user's first biological information, a value related to the autonomic nervous system (specifically, the TP value) as the second biological information, and a value related to stress (specifically, the cortisol value) as the third biological information.
[0059] Next, the classification unit 11 of the recommendation system 1 performs a classification process (step S2). The classification process classifies the user's fatigue state based on the acquired first to third biometric information. The classification process will be explained with reference to Figure 8. Figure 8 is a flowchart showing an example of the classification process in step S2.
[0060] As shown in Figure 8, the classification unit 11 determines whether the acquired muscle stiffness is equal to or greater than the first threshold (step S21). The first threshold for muscle stiffness is a value that indicates moderate muscle stiffness, and may be, for example, "18".
[0061] If the muscle stiffness is equal to or greater than the first threshold (step S21: YES), the classification unit 11 determines whether or not the muscle stiffness is equal to or greater than the second threshold (step S22). The second threshold for muscle stiffness is a value that indicates high muscle stiffness and is a larger value than the first threshold. For example, the second threshold may be "23".
[0062] If the muscle stiffness is above the second threshold (step S22: YES), the classification unit 11 classifies the user's fatigue state into a third state (step S23).
[0063] If the muscle stiffness is not above the second threshold (step S22: NO), the classification unit 11 classifies the user's fatigue state into the first state (step S24).
[0064] If NO is determined in step S21, if the process in step S23 is performed, or if the process in step S24 is performed, the classification unit 11 determines whether the TP value related to the autonomic nervous system is below the third threshold (step S25). The third threshold related to the autonomic nervous system is a value that indicates a low level of autonomic nervous system activity, for example, "290 to 310 (ms) ２ It could also be set to "295", for example.
[0065] If the TP value is below the third threshold (step S25: YES), the classification unit 11 classifies the user's fatigue state into the second state (step S26).
[0066] If the result in step S25 is NO, or if the process in step S26 is performed, the classification unit 11 determines whether the user's stress-related cortisol level is above the fourth threshold (step S27). The fourth threshold for the stress index is a value that indicates that the stress is moderate or above, and may be, for example, "0.14 (μg / dL)".
[0067] If the cortisol level is above the fourth threshold (step S27: YES), the classification unit 11 classifies the user's fatigue state into the second state (step S28).
[0068] If the result in step S27 is NO, or if the process in step S28 is performed, the classification unit 11 terminates the process shown in Figure 8 and returns the process to Figure 7. If, in the processes from steps S21 to S28, the classification unit 11 does not classify the user's fatigue state into any of the first to third states, it classifies the user's fatigue state into the zeroth state, which indicates that the user is not fatigued.
[0069] Returning to Figure 7, the recommendation unit 12 performs recommendation processing after the classification processing in step S2 (step S3). Specifically, the recommendation unit 12 recommends one of several fatigue recovery methods to the user according to the user's fatigue state classified in the classification processing. For example, if the user's fatigue state is classified as a first state in the classification processing, the recommendation unit 12 recommends the first fatigue recovery method. If the user's fatigue state is classified as a third state in the classification processing, the recommendation unit 12 recommends both the first and second fatigue recovery methods. If the user's fatigue state is classified as a third state in the classification processing, the recommendation unit 12 may recommend either the first or second fatigue recovery method. For example, if the user's fatigue state is classified as the third state, the recommendation unit 12 may recommend only the second fatigue recovery method or only the first fatigue recovery method from the two fatigue recovery methods. Also, if the user's fatigue state is classified as the second state during the classification process, the recommendation unit 12 will recommend the second fatigue recovery method. If the user's fatigue state is classified as the zeroth state during the classification process, the recommendation unit 12 will not recommend any fatigue recovery method.
[0070] Furthermore, the classification unit 11 may prioritize any of the first to third biological information in the classification process to classify the user's fatigue state. For example, the classification unit 11 may prioritize the second biological information (TP value) over the third biological information (cortisol value) to classify the user's fatigue state. When the second biological information is prioritized over the third biological information, if the TP value is below the third threshold (YES in step S25) and the cortisol value is not above the fourth threshold (NO in step S27), the classification unit 11 classifies the user's fatigue state into the second state. Also, when the second biological information is prioritized over the third biological information, if the TP value is not below the third threshold (NO in step S25) and the cortisol value is above the fourth threshold (YES in step S27), the classification unit 11 may classify the user's fatigue state into the second state. Furthermore, the classification unit 11 may prioritize the first biological information over the second and third biological information to classify the user's fatigue state. For example, even if the user is classified into a second state based on second and / or third biological information, if they are classified into a first or third state based on first biological information, the classification unit 11 may prioritize the classification based on first biological information and classify the user's fatigue state into a first or third state. The priority of biological information may be reversed as described above. Then, according to the user's fatigue state classified by the classification unit 11, the recommendation unit 12 may recommend a fatigue recovery method. This concludes the explanation of Figure 7.
