Method of motion analysis, method of metabolic data analysis, apparatus and wearable device
By acquiring user characteristic data and exercise type, and using a metabolic prediction model for analysis, the problem of analyzing exercise consumption of wearable devices in a single exercise scenario has been solved, enabling wide application and accurate analysis in multiple scenarios.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGDONG OPPO MOBILE TELECOMMUNICATIONS CORP LTD
- Filing Date
- 2025-01-14
- Publication Date
- 2026-07-14
AI Technical Summary
Traditional wearable devices can only analyze one type of exercise scenario when analyzing exercise consumption, which cannot cover multiple exercise scenarios and thus limits the exercise scenarios.
By acquiring user characteristic data and exercise type, a target metabolic prediction model is determined using a preset metabolic prediction model, and metabolic data of the user during exercise is obtained to generate exercise analysis information.
It enables the analysis of exercise consumption in various sports scenarios, expands the application scope of wearable devices, and can generate accurate exercise analysis information based on different user characteristics and sports types.
Smart Images
Figure CN122376080A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of wearable device technology, and in particular to a motion analysis method, a metabolic data analysis method, a device, and a wearable device. Background Technology
[0002] With the development of wearable device technology, the application scenarios for wearable devices are becoming more extensive. Many users are starting to use wearable devices to analyze their exercise consumption and determine exercise guidance recommendations based on the analysis results.
[0003] However, in traditional technologies, when using wearable devices to analyze exercise consumption, the analysis can only be performed on one type of exercise scenario. For example, it can only analyze exercise consumption in the outdoor running scenario and cannot cover other exercise scenarios. This results in a limitation on exercise scenarios when using wearable devices to analyze exercise consumption in traditional technologies. Summary of the Invention
[0004] This application provides a method for motion analysis, a method for metabolic data analysis, an apparatus, and a wearable device, which can make the exercise scenarios in which exercise consumption analysis is performed using wearable devices more extensive.
[0005] In a first aspect, embodiments of this application provide a motion analysis method, the method comprising:
[0006] Acquire user characteristic data, movement type, and the user's movement data;
[0007] The metabolic data of the user during exercise is obtained based on the user characteristic data, the exercise data, and the target metabolic prediction model; the target metabolic prediction model is determined from a preset set of metabolic prediction models based on the user characteristic data and the exercise type.
[0008] Based on the metabolic data, the user's exercise analysis information is generated.
[0009] Secondly, embodiments of this application provide a metabolic data analysis method, the method comprising:
[0010] Based on the user's predicted fat metabolism rate and total metabolic rate during exercise, the user's fat metabolism and glycogen metabolism during exercise are determined.
[0011] Thirdly, embodiments of this application provide a motion analysis device, the device comprising:
[0012] The first acquisition module is used to acquire the user's user characteristic data, movement type, and the user's movement data;
[0013] The second acquisition module is used to acquire the user's metabolic data during exercise based on the user feature data, the exercise data, and the target metabolic prediction model; the target metabolic prediction model is determined from a preset set of metabolic prediction models based on the user feature data and the exercise type.
[0014] The generation module is used to generate the user's exercise analysis information based on the metabolic data.
[0015] Fourthly, embodiments of this application provide a metabolic data analysis device, the device comprising:
[0016] The analysis module is used to determine the amount of fat metabolism and glycogen metabolism of the user during exercise based on the predicted fat metabolism rate and total metabolism of the user during exercise.
[0017] Fifthly, embodiments of this application provide a wearable device, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the motion analysis method as described in the first aspect and the metabolic data analysis method as described in the second aspect.
[0018] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the methods described in the first and second aspects.
[0019] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the methods described in the first and second aspects.
