Information processing system and information processing method

Abstract

Provided are an information processing system and an information processing method that can easily improve motivation to use an exercise support system. [Solution] The information processing system includes an acquisition unit that acquires user data related to the user, a generation unit that generates advice according to the user data based on the results of a comparison between reference data indicating a standard for exercise status and exercise data indicating the user's exercise status, and a presentation unit that presents the advice.

Description

[Technical Field]

[0001] The present disclosure relates to an information processing system and an information processing method. [Background technology]

[0002] Conventionally, systems that perform predetermined processing in response to exercise by a user are known. For example, Patent Document 1 discloses a system configured to monitor a user's form while performing an exercise and calculate an estimated energy expenditure of the user performing the exercise based on the type of exercise and the user's form. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Special Publication No. 2014-502527 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the system disclosed in Cited Document 1 leaves room for improvement in the content of the feedback provided to the user. For example, the system disclosed in Cited Document 1 is limited to providing advice for a single exercise session, and does not provide feedback tailored to each individual. As a result, users are unlikely to be motivated to continue using the system.

[0005] The present disclosure has been made in view of the above-mentioned problems, and an object of the present disclosure is to provide an information processing system and an information processing method that can easily improve motivation to use an exercise support system. [Means for solving the problem]

[0006] An information processing system according to one embodiment of the present disclosure includes an acquisition unit that acquires user data related to a user, a generation unit that generates advice according to the user data based on a comparison result between reference data indicating a standard for exercise performance and exercise data indicating the user's exercise performance, and a presentation unit that presents the advice.

[0007] An information processing method of one embodiment of the present disclosure is an information processing method executed by an information processing system, which acquires user data about a user, generates advice corresponding to the user data based on the results of a comparison between reference data indicating a standard for exercise performance and exercise data indicating the user's exercise performance, and presents the advice. [Effects of the Invention]

[0008] According to the present disclosure, it is possible to provide an information processing system and an information processing method that can easily improve motivation to use an exercise support system. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a functional block diagram illustrating an example of an information processing system according to an embodiment. [Figure 2] FIG. 2 is a schematic diagram showing the appearance of the information processing system of FIG. [Figure 3] 2 is a flowchart showing an example of processing executed by the information processing system of FIG. 1; [Figure 4] FIG. 2 is a diagram showing an example of an image displayed on a display unit. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In the drawings, identical or equivalent components are denoted by the same reference numerals, and redundant descriptions of identical or equivalent components will be omitted as appropriate.

[0011] FIG. 1 is a functional block diagram showing an example of an information processing system 1 according to an embodiment. FIG. 2 is a schematic diagram showing the appearance of the information processing system 1 of FIG. 1. The information processing system 1 may be configured by one device, or may be configured by multiple devices working together. The information processing system 1 supports exercise by a user. That is, the user uses the information processing system 1 when exercising.

[0012] As shown in FIG. 1, the information processing system 1 includes a display unit 11, an imaging unit 12, a sensor unit 13, a memory unit 14, an input unit 15, a control unit 16, an acquisition unit 17, a motion analysis unit 18, a generation unit 19, and a communication unit 20.

[0013] The display unit 11 includes a display device such as a liquid crystal display (LCD), an organic electroluminescence panel (OLED), or an inorganic electroluminescence panel. The display unit 11 displays various information under the control of the control unit 16. For example, the display unit 11 displays a demonstration video of an exercise performed by the user. The demonstration video is used as a model showing how the exercise should be performed. In this case, the user performs the same movements (exercises) as the demonstration video, for example, while watching the demonstration video. The display unit 11 may also display a video of the user's own exercise, captured by the imaging unit 12, for example. The display unit 11 may also display advice to the user as feedback regarding the user's exercise. In this embodiment, the display unit 11 functions as a presentation unit that presents advice to the user.

[0014] The imaging unit 12 is a device capable of capturing video, and specifically, for example, a camera. The imaging unit 12 is disposed in a position where it can capture an image of the user exercising. For example, the imaging unit 12 is disposed in a position where it can capture an image of the side toward which the display screen of the display unit 11 is facing. As an example, in the example shown in FIG. 2, the imaging unit 12 is disposed above the display unit 11, facing the same side as the side toward which the display screen of the display unit 11 is facing.

