Credit method based on wearable device and wearable device

Abstract

The application provides a wearable device-based integral method and a wearable device, the method comprising: obtaining physiological data of a user; determining a vitality score of the user based on the physiological data, the vitality score representing a current physical vitality degree of the user; determining a target suggestion adapted to the user according to the vitality score, the target suggestion comprising a movement suggestion and / or a rest suggestion; determining a target integral mode based on the target suggestion, and determining a total integral of the user by executing the target integral mode, the target integral mode comprising a movement integral mode and / or a rest integral mode, the movement integral mode being used to calculate a movement integral according to an actual movement process of the user, and the rest integral mode being used to calculate a rest integral according to an actual rest process of the user.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to an integration method based on a wearable device and a wearable device. Background Technology

[0002] With the increasing prevalence of wearable devices, people are using them more and more to record exercise data and monitor their health. Correspondingly, the demand for intelligent wearable devices is also rising. For example, some wearable devices, in conjunction with terminal applications, can output corresponding exercise scores for users, such as vitality scores and training readiness scores. These scores can be used to characterize the user's current level of physical activity and fitness for exercise, and have certain reference value.

[0003] However, when generating vitality scores and training readiness scores for users, only the user's exercise data is typically considered. As a result, the obtained exercise scores can only reflect the user's past athletic performance. In other words, the guidance provided by the exercise scores is singular and the suggestions are too biased, thus resulting in a limited scope of application. Summary of the Invention

[0004] To address the aforementioned technical problems, embodiments of this application provide an integration method based on wearable devices, comprising:

[0005] Obtain the user's physiological data; Based on the physiological data, a user's vitality score is determined, which represents the user's current level of physical vitality. Based on the vitality score, target recommendations are determined for the appropriate user, including exercise recommendations and / or rest recommendations; Based on the target suggestion, a target points mode is determined, and the user's total points are determined by executing the target points mode. The target points mode includes an exercise points mode and / or a rest points mode. The exercise points mode is used to calculate exercise points based on the user's actual exercise process, and the rest points mode is used to calculate rest points based on the user's actual rest process.

[0006] In one embodiment, determining the user's vitality score based on the physiological data includes: Based on the physiological data, a standardized vitality index is determined for the user. The standardized vitality index is used to reflect the degree of deviation of the user's current physical state from the user's long-term health level. The user's vitality score is determined based on the standardized vitality index, preset weights, and physiological data.

[0007] In one embodiment, determining the user's standardized vitality index based on the physiological data includes: The standardized vitality index is determined based on the following formula: ; Wherein, SVI is the standardized vitality index; Base_HRV is the first recovery baseline; HRV Today The current heart rate variability (HRV) data of the user, determined based on the physiological data.

[0008] In one embodiment, determining the user's vitality score based on the standardized vitality index, preset weights, and physiological data includes: Determine the user's current HRV data based on the physiological data; Based on the deep sleep duration or resting heart rate in the physiological data, a sleep quality correction factor is determined for the user's current state. The vitality score is calculated by combining the standardized vitality index, the weight of the preset first recovery baseline, the weight of the second recovery baseline, the current HRV data, and the sleep quality correction factor. The first recovery baseline is determined based on the HRV data within the first detection period, and the second recovery baseline is determined based on the HRV data within the second detection period. The first detection period is longer than the second detection period.

[0009] In one embodiment, determining a sleep quality correction factor for the user's current state based on the deep sleep duration or resting heart rate in the physiological data includes: The sleep quality correction factor is determined based on a comparison between the deep sleep duration in the aforementioned physiological data and the user's reference deep sleep duration; or The sleep quality correction factor is determined based on the comparison between the resting heart rate in the physiological data and the user's reference resting heart rate; The reference resting heart rate and reference deep sleep duration are set as fixed values ​​or determined based on the user's physiological data during a historical period.

[0010] In one embodiment, the rest suggestion is used to instruct the user to perform rest behaviors appropriate to the user's current physical condition; The exercise suggestions include a first exercise suggestion and a second exercise suggestion. Both the first exercise suggestion and the second exercise suggestion are used to instruct the user to perform a target exercise suitable for the user's current physical condition. The first exercise suggestion and the second exercise suggestion correspond to different vitality scores. The intensity of the first target exercise corresponding to the first exercise suggestion is higher than the intensity of the second target exercise corresponding to the second exercise suggestion. The step of determining the target recommendations for suitable users based on the vitality score includes: When the vitality score is within the first score range, the target recommendation is determined to be a rest recommendation; When the vitality score is within the second score range, the second exercise suggestion is determined as the target suggestion, and the first score range is smaller than the second score range; When the vitality score is within the third score range, the first exercise suggestion is determined to be the target suggestion, and the second score range is smaller than the third score range.

[0011] In one embodiment, the method further includes: If the target suggestion includes a rest suggestion, and it is detected that the user's rest duration reaches the target duration indicated by the rest suggestion, then the user's vitality score is recalculated, and a new vitality score is obtained; The new vitality score is used to update the target suggestions for the user, and a new target score pattern is determined based on the updated target suggestions.

[0012] In one embodiment, the motion integration mode calculates the integral using the following formula:

[0013] The exercise score is determined based on the effective amount of exercise generated by the user. The integral coefficient for motion integration varies depending on the motion integration mode. The rest integral mode calculates the integral using the following formula:

[0014] The rest score is determined based on the effective amount of rest generated by the user. The rest integral coefficient is determined based on the physiological data generated by the user during the rest process.