[0020] The aforementioned exercise analysis method, metabolic data analysis method, device, and wearable device, by acquiring user characteristic data, exercise type, and user exercise data, can determine the corresponding target metabolic prediction model from a preset set of metabolic prediction models based on user characteristic data and exercise type. Since the target metabolic prediction model is determined from the preset set of metabolic prediction models based on user characteristic data and exercise type, the corresponding target metabolic prediction model can be determined from the preset set of metabolic prediction models for different user characteristic data and exercise types. This makes it applicable to a variety of different exercise scenarios. Thus, based on user characteristic data, exercise data, and target metabolic prediction models, the user's metabolic data during exercise can be acquired, and user exercise analysis information can be generated based on the user's metabolic data during exercise. This enables exercise analysis to be performed for different exercise scenarios and generates user exercise analysis information, making the exercise scenarios for using wearable devices for exercise consumption analysis more extensive. Attached Figure Description
[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0022] Figure 1 Here is a flowchart of a motion analysis method in one embodiment;
[0023] Figure 2 Here is a flowchart of the motion analysis method in another embodiment;
[0024] Figure 3 Here is a flowchart of the motion analysis method in another embodiment;
[0025] Figure 4 This is a schematic diagram of the display interface of a wearable device according to one embodiment;
[0026] Figure 5 This is a schematic diagram of the motion analysis interface in one embodiment;
[0027] Figure 6 This is a schematic diagram of the guidance interface in one embodiment;
[0028] Figure 7 This is a schematic diagram of a motion assessment interface in one embodiment;
[0029] Figure 8 This is a structural block diagram of the motion analysis device in one embodiment;
[0030] Figure 9 This is a structural block diagram of a metabolic data analysis device in one embodiment;
[0031] Figure 10 This is a schematic diagram of the internal structure of a computer device in one embodiment. Detailed Implementation
[0032] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0033] In one embodiment, such as Figure 1As shown, a motion analysis method is provided. This embodiment illustrates the application of this method to a wearable device. It is understood that this method can also be applied to systems including wearable devices and third-party devices, and is implemented through the interaction between the wearable device and the third-party device. The third-party device can be an electronic device, fitness equipment, etc. In this embodiment, the method includes the following steps:
[0034] S201, Obtain user characteristic data, exercise type, and user exercise data.
[0035] The user's characteristic data may include, but is not limited to, gender, age, height, and weight. As an optional implementation, the user's characteristic data may also include personal historical data, such as historical exercise frequency, exercise intensity, resting heart rate, and heart rate variability. Exercise types may include, but are not limited to, aerobic exercises such as running, cycling, swimming, walking, badminton, tennis, basketball, roller skating, skiing, strength training, resistance training, and free exercise. The user's exercise data may include, but is not limited to, exercise duration, total metabolic rate, heart rate data, exercise intensity, exercise power, location information, cadence, gait, and pace.
[0036] Optionally, in this embodiment, the user can trigger the wearable device before starting exercise. The wearable device can respond to the user's trigger operation by displaying an exercise type selection interface for the user to choose an exercise type, thus obtaining the user's exercise type. Alternatively, in this embodiment, the wearable device's sensors can collect exercise data during the user's exercise process, enabling the wearable device to determine the user's exercise type based on the sensor-collected data. For example, for two different exercise types, running and cycling, running involves whole-body movement, while cycling mainly focuses on the lower body. The fat metabolism rates of the two are different. Therefore, the wearable device can determine the user's exercise type based on one or more of the exercise data collected by the sensors, such as total metabolic rate, heart rate data, and exercise intensity.
[0037] Optionally, in this embodiment, the user characteristic data can be obtained by the wearable device in response to the aforementioned triggering operation, displaying an information acquisition interface while simultaneously displaying the sports type selection interface, and acquiring the user characteristic data input by the user through the information acquisition interface. Optionally, in this embodiment, the user's personal historical data can be obtained by the wearable device through the aforementioned information acquisition interface, or it can be data stored in the wearable device's own memory during previous use of the wearable device, which the wearable device then retrieves from the memory. This embodiment does not limit the description of the method for obtaining the user's personal historical data. Optionally, in this embodiment, the acquired user feature data can be initialized, and the validity of the user feature data can be determined. For example, the user's identity can be identified based on the user feature data to determine whether the user's identity is legitimate. Alternatively, it can be determined whether the user feature data is empty; if the user feature data is empty and missing, a preset default value can be used as the user feature data. Furthermore, the reasonableness of the user feature data can be determined; for example, if the user inputs a height value that is not within the normal height range, it can be determined that the height value input by the user is unreasonable, and a preset default value can be used to replace the unreasonable user feature data.
[0038] Optionally, in this embodiment, the wearable device can collect the user's motion data through the collaborative work of built-in and external sensors. For example, the built-in sensors of the wearable device may include, but are not limited to, heart rate sensors, accelerometers, gyroscopes, and Global Positioning System (GPS) sensors. External sensors may include, but are not limited to, gait monitoring sensors and image sensors. As another example, the built-in sensors of the wearable device in this embodiment may include, but are not limited to, heart rate sensors, accelerometers, and gyroscopes. That is, the built-in sensors of the wearable device in this embodiment may not include GPS; motion data for acquiring the user's metabolic data during exercise can also be collected through other sensors. Optionally, in this embodiment, the user's motion data can be collected in real time through the collaborative work of built-in and external sensors, or the user's motion data can be collected at preset calculation intervals through the coordinated work of built-in and external sensors. That is, the user's motion data can be collected according to the calculation interval of the user's metabolic data. Furthermore, the acquired motion data can be preprocessed; for example, heart rate data and cadence can be smoothed and filtered.