[0015] The sensor unit 13 is configured to include various sensors. The sensors can be sensors capable of acquiring information about the environment in which the user is exercising, or sensors capable of acquiring information about the user exercising. The information about the environment in which the user is exercising is, for example, temperature or humidity. In this case, the sensor unit 13 includes a thermometer or a hygrometer. The information about the user exercising is, for example, vital data of the user. The vital data includes, for example, body temperature, blood pressure, or heart rate. In this case, the sensor unit 13 includes a sensor capable of acquiring vital data.

[0016] The various sensors included in the sensor unit 13 may be configured in any suitable manner. For example, the various sensors may be non-contact sensors. Alternatively, the various sensors may be wearable sensors that can acquire various information by being worn by a user.

[0017] The storage unit 14 is a storage medium capable of storing programs and data. The storage unit 14 can be configured, for example, with a semiconductor memory or a magnetic memory. Specifically, the storage unit 14 can be configured, for example, with an EEPROM (Electrically Erasable Programmable Read-Only Memory). The storage unit 14 may store, for example, programs for operating the control unit 16, the acquisition unit 17, the motion analysis unit 18, and the generation unit 19.

[0018] The input unit 15 is a mechanism capable of receiving operation input from a user to the information processing system 1. The input unit 15 is configured, for example, by a group of operation buttons or a keyboard provided on a device constituting the information processing system 1. Furthermore, if a touch sensor is provided on the display unit 11, the touch sensor provided on the display unit 11 may function as the input unit 15. Alternatively, if the information processing system 1 has a voice recognition function capable of recognizing speech by a user, the input unit 15 is configured to include a microphone that receives input of speech by a user.

[0019] The control unit 16 controls and manages the entire information processing system 1, including each functional unit of the information processing system 1. When the information processing system 1 is made up of multiple devices, the control unit 16 is provided in each of the multiple devices. The control unit 16 performs various controls, for example, by running a control program stored in the storage unit 14. For example, the control unit 16 can be configured with a control device such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit).

[0020] The acquisition unit 17 acquires data (hereinafter also referred to as "user data") related to a user who exercises using the information processing system 1. The acquisition unit 17 can acquire user data by various methods. For example, the acquisition unit 17 acquires user data based on an operation input to the input unit 15. For example, the acquisition unit 17 acquires user data pre-stored in the storage unit 14 from the storage unit 14. For example, the acquisition unit 17 acquires user data stored in an external server device or other storage device by performing information communication with the server device. The acquisition unit 17 is not limited to the method exemplified here, and can acquire user data using other known methods. Details of the user data will be described later.

[0021] The motion analysis unit 18 analyzes the user's motion. The function of the motion analysis unit 18 may be executed as part of the function of the control unit 16. Alternatively, the motion analysis unit 18 may be realized by a processor separate from the control unit 16.

[0022] For example, the motion analysis unit 18 analyzes the user's motion based on the user's motion captured by the imaging unit 12. The motion analysis unit 18 analyzes the user's motion by comparing reference data indicating a standard for the motion with motion data indicating the user's motion. The reference data is, for example, motion data shown by a demonstration video displayed on the display unit 11. The motion data is, for example, data indicating the user's motion captured by the imaging unit 12. The motion analysis unit 18 can analyze the user's motion using a known method. For example, the motion analysis unit 18 can analyze the motion using three-dimensional coordinates, time, vectors, angles, and other quantitative values ​​for each person's feature points. Specifically, the motion analysis unit 18 calculates the coordinates for each person's feature points and calculates the amount of change in the feature points to analyze the user's motion. The motion analysis unit 18 may analyze the motion using AI (artificial intelligence).

[0023] The generation unit 19 generates advice according to the user data based on the comparison result between the reference data and the exercise data. The function of the generation unit 19 may be executed as part of the function of the control unit 16. Alternatively, the generation unit 19 may be realized by a processor separate from the control unit 16.

[0024] For example, the generation unit 19 acquires a comparison result between the reference data and the movement data from the motion analysis unit 18. Based on the acquired comparison result, the generation unit 19 generates advice corresponding to the user data acquired by the acquisition unit 17. For example, data on the wording of advice is stored in the memory unit 14, and the generation unit 19 may refer to the memory unit 14 and generate advice corresponding to the user data based on the wording of advice stored in the memory unit 14. The generation unit 19 may be configured by a generation AI. In this case, the generation unit 19 generates advice using the generation AI. Details of the advice generated by the generation unit 19 will be described later.

[0025] The communication unit 20 executes information communication with an external device. For example, the communication unit 20 executes information communication with an external server device. The external server device may be a physical server or a cloud server. The communication unit 20 executes information communication, for example, wirelessly. The communication unit 20 transmits and receives various information through information communication.