[0015] In one embodiment, determining the user's total points by executing the target points pattern includes: Within the specified points statistics period, if the target points mode is the sports points mode, the user's total points are determined based on the sports points. Within the specified points statistics period, if the target points mode is the rest points mode, the user's total points are determined based on the rest points. Within a specified points statistics period, if the target points mode includes both an exercise points mode and a rest points mode, the user's total points are determined based on the exercise points and rest points.

[0016] Another embodiment of the present invention also provides a wearable device, including at least a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program in the memory, implements the steps of the integration method based on the wearable device described in any of the above embodiments.

[0017] Another embodiment of the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the integration method based on a wearable device as described above.

[0018] Based on the foregoing, the wearable device-based point system provided in this application obtains the user's physiological data and determines the user's current physical activity level based on this data. Then, it adapts target suggestions to the user's current physical activity level. Since the target suggestions are adapted based on the user's current physical activity level and include exercise and / or rest suggestions, and the system sets a corresponding point system for the user based on these suggestions, it not only ensures that the target suggestions output by the system match the user's current physical activity level but also encourages the user to engage in exercise and / or rest that matches the target suggestions through the provided point system. Therefore, it effectively avoids the problems caused by a singular guiding role of point scores and overly biased suggestions, such as only awarding points for exercise. Furthermore, it allows the point system of this application to be applied to a wide range of wearable devices. Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the application. The objectives and other advantages of this application can be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0019] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the integration method based on wearable devices in an embodiment of the present invention.

[0022] Figure 2 This is a flowchart illustrating an integration method based on a wearable device according to another embodiment of the present invention.

[0023] Figure 3 This is a flowchart illustrating the integration method based on wearable devices in an application embodiment of the present invention.

[0024] Figure 4 This is a structural block diagram of an integration device based on a wearable device in an embodiment of the present invention.

[0025] Figure 5 This is a structural block diagram of a wearable device according to an embodiment of the present invention. Detailed Implementation

[0026] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings, but these are not intended to limit the scope of the invention.

[0027] It should be understood that various modifications can be made to the embodiments disclosed herein. Therefore, the following description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope of this disclosure will be apparent to those skilled in the art.

[0028] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present disclosure and, together with the general description of the disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.

[0029] These and other features of the invention will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0030] It should also be understood that although the invention has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of the invention, which have the features described in the claims and are therefore all within the scope of protection defined herein.

[0031] The above and other aspects, features and advantages of this disclosure will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0032] Specific embodiments of the present disclosure are described thereafter with reference to the accompanying drawings; however, it should be understood that the disclosed embodiments are merely examples of the present disclosure and can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the present disclosure. Therefore, the specific structural and functional details disclosed herein are not intended to be limiting, but merely to serve as the basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of substantially any suitable detailed structures.

[0033] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in still another embodiment,” all of which may refer to one or more of the same or different embodiments according to this disclosure.

[0034] The method can be implemented by an electronic device, such as a wearable device and / or a mobile terminal. Here, the wearable device can be a smartwatch, smart bracelet, or smart ring, etc., and the mobile terminal can be a mobile phone, tablet computer, etc. The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0035] like Figure 1 As shown, this embodiment of the invention provides an integration method based on a wearable device, including: S1: Obtain the user's physiological data; S2: Determine the user's vitality score based on the physiological data, whereby the vitality score represents the user's current physical vitality; S3: Determine target recommendations for the user based on the vitality score, the target recommendations including exercise recommendations and / or rest recommendations; S4: Determine the target score mode based on the target suggestion, and determine the user's total score by executing the target score mode. The target score mode includes an exercise score mode and / or a rest score mode. The exercise score mode is used to calculate exercise points based on the user's actual exercise process, and the rest score mode is used to calculate rest points based on the user's actual rest process.

[0036] In this embodiment, the calculation period for the score is one calendar day, but it can also be other time periods, such as several hours, a week, etc., the specifics are not fixed. In this embodiment, obtaining the user's physiological and activity data includes obtaining the user's current state or physiological data containing data from a past period, such as physiological data from the past 12 hours. Based on the obtained physiological data, the electronic device can calculate a vitality score to determine the user's current vitality score. This vitality score reflects the user's current physical vitality, determining whether the user is currently fatigued, in a normal vitality state, or in a highly energetic state, i.e., determining the level of physical vitality. Next, the electronic device can determine target suggestions adapted to the user's current physical state based on the vitality score. These target suggestions include exercise suggestions and / or rest suggestions. Exercise suggestions guide the user on what type of exercise to engage in, such as low-intensity exercise or high-intensity exercise. Rest suggestions guide the user on what type of rest to engage in, such as meditation or sleep, matching the user's current physical state with the aim of quickly restoring the user's physical condition. Different types of exercise have different point values. Performing and completing recommended exercises or rest periods yields higher points, while failing to follow the recommendations results in lower points, no points, or deductions from existing points. Electronic devices can use this information to guide users to complete the recommended exercises or rest periods, and then use points to guide users to engage in exercises or rest periods that match their physical condition, promoting physical recovery, improving health, and ensuring the user's body is in the healthiest possible state.