[0039] S202, based on user characteristic data, exercise data and target metabolic prediction model, obtain the user's metabolic data during exercise; the target metabolic prediction model is determined from a set of preset metabolic prediction models based on user characteristic data and exercise type.
[0040] In this embodiment, it should be noted that multiple personalized metabolic models can be constructed in advance based on different users' exercise levels and historical data, forming a set of metabolic prediction models. In this embodiment, when it is necessary to analyze a user's exercise status, the target metabolic prediction model that is closest to the user's user characteristics and exercise type can be determined from the preset set of metabolic prediction models based on the user's user characteristic data and exercise type.
[0041] Optionally, in this embodiment, the user's user feature data and exercise data features can be acquired, concatenated into a feature sequence, and then the concatenated feature sequence can be filtered for outliers and smoothed before being input into the target metabolic prediction model. The target metabolic prediction model then outputs the user's metabolic data during exercise. Optionally, the user's metabolic data during exercise may include, but is not limited to, fat metabolism rate, fat metabolism amount, glycogen metabolism amount, glycogen metabolism rate, and total energy consumption. Optionally, in this embodiment, the user's user feature data and exercise data can be input into the target metabolic prediction model, which outputs the user's fat metabolism rate. The wearable device further calculates other metabolic data of the user based on the fat metabolism rate output by the target metabolic prediction model. Alternatively, in this embodiment, the user's user feature data and exercise data can also be input into the target metabolic prediction model, which outputs the user's glycogen metabolism rate. The wearable device further calculates other metabolic data of the user based on the glycogen metabolism rate output by the target metabolic prediction model.
[0042] S203 generates exercise analysis information for the user based on metabolic data.
[0043] Optionally, in this embodiment, the user's fat-burning data and fat-burning zone type during the exercise can be determined based on the user's metabolic data during the exercise. Then, the user's exercise analysis information can be generated based on the user's fat-burning data and fat-burning zone type during the exercise. Optionally, in this embodiment, the generated user's exercise analysis information may include, but is not limited to, information such as the user's fat-burning analysis information and fat-burning efficiency analysis information during the exercise.
[0044] In the aforementioned exercise analysis method, by acquiring user characteristic data, exercise type, and user exercise data, a corresponding target metabolic prediction model can be determined from a pre-set set of metabolic prediction models based on the user characteristic data and exercise type. Since the target metabolic prediction model is determined from the pre-set set of metabolic prediction models based on the user characteristic data and exercise type, a corresponding target metabolic prediction model can be determined from the pre-set set of metabolic prediction models for different user characteristic data and exercise types. This method is applicable to a variety of different exercise scenarios. Thus, based on the user characteristic data, exercise data, and target metabolic prediction model, the user's metabolic data during exercise can be obtained. Based on the user's metabolic data during exercise, exercise analysis information can be generated, enabling exercise analysis to be performed for different exercise scenarios and generating user exercise analysis information. This makes the exercise scenarios for using wearable devices to analyze exercise consumption more extensive.
[0045] This embodiment will explain in detail the specific process of acquiring a user's metabolic data during exercise. In one embodiment, such as... Figure 2 As shown, the above S202 includes:
[0046] S301 obtains the fat metabolism rate based on user characteristic data, exercise data, and target metabolic prediction model.
[0047] Optionally, in this embodiment, the user's user feature data and the user's exercise data can be acquired, and then concatenated to form a feature sequence. This feature sequence is then input into a target metabolic prediction model to obtain the user's fat metabolism rate during exercise. It should be noted that if the user's user feature data and real-time exercise data are input into the target metabolic prediction model, the obtained result is the user's real-time fat metabolism rate; if the user's user feature data and exercise data throughout the entire exercise process are input into the target metabolic prediction model, the obtained result is the user's total fat metabolism rate throughout the entire exercise process.
[0048] It is understood that the target metabolic prediction model in this embodiment is a pre-trained model used to obtain the fat metabolism rate during user exercise. Optionally, the target metabolic prediction model in this embodiment can be one or more models selected from support vector machines, neural network models, decision trees, random forests, and gradient boosting trees. Optionally, in this embodiment, when training the target metabolic prediction model, training sample data can be constructed based on the exercise data generated by the sample user during exercise and the time of generation. Fat metabolism rate is predicted based on the sample data and the initial metabolic prediction model to obtain the predicted value of the sample user's real-time fat metabolism rate during exercise. The square of the difference between the actual value and the predicted value of the real-time fat metabolism rate is calculated as the value of the loss function of the initial metabolic prediction model. The model parameters of the initial metabolic prediction model are adjusted according to the value of the loss function until the model converges to obtain the target metabolic prediction model. The actual value of the real-time fat metabolism rate can be calculated using a formula based on the amount of oxygen inhaled and carbon dioxide exhaled by the sample user during exercise.