[0026] When the information processing system 1 uses a generation AI for generating advice, for example, on an open network, the communication unit 20 communicates information with an AI server of the generation AI. Also, when the information processing system 1 manages information used for information processing (for example, demonstration video, reference data, user data, etc.) on an open network, the communication unit 20 communicates information with a server that has the information used for information processing on the open network.

[0027] For example, if the information processing system 1 has a function of having a conversation with the user through the AI, the storage unit 14 may store a conversation history between the AI ​​and the user.

[0028] Next, an example of processing executed by the information processing system 1 will be described with reference to Fig. 3. Fig. 3 is a flowchart showing an example of processing executed by the information processing system 1 of Fig. 1.

[0029] First, the acquisition unit 17 acquires user data (step S11). The user data is input by, for example, a user. The user can input the user data using a known method. For example, the user can input the user data using a keyboard, a touch panel, or the like as the input unit 15. Alternatively, the user may input the user data to a device other than the information processing system 1 (for example, a terminal device such as a smartphone). In this case, the information processing system 1 can acquire the user data by communicating with the device. The user can input the user data by speaking. In this case, for example, a microphone as the input unit 15 of the information processing system 1 can acquire the content of the user's speech and generate user data based on the user's speech through voice recognition. The user can input the user data using other known methods in addition to the method described here. Furthermore, the user data does not necessarily have to be input by the user, but may be input by another person on behalf of the user. The acquisition unit 17 can acquire the user data input by the user.

[0030] The user data does not necessarily have to be input by the user or another person. For example, the user data may be automatically collected by the information processing system 1. For example, the information processing system 1 stores exercise data when the user exercises in the storage unit 14 and automatically collects the exercise data, thereby automatically acquiring information about the user's exercise history.

[0031] The user data is data related to a user who exercises using the information processing system 1. The user data may be data related to the user and related to exercise. Here, several examples of the user data are described, but the user data is not limited to the examples shown here.

[0032] For example, the user data may include the user's physical characteristics. Physical characteristics are characteristics related to the body itself. Physical characteristics may include, but are not limited to, height, weight, gender, and age.

[0033] For example, the user data includes information regarding the user's exercise purpose. Examples of exercise purposes include, but are not limited to, dieting, building muscle, relieving muscle stiffness, maintaining a figure, and refreshing yourself. The exercise purpose may be related to a specific part of the body, such as shaping up a specific part of the body (e.g., the stomach). The exercise purpose may be selectable by the user from a list of purposes, or may be freely entered by the user.

[0034] For example, the user data includes information regarding the user's exercise preference. The exercise preference is, for example, the direction of exercise. The exercise preference may be, for example, wanting to exercise leisurely, wanting to exercise to music, wanting to do muscle training, etc., but is not limited to these examples. The exercise preference may also include, for example, the frequency of exercise. The frequency of exercise is determined, for example, by the amount of time or number of times exercised per specific period (e.g., one day or one week). The exercise preference may be, for example, selectable by the user from items listed as preferences, or may be freely input by the user.

[0035] For example, the user data includes information regarding the user's preferences. Preferences are the user's tastes. Preferences may be, for example, preferences regarding feedback on the user's exercise provided by the information processing system 1. For example, preferences may be, for example, whether the user wants gentle instruction or strict instruction, but are not limited to the examples given here. Preferences may be, for example, selectable by the user from items listed as preferences, or may be freely input by the user.

[0036] The above-mentioned purpose, inclination, and preference do not necessarily have to be clearly distinguished. That is, regardless of whether the above-mentioned example belongs to the purpose, inclination, or preference, the acquiring unit 17 may acquire information such as that shown in the above-mentioned example as user data.

[0037] For example, the user data includes information regarding the user's knowledge of exercise. As an example, the user's knowledge of exercise is whether or not the user knows the names of specific muscles in the body. However, the user's knowledge of exercise is not limited to this and may include other knowledge related to exercise. The user's knowledge of exercise may be input by the user, or the information processing system 1 may have an AI-based conversation function with the user, and the information processing system 1 may infer the user's knowledge of exercise based on the conversation.

[0038] For example, the user data may include information about the user's exercise history. The information about the user's exercise history may include the user's past exercise data. The information about the user's exercise history may include information about when, how much, and what type of exercise the user performed. The information about the exercise history may be automatically collected by the information processing system 1 based on, for example, past exercise data stored in the memory unit 14, as described above.