[0037] Specifically, the system determines the target points pattern based on the target suggestions. These suggestions might include exercise, rest, or both. For example, it might suggest prioritizing rest before engaging in light exercise. This typically occurs when a user's physical condition is slightly unwell and recovers quickly with rest. To promote better recovery, light exercise, such as walking, could be recommended. The system then assigns a points pattern to each suggestion, such as an exercise points pattern or a rest points pattern. As mentioned above, resting also earns points, encouraging users to rest and helping them quickly recover. When a user follows the system's exercise or rest suggestions, the system calculates points and tracks all points earned within the current period, such as points earned on the current day. These points can be used for gift redemption in the online store, with no specific usage restrictions.

[0038] In practical applications, for example, if a user stayed up late working the previous night, resulting in insufficient sleep duration or insufficient deep sleep, leading to poor physical vitality, wearable devices such as watches, bracelets, and smart rings can collect and analyze the user's physiological data. If the system determines the user is fatigued, it can provide a rest suggestion based on their physical condition and activate a points-based system that rewards users for rest, encouraging them to rest and quickly restore their vitality. Conversely, if a user slept for a long time or had a long period of deep sleep the previous night, and the physiological data collected by the wearable device indicates good physical vitality, the system will provide an exercise suggestion and activate a points-based system that rewards users for exercise, encouraging them to exercise and improve their health.

[0039] Based on the above, it is clear that the solution in this embodiment provides target suggestions to users based on their current physical condition, meeting their current physical needs and promoting rapid recovery, thus truly protecting their health. Furthermore, to prevent users from continuing to exercise when they need rest in order to earn points, the solution in this embodiment still awards points to users when they take rest actions based on the rest suggestions. In other words, users can earn points even when they need to rest, effectively preventing the aforementioned phenomenon and further assisting in protecting users' health.

[0040] In one embodiment, obtaining the user's physiological data may include, but is not limited to, the user's heart rate variability (HRV) data, and may also include other data such as blood oxygen saturation and blood pressure. This data can be collected through the user's smart wearable devices, such as smartwatches, AI devices with brain-computer interfaces, etc., with no specific limitations. The entity performing the data processing can be the data acquisition device, a cloud service platform, or the user's terminal device, such as a mobile phone, tablet, etc., with no specific limitations. The entity performing the data processing can share the results with the service platform or other user-related devices that the user can use or access.

[0041] It is easy to understand that a user's physical vitality or fatigue level can be reflected in certain physiological data. For example, a user's vitality score can be determined based on physiological data, specifically by calculating it using a coupling formula between physiological data and vitality score.

[0042] Taking physiological data such as blood oxygen, blood pressure, and HRV as examples, by pre-configuring corresponding weight coefficients for different blood oxygen, blood pressure, and HRV values, and substituting these weight coefficients with the acquired actual physiological parameters into a pre-constructed coupling formula, a vitality score representing the user's current level of physical activity can be obtained. Here, the coupling formula can be pre-constructed based on the actual items of the physiological parameters. For example, vitality scores can be indexed on a large amount of physiological data, and permissions can be assigned to different items of physiological data to characterize the degree of influence of different physiological data on the vitality score. Data analysis tools can then be used to fit regular expressions to the indexed data content, thereby obtaining the coupling formula for calculating the vitality score. Of course, other known methods can also be used to construct the coupling formula. Alternatively, other known technical means can be used to determine the user's vitality score based on physiological data; this is not limited here.

[0043] like Figure 2 As shown in the illustration, as one embodiment, determining a user's vitality score by combining the physiological data and exercise data includes: S201: Determine the user's standardized vitality index based on the physiological data. The standardized vitality index is used to reflect the degree of deviation of the user's current physical state from the user's long-term health level. S202: Determine the user's vitality score based on the standardized vitality index, preset weights, and physiological data.

[0044] In other words, in this embodiment, the user's standardized vitality index is first calculated based on physiological data to determine the degree of deviation of the user's current physical state from the user's long-term health level. Then, based on the standardized vitality index and physiological data, as well as the preset weights determined based on the user's historical physical state, the above-mentioned coupling formula is used to calculate the vitality score.

[0045] Specifically, determining the user's vitality score by combining the standardized vitality index, preset weights, and physiological data includes: S203: Determine the user's current HRV data based on the physiological data; S204: Based on the deep sleep duration or resting heart rate in the physiological data, determine a sleep quality correction factor for the user's current state; S205: The vitality score is calculated by combining the standardized vitality index, the weight of the preset first recovery baseline, the weight of the second recovery baseline, the current HRV data, and the sleep quality correction factor. The first recovery baseline is determined based on HRV data within a first testing period, and the second baseline is determined based on HRV data within a second testing period. The first testing period is longer than the second testing period. For example, the first testing period may be, but is not limited to, the past month, and the second testing period may be, but is not limited to, the past week.

[0046] In actual calculations, the vitality score can be obtained by using the above parameters as a basis and combining them with the aforementioned coupled formula. ; Wherein, DVS is the vitality score; x This is a baseline constant, which can be 50 or similar, representing the score when the HRV data is close to the first recovery baseline (long-term baseline). k SVI is the scaling constant; SVI is the standard vitality index; the preset weights include long-term baseline weights and short-term baseline weights (i.e., the weights of the second recovery baseline), Weight1 is the weight of the first recovery baseline (long-term baseline), and Weight2 is the weight of the second recovery baseline (short-term baseline); short_HRV is the average HRV value within the first detection period; HRV Today The current HRV data of the user is determined based on the physiological data; Sleep_Factor is the sleep quality correction factor determined based on the physiological data.