[0071] The order of processes, content, and thresholds used for determination in the flowchart above are merely examples and may be changed as appropriate. Furthermore, some or all of the processes described above may be performed by a dedicated circuit provided by the recommendation system 1, or by a general-purpose processor.
[0072] As described above, in this embodiment, multiple types of biometric information of the user are acquired, and based on the acquired biometric information, the user's fatigue state is classified into one of several states, and a fatigue recovery method corresponding to the classification is recommended. This makes it possible to recommend an appropriate fatigue recovery method according to the user's fatigue state, and to efficiently recover from the user's fatigue.
[0073] Furthermore, in the above embodiment, since multiple types of biological information are acquired, the user's fatigue state can be classified in detail, and fatigue recovery methods can be proposed according to the type of fatigue.
[0074] Furthermore, in the above embodiment, multiple types of biological information are acquired, including muscle stiffness, information related to the autonomic nervous system, and cortisol levels. This allows for the classification of the user's fatigue state in a simple manner.
[0075] Furthermore, in the above embodiment, the first fatigue recovery method includes multiple types of low-intensity exercise and the intake of food or beverages containing the first component. The second fatigue recovery method includes multiple types of acupressure or massage treatments and the intake of food or beverages containing the second component. This can enhance the fatigue recovery effect.
[0076] (Modifications) The above embodiment has been described, but the above embodiment is merely an example, and modifications such as the following may be made.
[0077] For example, in the above embodiment, the user's fatigue state is classified based on the acquired first to third biometric information, and one of several fatigue recovery methods is recommended according to the classified state. In other embodiments, the user's fatigue state may be classified based only on the first biometric information, and one of several fatigue recovery methods may be recommended according to the classified state.
[0078] Furthermore, in the above embodiment, muscle stiffness of a part of the user's body (for example, the shoulder) is acquired as first biological information, the user's fatigue state is classified based on the muscle stiffness of that part, and one of several fatigue recovery methods is recommended according to the classified state. In other embodiments, multiple first biological information items indicating muscle stiffness of multiple parts of the user are acquired, the user's fatigue state is classified based on the multiple first biological information items, and one of several fatigue recovery methods is recommended according to the classified state.
[0079] In other embodiments, four or more biometric data points may be acquired, the user's fatigue state may be classified based on the acquired biometric data, and one of several fatigue recovery methods may be recommended according to the classified state. The biometric data is not limited to measurements from a biometric data measuring device, but may also include, for example, information from the user's responses to a questionnaire regarding their fatigue level.
[0080] Furthermore, in the above embodiment, the first fatigue recovery method and / or the second fatigue recovery method were recommended, but in other embodiments, any of the three or more fatigue recovery methods may be recommended.
[0081] Furthermore, in the above embodiment, the user's fatigue state is classified into one of the first to third states based on the first to third biological information, and a first fatigue recovery method and / or a second fatigue recovery method are recommended according to the classified state. In other embodiments, the user's fatigue state may be classified into four or more categories.
[0082] In other embodiments, depending on the user's fatigue state, some of the fatigue recovery methods included in the first fatigue recovery method may be recommended, or some of the fatigue recovery methods included in the second fatigue recovery method may be recommended. In other embodiments, depending on the user's fatigue state, a fatigue recovery method combining some of the fatigue recovery methods included in the first fatigue recovery method and some of the fatigue recovery methods included in the second fatigue recovery method may be recommended.
[0083] Furthermore, in the above embodiment, the user is classified into a first or third state based on first biological information (e.g., information on muscle stiffness), and into a second state based on second biological information (e.g., information on the autonomic nervous system) and third biological information (e.g., information on stress). In other embodiments, the user's fatigue state may be classified into one of several states for each piece of biological information. For example, the user may be classified into one of states A1 to Ax based on first biological information, one of states B1 to By based on second biological information, and one of states C1 to Cz based on third biological information (where x, y, and z are positive integers). Then, one of several fatigue recovery methods may be recommended according to these classifications.
[0084] Furthermore, a one-to-one correspondence may be established between classifications based on biological information and fatigue recovery methods, or an n-to-m correspondence may be established between classifications and fatigue recovery methods (where n and m are positive integers). m may be a number smaller than n or a number larger than n.
[0085] In other embodiments, in addition to one or more biometric data, user attribute information such as height, weight, age, sex, body fat percentage, muscle mass, body temperature, and body surface temperature may be acquired. Based on one or more biometric data related to fatigue and the user's attribute information, the user's fatigue state may be classified, and one of several fatigue recovery methods may be recommended according to the classified state. In addition to the above, the user's attribute information may include, for example, the user's exercise habits and eating habits. Depending on the user's attribute information, the reference values for biometric data (values of biometric data when not fatigued) may differ, and thresholds may be set for each of the multiple biometric data according to the user's attribute information. For example, the first and second thresholds for muscle stiffness may differ depending on age. For example, the first threshold for muscle stiffness for a user in their teens may be different from the first threshold for muscle stiffness for a user in their forties. Also, the first and second thresholds for muscle stiffness may differ depending on sex. The same applies to the third threshold for autonomic nervous system function and the fourth threshold for stress.