[0039] The information processing system 1 acquires the data of the demonstration video (step S12). The data of the demonstration video can be acquired by an appropriate method. For example, the data of the demonstration video is stored in advance in the storage unit 14, and is acquired by reading out the data stored in the storage unit 14. Alternatively, the data of the demonstration video can be acquired via the Internet, for example, from a distribution server that distributes exercise-related content.

[0040] The information processing system 1 acquires demonstration video data based on a predetermined operation input by the user. In this case, the user can select the demonstration video to be acquired. Alternatively, the information processing system 1 may automatically select a demonstration video using a predetermined algorithm and acquire data of the selected demonstration video. Specifically, the information processing system 1 may acquire demonstration video data based on acquired user data. For example, the information processing system 1 may search for and acquire demonstration video data showing an exercise demonstration that matches the user's goals, inclinations, and preferences as user data. Alternatively, the information processing system 1 may search for and acquire demonstration video data showing an exercise demonstration related to or similar to an exercise the user has performed in the past based on information about the user's exercise history as user data.

[0041] Furthermore, the information processing system 1 acquires reference data including the demonstration video shown by the demonstration video data acquired in step S12 (step S13). The reference data is data of the exercise shown in the demonstration video. For example, the reference data is data showing the change in coordinates of each feature point of a person during the exercise shown in the demonstration video. More specifically, the reference data may include information on three-dimensional coordinates relating to the location of which body part (feature point) at a certain point in the exercise, the speed or direction of change of the feature point, the movement of the gaze, etc.

[0042] When reference data including a demonstration video exists, the information processing system 1 acquires the reference data including the demonstration video together with the data of the demonstration video. When reference data including a demonstration video does not exist, the information processing system 1 can generate reference data including the demonstration video from the data of the demonstration video in the motion analysis unit 18. In this case, the motion analysis unit 18 acquires the reference data by, for example, extracting feature points from the state of the movement shown in the data of the demonstration video and identifying changes in the coordinates of the feature points.

[0043] The information processing system 1 outputs the demonstration video based on the data of the demonstration video acquired in step S12 (step S14). For example, the information processing system 1 outputs the demonstration video by displaying it on the display unit 11. When starting to exercise, the user performs an operation input to output the demonstration video. The information processing system 1 outputs the demonstration video based on the operation input by the user. The user exercises while watching the demonstration video displayed on the display unit 11.

[0044] If there is no reference data including a performance video, the information processing system 1 may output the performance video in step S14 and simultaneously execute the processing of step S13. In other words, if there is no reference data including a performance video, the information processing system 1 may generate reference data including the performance video in real time while outputting the performance video. Therefore, step S13 does not necessarily have to be executed before outputting the performance video, and may be executed simultaneously with step S14.

[0045] The information processing system 1 acquires the user's exercise data while outputting the demonstration video (step S15). Specifically, while the user is exercising while watching the demonstration video, the imaging unit 12 captures the user's exercise, and the motion analysis unit 18 analyzes the user's exercise based on the captured video to acquire the user's exercise data. The motion analysis unit 18 acquires the exercise data by, for example, extracting feature points from the user's exercise and identifying changes in the coordinates of the feature points.

[0046] Then, the information processing system 1 compares the reference data acquired in step S13 with the motion data acquired in step S15 using the motion analysis unit 18 (step S16). In step S16, the motion analysis unit 18 uses the reference data as a reference and extracts differences between the motion data and the reference data.

[0047] The information processing system 1 generates advice according to the user data based on the comparison result in step S16 using the generation unit 19 (step S17). For example, a plurality of advice contents (wordings) are stored in advance in the storage unit 14, and the generation unit 19 generates the advice by selecting one or more advice contents based on the comparison result and the user data from the plurality of advice contents stored in the storage unit 14, for example, according to a predetermined algorithm. Alternatively, the generation unit 19 may generate the advice using a generation AI.

[0048] Here, some examples of advice generated by the generator 19 in accordance with user data will be described.