[0047] Specifically, the standardized vitality index, in this embodiment, is obtained by comparing the average of the user's daily measured HRV, such as the cardiac data generated during a certain period in the morning or at noon, with the user's long-term baseline. It can be determined based on the following formula: ; Base_HRV is the long-term baseline mentioned above.

[0048] The scaling constant kFor example, it can be, but is not limited to, 0.2, which is used to control the degree of influence of SVI on the score. For the long-term baseline, this embodiment uses the median or exponential moving average of the user's morning HRV data over the past 60 days or other days to filter out daily noise. The weight of the long-term baseline can be, for example, but is not limited to, 0.6, and the weight of the short-term trend can be, for example, but is not limited to, 0.3. The short-term baseline can be determined using the average of the user's morning HRV data over the past week, such as 7 days, to reflect the user's recent physical recovery trend and vitality. The sleep quality correction factor is a dynamic value that changes dynamically according to the user's sleep quality. In this embodiment, this factor can be determined based on the user's deep sleep duration or resting heart rate (RHR) last night. For example: S206: Determine the sleep quality correction factor based on the comparison between the user's resting heart rate and the user's reference resting heart rate in the user's physiological data; or S207: Determine the sleep quality correction factor based on the comparison results between the deep sleep duration in the user's physiological data and the user's reference deep sleep duration; The reference resting heart rate and reference deep sleep are either fixed values ​​or average values ​​calculated based on the user's physiological data over a historical period.

[0049] For example, assuming an abnormally high resting heart rate (RHR) or less than one hour of deep sleep (the specific reference value varies), the sleep quality correction factor will be lowered, such as by reducing the current factor value by 10. For RHR, the reference value can be determined by averaging the data collected during a 30-minute sliding window of the lowest heart rate. The reference value for deep sleep can be the average deep sleep duration of the user over a past week or month. When the currently collected RHR is greater than the reference value, or the currently collected deep sleep duration is less than the corresponding reference value, the sleep quality correction factor will be lowered; conversely, it will be raised. The specific increase or decrease values ​​are variable and can be flexibly configured. The higher the HRV data collected on a given day, the better the user's recovery, the lower the stress level, and the more suitable it is for exercise; conversely, the lower the HRV data, the more suitable it is for rest.

[0050] After calculating the vitality score, the system determines target suggestions tailored to the user based on the vitality score. These target suggestions, as described above, include rest areas and exercise suggestions. The rest suggestions instruct the user to perform rest behaviors suitable for their current physical state. For example, if the user is in a state of normal fatigue, meditation or a short nap may be suggested; if the user is in a state of moderate to severe fatigue, sleep may be recommended. The exercise suggestions include a first exercise suggestion and a second exercise suggestion. Both instruct the user to perform target exercises suitable for their current physical state. The first and second exercise suggestions are matched with different intervals of the vitality score. That is, the first exercise suggestion is suitable when the vitality score falls within one interval, and the second exercise suggestion is suitable when the vitality score falls within another interval. The two intervals are of different sizes. The intensity of the target exercise is matched to the vitality score. The intensity of the first target exercise corresponding to the first exercise suggestion is higher than the intensity of the second target exercise corresponding to the second exercise suggestion. For example, if the vitality score is high, high-intensity exercise may be suggested, and vice versa.

[0051] Specifically, determining the target recommendations for suitable users based on the vitality score includes: When the vitality score is within the first score range, the target recommendation is determined to be a rest recommendation; When the vitality score is within the second score range, the second exercise suggestion is determined as the target suggestion, and the first score range is smaller than the second score range; When the vitality score is within the third score range, the first exercise suggestion is determined to be the target suggestion, and the second score range is smaller than the third score range.

[0052] In other words, based on the vitality score, the system matches it with different preset score ranges to determine which range the current vitality score falls into, and then determines the corresponding suggestion as the target suggestion based on that range. In this embodiment, each score range can be considered to correspond to one suggestion; once the score range is determined, the corresponding suggestion can be identified as the target suggestion. Similar to traffic lights, if the current vitality score is in the green light range, it means the user's current physical condition is very good and can perform high-intensity exercise, so the first exercise suggestion can be identified as the target suggestion. If the current vitality score is in the yellow light range, it means the user's current physical condition is average and can perform low-intensity exercise, so the second exercise suggestion can be identified as the target suggestion. If the current vitality score is in the red light range, it means the user's current physical condition is poor and needs to rest, so the rest suggestion can be identified as the target suggestion.

[0053] For example, in this embodiment, the system determines the user's current physical state based on their vitality score, such as whether they are fatigued or how fatigued they are. A very low level of fatigue indicates a high vitality state, while a high level indicates fatigue. Once the user's physical state is determined, the system analyzes it to determine if rest is needed. If so, a rest suggestion is directly assigned. If not, meaning the user is in good physical condition or even energetic, an exercise suggestion is assigned. This exercise suggestion includes recommended exercises, which vary depending on the user's physical state, primarily in intensity. For example, the system can determine the appropriate exercise type for the user's current physical state, such as aerobic exercise, anaerobic exercise, high-intensity exercise, low-intensity exercise, etc., and ultimately determine the exercise suggestion based on the determined exercise type, guiding the user to perform exercises that match their current physical state.