[0086] Furthermore, in the above embodiment, a threshold is set for each of the multiple biometric information items, the user's fatigue state is classified based on the set thresholds, and a fatigue recovery method corresponding to the classified state is recommended. In other embodiments, for example, the user's fatigue state may be classified and a fatigue recovery method corresponding to the classified state may be recommended by inputting multiple biometric information items into a trained model generated by machine learning. For example, a trained model may be generated using training data that associates multiple biometric information items with the user's fatigue state. Alternatively, a trained model may be generated using training data that associates multiple biometric information items, user attribute information, and the user's fatigue state. When one or more biometric information items of the user and the user's attribute information are input to such a trained model, a classification of the user's fatigue state may be output. Then, a fatigue recovery method corresponding to the output fatigue state may be recommended.
[0087] Furthermore, the recommendation system 1 described above may consist of a single device or multiple devices capable of communicating with each other. For example, such a system may be composed of multiple information processing devices connected to a network such as the Internet.
[0088] Furthermore, the configurations of the above embodiments and their modified forms can be combined in any way, as long as they do not contradict each other. Also, the above is merely an example of the present invention, and various other improvements and modifications may be made.
[0089] 1 Recommendation System 10 Information Acquisition Unit 11 Classification Unit 12 Recommendation Unit
Claims
1. An information presentation method comprising: a first acquisition step of acquiring a first type of biometric information relating to a user's fatigue; and a recommendation step of recommending one of a plurality of fatigue recovery methods, including a first fatigue recovery method and a second fatigue recovery method, based on at least the first type of biometric information.
2. The information presentation method according to claim 1, further comprising a second acquisition step of acquiring a second type of biometric information different from the first type of biometric information relating to the user's fatigue, wherein the recommendation step recommends any of the plurality of fatigue recovery methods based on the first type of biometric information and the second type of biometric information.
3. The information presentation method according to claim 2, wherein the first type of biological information is information relating to muscle stiffness, and the second type of biological information is information relating to the autonomic nervous system.
4. The information presentation method according to claim 2, wherein the first type of biological information is information relating to muscle stiffness, and the second type of biological information is information relating to stress-related hormones.
5. The information presentation method according to claim 2, further comprising a third acquisition step of acquiring a third type of biological information relating to the user's fatigue, wherein the recommendation step recommends any of the plurality of fatigue recovery methods based on the first type of biological information, the second type of biological information, and the third type of biological information, wherein the first type of biological information is information relating to muscle stiffness, the second type of biological information is information relating to the autonomic nervous system, and the third type of biological information is information relating to stress-related hormones.
6. The information presentation method according to claim 5, wherein the first fatigue recovery method includes low-intensity exercise, the second fatigue recovery method includes acupressure or massage, and in the recommendation step, if the first type of biological information is greater than or equal to a first threshold and less than a second threshold, the first fatigue recovery method is recommended; if the first type of biological information is greater than or equal to a second threshold, the first fatigue recovery method and / or the second fatigue recovery method is recommended; if the second type of biological information is less than or equal to a third threshold, the second fatigue recovery method is recommended; and if the third type of biological information is greater than or equal to a fourth threshold, the second fatigue recovery method is recommended.
7. The information presentation method according to claim 2, wherein the first type of biological information is information relating to muscle stiffness, the second type of biological information is information relating to the autonomic nervous system, the first fatigue recovery method includes low-intensity exercise, the second fatigue recovery method includes acupressure or massage, and in the recommendation step, if the first type of biological information is greater than or equal to a first threshold and less than a second threshold, the first fatigue recovery method is recommended, if the first type of biological information is greater than or equal to the second threshold, the first fatigue recovery method and / or the second fatigue recovery method is recommended, and if the second type of biological information is less than or equal to a third threshold, the second fatigue recovery method is recommended.
8. The information presentation method according to any one of claims 1 to 5, wherein the first fatigue recovery method includes low-intensity exercise, and the second fatigue recovery method includes acupressure or massage.
9. The information presentation method according to any one of claims 1 to 5, wherein the first fatigue recovery method comprises low-intensity exercise and ingestion of food or beverage containing the first component, and the second fatigue recovery method comprises acupressure or massage and ingestion of food or beverage containing the second component.
10. An information presentation method comprising: a first acquisition step of acquiring a first type of biological information relating to a user's fatigue; and a classification step of classifying the user's fatigue state into one of a plurality of states based on at least the first type of biological information.
11. An information processing system comprising: a first acquisition means for acquiring a first type of biometric information relating to a user's fatigue; and a recommendation means for recommending at least one of a plurality of fatigue recovery methods, including a first fatigue recovery method and a second fatigue recovery method, based on at least the first type of biometric information.
12. An information processing system comprising: a first acquisition means for acquiring a first type of biological information relating to a user's fatigue; and a classification means for classifying the user's fatigue state into one of a plurality of states based on at least the first type of biological information.