[0049] For example, the generation unit 19 generates advice for a specific body part determined based on user data. For example, assume that the user data includes a purpose for exercising a specific body part. The generation unit 19 generates advice for the specific body part from the differences extracted in step S16. As a specific example, if the difference between the reference data and the exercise data extracted by the motion analysis unit 18 for the specific body part is equal to or greater than a predetermined threshold, the generation unit 19 generates advice to make the user's exercise more similar to the exercise in the demonstration video (e.g., advice such as "Make your exercise more in sync with the video"). If the difference is less than the predetermined threshold, the generation unit 19 generates advice indicating that the user's exercise is going well (e.g., advice such as "Keep up the good work"). The predetermined threshold and candidate wording for the advice to be output are stored, for example, in the storage unit 14. Alternatively, if the user data includes a purpose for exercising an arm (e.g., "I want to train my arms"), the generation unit 19 generates advice for the specific body part, such as "Raise your arms higher" based on the comparison results.

[0050] For example, the generation unit 19 generates advice using expressions determined based on user data. For example, assume that preferences regarding feedback on exercise are input as user data. The generation unit 19 generates advice based on the differences extracted in step S16 so that the expressions of the advice are tailored to the user's preferences. The expressions here include, for example, the wording of the advice. For example, if the user data indicates a request for gentle instruction, the generation unit 19 generates advice using wording such as "Great! Try raising your arms higher!" based on the comparison result. For example, if the user data indicates a request for strict instruction, the generation unit 19 generates advice using an imperative tone such as "Not yet! Raise your arms higher!" based on the comparison result. Note that the wording of the advice determined based on the user data does not necessarily have to be expressed as wording. For example, the wording of the advice may be expressed as speaking speed or tone, or a combination of these. When the generation unit 19 generates advice using a generation AI, the preferences can be used to define the personality of the trainer providing the advice.

[0051] For example, the generation unit 19 generates advice according to the user's knowledge about exercise, which is determined based on user data. Specifically, the generation unit 19 generates advice using expressions determined according to the user's knowledge about exercise. For example, if the user data indicates that the user has a certain level of knowledge about exercise or more, the generation unit 19 includes specific muscle names in the advice. For example, when generating advice about arm movements, the generation unit 19 includes names such as the biceps, triceps, brachialis, and brachioradialis in the advice. On the other hand, if the user data indicates that the user has less than a certain level of knowledge about exercise, the generation unit 19 does not include specific muscle names in the advice. In this case, the generation unit 19 generates advice such as "Raise your arms higher" without including muscle names in the advice. Note that advice according to exercise knowledge is not necessarily reflected only in whether or not to include muscle names. For example, advice according to exercise knowledge can also be reflected in whether or not to include more specialized expressions about muscles and body movements.

[0052] For example, the generation unit 19 may generate advice based on a comparison result between the user's past exercise data indicated by the exercise history and the exercise data indicating the user's latest exercise state. In this case, the motion analysis unit 18 not only compares the reference data with the exercise data acquired in step S15, but also compares the exercise data included in the user's exercise history, for example, stored in the storage unit 14, with the latest exercise data, i.e., the exercise data acquired in step S15. This allows, for example, differences between the user's past exercise state and the latest exercise state to be extracted. The generation unit 19 can generate advice based on the differences between the past exercise state and the latest exercise state extracted in this manner. Specifically, if the numerical value indicating the difference between the reference data and the latest exercise data is smaller than the numerical value indicating the difference between the reference data and the past exercise data, the generation unit 19 generates advice indicating that the user's exercise state is improving (e.g., advice such as "You are exercising better than before"). Conversely, if the numerical value indicating the difference between the reference data and the latest exercise data is greater than the numerical value indicating the difference between the reference data and the past exercise data, the generation unit 19 generates advice to the user indicating that the previous exercise state was more appropriate (for example, advice such as "Try to remember how you moved your body before"). Alternatively, for example, if the latest exercise state is closer to the exercise state indicated by the reference data than the past exercise state, the generation unit 19 generates advice such as "You are moving your body more appropriately than before." Conversely, if the past exercise state is closer to the exercise state indicated by the reference data than the latest exercise state, the generation unit 19 generates advice such as "Raise your arms firmly like before."

[0053] In step S17, the generator 19 can generate advice in real time according to the user's exercise. The content of the advice is not limited to improvements regarding the user's exercise, but may include any feedback regarding the user's exercise. For example, the content of the advice may include encouragement, praise, etc.

[0054] The advice may be generated by appropriately combining the above examples. Furthermore, the user data may be reflected in the generation of the advice in terms of both technical and speech depending on the content of the data. The technical aspect is the technical content of how to perform the exercise, and the speech aspect is the way in which the advice is presented to the user, i.e., the expressive content of the presentation.