[0054] When applying the system, the "red, green, and yellow" indicator lights are used as an example to determine the user's physical condition and target suggestions. For instance, a vitality score greater than 70 corresponds to a green indicator light, indicating that the user's physical condition has fully recovered and is in excellent condition, ready to challenge high-intensity exercise and earn high token rewards, i.e., high points. A vitality score between 50 and 70 indicates a normal physical condition, suitable for daily exercise, and the corresponding indicator light is yellow. When the vitality score is less than 50, the corresponding indicator light is red, indicating that the user is fatigued and needs rest. The system will then guide the user to perform restorative tasks, such as meditation or sleeping, to promote the recovery of physical vitality. Tokens are set as rewards to prevent over-exercising. The system determines the corresponding indicator light by comparing scores, and then further determines the suitable exercise type for the user by judging the lit indicator light, such as sleeping, meditation, walking, jogging, or high-intensity exercise, and then determines target suggestions based on the exercise type.

[0055] As an optional solution, the method further includes: S5: If the target suggestion includes a rest suggestion, and it is detected that the user's rest duration reaches the target duration indicated by the rest suggestion, then the user's vitality score is re-determined to obtain a new vitality score; S6: Update the target suggestion for the user based on the new vitality score, and determine a new target score pattern based on the updated target suggestion.

[0056] For example, when a user is physically fatigued and needs rest (i.e., when the system suggests rest), the system can monitor the user's rest behavior by collecting physiological data. After a period of rest, the system will determine the user's new physical state based on the newly collected physiological data. If the system determines that the user's current physical state has recovered to its normal level, such as returning to the long-term baseline, it can then re-determine exercise suggestions tailored to the user's current physical state. This corresponds to a situation where the user has both exercise and rest points for the day. However, if, after a period of rest, the system determines that the user's physical state has not recovered based on new physiological data, it will continue to provide rest suggestions or adjust them, such as adjusting rest behavior to increase rest intensity, like switching from meditation to sleep. If the new physiological data indicates that the user's physical state, while not fully recovered, is close to the long-term baseline, the system can also adjust rest behavior to reduce rest intensity, such as switching from sleep to meditation, until the user's physical state recovers, at which point it can provide appropriate exercise suggestions again. In other words, the system will flexibly adjust its target suggestions based on the new physiological data generated by the user during rest periods, at least in real time.

[0057] Furthermore, determining the target integration pattern based on the target strategy includes: S401: When the target suggestion or the updated target suggestion characterizes a suggestion for the user to rest, the target score mode is determined to be a rest score mode; S402: When the target suggestion or the updated target suggestion characterizes the user's movement, the corresponding motion integration mode is determined as the target integration mode based on the corresponding target movement, and the motion integration modes corresponding to the first target movement and the second target movement are different.

[0058] For example, the system determines the target scoring mode based on the specific content of the target suggestion. If the target suggestion is a rest suggestion, then the rest scoring mode is the target scoring mode. If the target suggestion is an exercise suggestion, then the scoring mode is determined based on the specific first exercise suggestion and second exercise suggestion. The different exercise scoring modes are matched with the different types of corresponding exercise suggestions and target exercises. In this embodiment, the first target exercise is a high-intensity, high-consumption type of exercise, which has a high difficulty coefficient, and therefore the corresponding score is also high, i.e., a high-scoring mode, which can be reflected in a higher scoring coefficient. For the second target exercise, which is a low-difficulty type of exercise, such as jogging or walking, the score involved in this type of exercise is relatively low, i.e., a low-scoring mode, which can be reflected in a lower scoring coefficient. It is possible, but not limited to, that the scoring coefficient of the first exercise type is greater than 1, and the scoring coefficient of the second exercise type is equal to 1, etc.

[0059] Specifically, the motion integration mode calculates the integral using the following formula:

[0060] The exercise score is determined based on the effective amount of exercise generated by the user. The integral coefficient for motion integration varies depending on the motion integration mode. The rest integral mode calculates the integral using the following formula:

[0061] The rest score is determined based on the effective amount of rest generated by the user. The rest integral coefficient is determined based on the physiological data generated by the user during the rest process.

[0062] The exercise integral coefficient in the above embodiments can vary depending on the type of exercise. For example, if the recommended exercise for the user is of type one, the exercise integral coefficient can be adjusted to 1.2 (the specific value is not limited). If the recommended exercise is of type two, the exercise integral coefficient can be adjusted to 1 (the specific value is not limited). The rest integral coefficient in this embodiment is not fixed, but is flexibly determined based on the user's actual physical condition and rest quality. The determination logic includes: 1. Meditation / Breathing Exercises: Heart Rate (HR): Maintained consistently near or below resting heart rate (RHR); Breathing Rate: Conforms to the guided pattern (e.g., 4...). (6 beats / minute) and high heart rate stability. CRecovery=1 (completely completed); if heart rate fluctuations are large or motion detection is present, then CRecovery=0.

[0063] 2. Focus on sleep, sleep efficiency: the number and duration of awakenings during sleep; deep sleep / REM duration: whether it reaches more than 80% of the individual's historical average. Determine the recovery rate (e.g., 0.8-1.0) based on sleep efficiency and deep sleep achievement rate.

[0064] 3. Mild recovery exercise: Heart rate (HR): Maintained below 50% of maximum heart rate (fat-burning zone); HRV trend: No significant decrease during activity. CRecovery=1; If heart rate enters the aerobic zone (HR>70%MaxHR), then CRecovery=0.

[0065] 4. Resting / Inactivity Time, Accelerometer: No significant movement; Posture: Continuously in a sitting or lying position. CRecovery=1 (complete rest); If there is frequent walking or vigorous hand movements, then CRecovery=0.

[0066] In other words, the system can flexibly adjust the rest points coefficient by monitoring users' physiological data in real time. This allows the system to keep track of users' physical condition and promote better rest by adjusting the points system. Users can earn more tokens after resting, thus creating a virtuous cycle that ensures users are healthier.

[0067] By detecting the user's physiological data, after determining that the user has completed their daily exercise and / or rest tasks, the system will calculate the user's daily points. This involves determining the user's total points based on the point system relevant to the target point system, including: S501: Within the specified points statistics period, if the target points mode is the sports points mode, the user's total points are determined based on the sports points. S502: Within the specified points statistics period, if the target points mode is the rest points mode, the user's total points are determined based on the rest points. S503: Within a specified points statistics period, if the target points mode includes both an exercise points mode and a rest points mode, the user's total points are determined based on the exercise points and rest points.

[0068] In other words, taking a specified points calculation period as a natural day as an example, the system will calculate the total points based on the tasks completed by the user on that day. If the user only has rest tasks on that day, only rest points will be calculated. If the user only has exercise tasks on that day, only exercise points will be calculated. If the user has both rest tasks and exercise tasks on that day, both exercise points and rest points will be calculated.

[0069] In one application embodiment, the example of red, green, and yellow indicator lights is still continued, such as... Figure 3 As shown, the system's overall execution logic includes obtaining the user's physiological and exercise data, calculating a vitality score based on the physiological and exercise data, and determining the corresponding indicator light. The system then determines the color of the indicator light and whether it is red. If red, it activates the rest points mode; otherwise, it activates the exercise points mode. Next, it determines whether the indicator light is green, indicating whether the user's current physical vitality level is high enough to perform high-intensity exercise. If green, it determines to use a points calculation mode with a points coefficient greater than 1; otherwise, it determines to use a points calculation mode with a points coefficient equal to 1. Once the user completes the exercise or rest task, or when the settlement time arrives, the system automatically calculates the total points.

[0070] The following examples from actual users illustrate the solution: Example 1: When user Xiao Li wakes up in the morning, his vitality score calculated from physiological and exercise data is 65. The process proceeds to decision point one: "Is it a red light?" Since 65 is not lower than 50, the judgment result is "No," and the system activates the exercise scoring mode. Next, it proceeds to decision point two: "Is it a green light?" Since 65 is lower than 70, the judgment result is "No," and the system executes the operation of "scoring coefficient of 1." This means that Xiao Li's exercise achievements throughout the day (such as steps) will be scored using a standard multiplier. If Xiao Li walks 10,000 steps that day, the final exercise score will be the base score multiplied by 10,000 steps multiplied by a 1.0 multiplier. Since he did not trigger the rest mode, the final score output will only be the exercise score.

[0071] Example 2: When user Xiao Wang woke up in the morning, his vitality score, calculated from physiological and exercise data, was 40 points, which was deemed a "red light" by the system, triggering a rest points mode. Unable to engage in high-yield exercise at this time, he completed a 30-minute meditation recovery task and activated the rest points calculation through his watch's menu, earning basic rest points. Xiao Wang then decided to improve his condition through rest and nutritional supplements.

[0072] By the afternoon, Xiao Wang's physiological data had rebounded to 80 points (green status). The system then reassessed the situation, determining that he was neither in a red light nor a green light state. The system activated the exercise scoring mode and set the scoring coefficient to be greater than 1 (e.g., 1.2). Xiao Wang then ran five kilometers in the afternoon, earning a high-multiplier exercise score. Ultimately, Xiao Wang's daily score output will be the sum of his morning rest score and his afternoon high-multiplier exercise score. This composite score reflects Xiao Wang's total score under the system's guidance (recovery first, then high-efficiency exercise).

[0073] The vitality score proposed in this embodiment, based on heart rate variability (HRV), simplifies complex physiological data into intuitive scores. It can even further represent the score range using red, yellow, and green indicator lights, completely solving the core drawback of existing exercise algorithms that only reward exercise volume while neglecting recovery. This embodiment intuitively tells users their current physical state (e.g., "red" indicates the need for rest), effectively avoiding overtraining and fatigue accumulation, and encouraging users to rest and recover scientifically. Furthermore, by transforming health data into actionable daily decisions (goal-oriented decisions), it significantly lowers the barrier to health management, upgrading smartwatches from simple data recording tools into real-time, personalized health guidance coaches, greatly enhancing user engagement and product trust.