[0055] The display unit 11 presents the advice generated by the generation unit 19 to the user (step S18). That is, the display unit 11 can present the advice generated by the generation unit 19 to the user based on the control of the control unit 16. Note that the advice does not necessarily have to be presented by display on the display unit 11. For example, the information processing system 1 may have a speaker capable of outputting sound as the presentation unit, and may present the advice to the user by outputting the advice as audio from the speaker. The advice may be presented by using a combination of display on the display unit 11 and audio output from the speaker.

[0056] FIG. 4 is a diagram showing an example of an image displayed on display unit 11. For example, as shown in FIG. 4, the image displayed on display unit 11 has a reference data display area 41 and an exercise data display area 43. Reference data display area 41 displays a model image of the reference exercise. The model image of the reference exercise may include, for example, a demonstration image of reference data including a demonstration image. The model image of the reference exercise may include an image generated based on the reference data. Exercise data display area 43 displays the user's exercise and characteristic points 47 of the user's exercise. Furthermore, as described above, advice 45 is displayed on display unit 11 based on a numerical value indicating the difference between the reference data and the exercise data.

[0057] In this way, the information processing system 1 according to this embodiment generates advice according to user data based on the comparison result between the reference data and the exercise data, and presents the generated advice. In other words, the advice presented will be tailored to the user data. Therefore, the information processing system 1 can present personalized advice for each user. This allows users to receive advice tailored to their own characteristics, increasing their motivation to continue exercising. In other words, the information processing system 1 makes it easier to increase motivation to use an exercise support system.

[0058] In particular, when advice is generated by the generation AI, appropriate advice is presented according to the user's exercise data, rather than advice with fixed wording pre-stored in the storage unit 14. Therefore, the advice presented to the user is not fixed wording, so the user is less likely to get bored of exercising using the information processing system 1, and motivation to continue using the information processing system 1 is improved.

[0059] Furthermore, the information processing system 1 can generate dynamic advice tailored to the user's exercise by generating and presenting advice in real time. However, the generation unit 19 does not necessarily have to generate advice in real time. For example, the generation unit 19 may generate advice based on the results of comparing reference data with exercise data after a series of exercises based on a demonstration video has been completed. In this case, the generation unit 19 may generate advice such as a summary of the user's current exercise or points to keep in mind for the next exercise. Furthermore, the information processing system 1 may generate and present advice both in real time and after the exercise has been completed.

[0060] Although the present disclosure has been described based on the drawings and embodiments, it should be noted that those skilled in the art can easily make various modifications and alterations based on the present disclosure. Therefore, it should be noted that these modifications and alterations are included in the scope of the present disclosure. For example, functions included in each functional unit or step can be rearranged so as not to be logically inconsistent, and multiple functional units or steps can be combined or divided into one. [Explanation of symbols]

[0061] 1. Information Processing Systems 11 Display section 12 Imaging unit 13 Sensor section 14 Storage section 15 Input section 16 Control Unit 17 Acquisition Department 18 Motion analysis section 19 Generation part 20 Communications Department 41 Reference data display area 43 Exercise data display area 45 Advice 47 feature points

Claims

1. an acquisition unit for acquiring user data relating to a user; a generation unit that generates advice according to the user data based on a comparison result between reference data indicating a standard of an exercise state and exercise data indicating the exercise state of the user; a presentation unit that presents the advice; An information processing system comprising:

2. the user data includes information regarding the user's exercise goals; the generation unit generates advice regarding a body part determined according to the purpose based on the comparison result. The information processing system according to claim 1 .

3. the user data includes information regarding the user's preferences; the generation unit generates advice in an expression determined according to the preference based on the comparison result. The information processing system according to claim 1 .

4. The information processing system of claim 3 , wherein the expression includes a wording of the advice.

5. the user data includes information regarding the user's exercise knowledge; the generation unit generates advice in an expression determined according to the knowledge based on the comparison result. The information processing system according to claim 1 .

6. the user data includes information regarding the user's exercise history, including past exercise data; the generation unit generates the advice based on a comparison result between past exercise data of the user indicated by the exercise history and exercise data indicating the latest exercise state of the user. The information processing system according to claim 1 .

7. The information processing system according to claim 1 , wherein the generator generates the advice in accordance with the exercise of the user.

8. The information processing system according to claim 1 , wherein the generation unit is configured by a generation AI (Artificial Intelligence).

9. An information processing method executed by an information processing system, comprising: Obtain user data about the user; generating advice according to the user data based on a comparison result between reference data indicating a standard of exercise status and exercise data indicating the user's exercise status; Presenting said advice; Information processing methods.

Images