[0074] likeFigure 4 As shown, another embodiment of the present invention also provides an points-based device based on a wearable device, comprising: Module 1 is used to obtain the user's physiological data; The first determining module 2 is used to determine the user's vitality score based on the physiological data, wherein the vitality score represents the user's current level of physical vitality. The second determining module 3 is used to determine the target recommendations for the user based on the vitality score, the target recommendations including exercise recommendations and / or rest recommendations; The third determining module 4 is used to determine a target scoring mode based on the target suggestion, and to determine the user's total score by executing the target scoring mode. The target scoring mode includes an exercise scoring mode and / or a rest scoring mode. The exercise scoring mode is used to calculate exercise points based on the user's actual exercise process, and the rest scoring mode is used to calculate rest points based on the user's actual rest process.

[0075] In one embodiment, determining the user's vitality score based on the physiological data includes: Based on the physiological data, a standardized vitality index is determined for the user. The standardized vitality index is used to reflect the degree of deviation of the user's current physical state from the user's long-term health level. The user's vitality score is determined based on the standardized vitality index, preset weights, and physiological data.

[0076] In one embodiment, determining the user's standardized vitality index based on the physiological data includes: The standardized vitality index is determined based on the following formula: ; Wherein, SVI is the standardized vitality index; Base_HRV is the first recovery baseline; HRV Today The current heart rate variability (HRV) data of the user, determined based on the physiological data.

[0077] In one embodiment, determining the user's vitality score by combining the standardized vitality index, preset weights, and physiological data includes: Determine the user's current HRV data based on the physiological data; Based on the deep sleep duration or resting heart rate in the physiological data, a sleep quality correction factor is determined for the user's current state. The vitality score is calculated by combining the standardized vitality index, the weight of the preset first recovery baseline, the weight of the second recovery baseline, the current HRV data, and the sleep quality correction factor. The first recovery baseline is determined based on the HRV data within the first detection period, and the second recovery baseline is determined based on the HRV data within the second detection period. The first detection period is longer than the second detection period.

[0078] In one embodiment, determining a sleep quality correction factor for the user's current state based on the deep sleep duration or resting heart rate in the physiological data includes: The sleep quality correction factor is determined based on a comparison between the deep sleep duration in the aforementioned physiological data and the user's reference deep sleep duration; or The sleep quality correction factor is determined based on the comparison between the resting heart rate in the physiological data and the user's reference resting heart rate; The reference resting heart rate and reference deep sleep duration are set as fixed values ​​or determined based on the user's physiological data during a historical period.

[0079] In one embodiment, the rest suggestion is used to instruct the user to perform rest behaviors appropriate to the user's current physical condition; The exercise suggestions include a first exercise suggestion and a second exercise suggestion. Both the first exercise suggestion and the second exercise suggestion are used to instruct the user to perform a target exercise suitable for the user's current physical condition. The first exercise suggestion and the second exercise suggestion correspond to different vitality scores. The intensity of the first target exercise corresponding to the first exercise suggestion is higher than the intensity of the second target exercise corresponding to the second exercise suggestion. The step of determining the target recommendations for suitable users based on the vitality score includes: When the vitality score is within the first score range, the target recommendation is determined to be a rest recommendation; When the vitality score is within the second score range, the second exercise suggestion is determined as the target suggestion, and the first score range is smaller than the second score range; When the vitality score is within the third score range, the first exercise suggestion is determined to be the target suggestion, and the second score range is smaller than the third score range.

[0080] In one embodiment, the device further includes: The fourth determining module is used to redetermine the user's vitality score and obtain a new vitality score if the target suggestion includes a rest suggestion and the user's rest duration reaches the target duration indicated by the rest suggestion. The fifth determining module is used to update the target suggestion for the user based on the new vitality score, and to determine a new target score pattern based on the updated target suggestion.

[0081] In one embodiment, the motion integration mode calculates the integral using the following formula:

[0082] The exercise score is determined based on the effective amount of exercise generated by the user. The integral coefficient for motion integration varies depending on the motion integration mode. The rest integral mode calculates the integral using the following formula:

[0083] The rest score is determined based on the effective amount of rest generated by the user. The rest integral coefficient is determined based on the physiological data generated by the user during the rest process.

[0084] In one embodiment, determining the user's total points by executing the target points pattern includes: Within the specified points statistics period, if the target points mode is the sports points mode, the user's total points are determined based on the sports points. Within the specified points statistics period, if the target points mode is the rest points mode, the user's total points are determined based on the rest points. Within a specified points statistics period, if the target points mode involved includes both exercise points mode and rest points mode, the user's total points are determined based on the exercise points and rest points.

[0085] like Figure 5 As shown, another embodiment of the present invention also provides a wearable device, including at least a memory 6 and a processor 5. The memory 6 stores a computer program, and the processor 5 implements the steps of the integration method based on the wearable device described in any of the above embodiments when executing the computer program on the memory 6.

[0086] Furthermore, one embodiment of the present invention also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the integration method based on a wearable device as described above. It should be understood that the various solutions in this embodiment have the corresponding technical effects in the above-described method embodiments, and will not be repeated here.

[0087] Furthermore, embodiments of the present invention also provide a computer program product tangibly stored on a computer-readable medium and comprising computer-readable instructions that, when executed, cause at least one processor to perform an integration method based on a wearable device, such as the embodiments described above.

[0088] It should be noted that the computer storage medium of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access storage medium (RAM), a read-only storage medium (ROM), an erasable programmable read-only storage medium (EPROM or flash memory), an optical fiber, a portable compact disk read-only storage medium (CD-ROM), an optical storage medium, a magnetic storage medium, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program configured for use by or in connection with an instruction execution system, system, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, antenna, optical fiber, RF, etc., or any suitable combination thereof.

[0089] Furthermore, those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0090] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0091] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0092] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of protection of this application is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this application as described above, which are not provided in detail for the sake of brevity.

Claims

1. An integration method based on wearable devices, characterized in that, include: Obtain the user's physiological data; Based on the physiological data, a user's vitality score is determined, which represents the user's current level of physical vitality. Based on the vitality score, target recommendations are determined for the appropriate user, including exercise recommendations and / or rest recommendations; Based on the target suggestion, a target points mode is determined, and the user's total points are determined by executing the target points mode. The target points mode includes an exercise points mode and / or a rest points mode. The exercise points mode is used to calculate exercise points based on the user's actual exercise process, and the rest points mode is used to calculate rest points based on the user's actual rest process.

2. The integration method based on wearable devices according to claim 1, characterized in that, Determining the user's vitality score based on the physiological data includes: Based on the physiological data, a standardized vitality index is determined for the user. The standardized vitality index is used to reflect the degree of deviation of the user's current physical state from the user's long-term health level. The user's vitality score is determined based on the standardized vitality index, preset weights, and physiological data.

3. The integration method based on wearable devices according to claim 2, characterized in that, The determination of the user's standardized vitality index based on the physiological data includes: The standardized vitality index is determined based on the following formula: ； Wherein, SVI is the standardized vitality index; Base_HRV is the first recovery baseline; HRV Today The current heart rate variability (HRV) data of the user, determined based on the physiological data.

4. The integration method based on wearable devices according to claim 2, characterized in that, The process of determining the user's vitality score based on the standardized vitality index, preset weights, and physiological data includes: Determine the user's current HRV data based on the physiological data; Based on the deep sleep duration or resting heart rate in the physiological data, a sleep quality correction factor is determined for the user's current state. The vitality score is calculated by combining the standardized vitality index, the weight of the preset first recovery baseline, the weight of the second recovery baseline, the current HRV data, and the sleep quality correction factor. The first recovery baseline is determined based on the HRV data within the first detection period, and the second recovery baseline is determined based on the HRV data within the second detection period. The first detection period is longer than the second detection period.

5. The integration method based on wearable devices according to claim 4, characterized in that, The process of determining a sleep quality correction factor for the user's current state based on the deep sleep duration or resting heart rate in the physiological data includes: The sleep quality correction factor is determined based on a comparison between the deep sleep duration in the aforementioned physiological data and the user's reference deep sleep duration; or The sleep quality correction factor is determined based on the comparison between the resting heart rate in the physiological data and the user's reference resting heart rate; The reference resting heart rate and reference deep sleep duration are set as fixed values ​​or determined based on the user's physiological data during a historical period.

6. The integration method based on wearable devices according to claim 1, characterized in that, The rest suggestions are used to instruct users to perform rest behaviors appropriate to their current physical condition; The exercise suggestions include a first exercise suggestion and a second exercise suggestion. Both the first exercise suggestion and the second exercise suggestion are used to instruct the user to perform a target exercise suitable for the user's current physical condition. The first exercise suggestion and the second exercise suggestion correspond to different vitality scores. The intensity of the first target exercise corresponding to the first exercise suggestion is higher than the intensity of the second target exercise corresponding to the second exercise suggestion. The step of determining the target recommendations for suitable users based on the vitality score includes: When the vitality score is within the first score range, the target recommendation is determined to be a rest recommendation; When the vitality score is within the second score range, the second exercise suggestion is determined as the target suggestion, and the first score range is smaller than the second score range; When the vitality score is within the third score range, the first exercise suggestion is determined to be the target suggestion, and the second score range is smaller than the third score range.

7. The integration method based on wearable devices according to claim 6, characterized in that, The method further includes: If the target suggestion includes a rest suggestion, and it is detected that the user's rest duration has reached the target duration indicated by the rest suggestion, then the user's vitality score is re-determined to obtain a new vitality score; The target recommendation is updated based on the new vitality score, and a new target score pattern is determined based on the updated target recommendation.

8. The integration method based on wearable devices according to any one of claims 1 to 7, characterized in that, The motion integral mode calculates the integral using the following formula: The exercise score is determined based on the effective amount of exercise generated by the user. The integral coefficient of motion; The rest integral mode calculates the integral using the following formula: The rest score is determined based on the effective amount of rest generated by the user. The rest integral coefficient is determined based on the physiological data generated by the user during the rest process.

9. The integration method based on wearable devices according to claim 1, characterized in that, The process of determining the user's total points by executing the target points pattern includes: Within the specified points statistics period, if the target points mode is the sports points mode, the user's total points are determined based on the sports points. Within the specified points statistics period, if the target points mode is the rest points mode, the user's total points are determined based on the rest points. Within a specified points statistics period, if the target points mode includes both an exercise points mode and a rest points mode, the user's total points are determined based on the exercise points and rest points.

10. A wearable device, characterized in that, It includes at least a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program in the memory, implements the steps of the integration method based on a wearable device as described in any one of claims 1-9.

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