[0049] S302 obtains metabolic data based on fat metabolism rate, preset calculation interval, and total metabolic amount.
[0050] The preset calculation interval can be the interval for acquiring metabolic data, i.e., how often metabolic data is acquired. Multiple preset calculation intervals can be included throughout the user's exercise process. For example, if the user's exercise process is 1 hour and the preset calculation interval is 15 minutes, then there can be 4 preset calculation intervals throughout the user's exercise process. Additionally, it should be noted that the total metabolic rate in this embodiment refers to the total calories consumed by the user during the entire exercise process. The total metabolic rate in this embodiment can be equivalent to the total calories consumed by the user during the entire exercise process. Optionally, in this embodiment, the user's fat metabolism rate can be obtained based on the user's fat metabolism rate and the preset calculation interval. For example, the product of the fat metabolism rate and the preset calculation interval can be used as the user's fat metabolism rate, and then the metabolic data during the user's exercise process can be obtained based on the total metabolic rate and the fat metabolism rate. Optionally, in this embodiment, the user's glycogen metabolism rate during exercise can be obtained based on the difference between the total metabolic rate and the fat metabolism rate, and the glycogen metabolism rate can be obtained based on the glycogen metabolism rate and the preset calculation interval.
[0051] In this embodiment, the process of obtaining the user's fat metabolism rate during exercise based on the user's user characteristic data, exercise data, and target metabolic prediction model is relatively simple and can quickly obtain the fat metabolism rate. Thus, the user's metabolic data during exercise can be quickly obtained based on the fat metabolism rate, preset calculation interval, and total metabolism, thereby improving the efficiency of obtaining the user's metabolic data during exercise.
[0052] In some scenarios, wearable devices can determine a user's fat-burning information during exercise based on their metabolic data, and generate exercise analysis information based on this data. In one embodiment, the aforementioned metabolic data includes fat metabolism rate, fat metabolism amount, glycogen metabolism amount, and glycogen metabolism rate, such as... Figure 3 As shown, the above S203 includes:
[0053] S401 determines the user's fat-burning zone type and fat-burning efficiency based on fat metabolism rate.
[0054] Fat metabolism rate refers to the rate at which body fat is converted into energy and consumed. For each user, the zone with the highest fat metabolism rate during exercise is called the optimal fat-burning zone. When a user is in another fat-burning zone, they can increase or decrease the intensity of their exercise to reach the optimal fat-burning zone.
[0055] In some embodiments, the fat-burning zone types include: optimal fat-burning zone, increased intensity zone, and decreased intensity zone. Further, by displaying the user's current fat-burning zone type, the system can prompt the user whether to increase, decrease, or maintain the current exercise intensity. For example, such as... Figure 4 As shown, the top of the circular aperture can be used as the optimal fat-burning zone, with the left and right sides representing the zones for increasing and decreasing intensity, respectively. The current zone on the surface is highlighted. Other shapes or text can also be used for display. To facilitate user viewing during exercise, different colors can be used to indicate different types, and voice and vibration cues can also be incorporated.
[0056] Optionally, in this embodiment, the type of fat-burning zone the user is in can be determined based on the fat metabolism rate. For example, the determined fat-burning zone type can be the optimal fat-burning zone, or a fat-burning zone type where exercise intensity needs to be increased or decreased to reach the optimal fat-burning zone. Optionally, in this embodiment, the wearable device can determine the user's fat-burning efficiency throughout the exercise process based on the proportion of time the user is in the optimal fat-burning zone. Optionally, in this embodiment, during the user's exercise, the type of fat-burning zone the user is in can be displayed on the wearable device's display interface. For example, this can be achieved through... Figure 4 The interface shown displays the type of fat-burning zone the user is in during exercise. Figure 4 The first interface displays the type of fat-burning zone the user is in during exercise, indicating that the user needs to increase the intensity of their workout to reach the optimal fat-burning zone. Figure 4 The second interface displays when the user is in their optimal fat-burning zone during exercise. Figure 4The third interface displays the fat-burning zone type, indicating when the user needs to reduce exercise intensity to reach the optimal fat-burning zone during exercise. For example... Figure 4 As shown, the display interface can also show information such as the user's fat metabolism, glycogen metabolism, heart rate, and total exercise consumption during exercise.
[0057] S402 generates exercise analysis information and / or prompt information for the user based on one or more of the following: fat metabolism amount, glycogen metabolism amount, fat metabolism rate, glycogen metabolism rate, fat burning zone type, and fat burning efficiency. During the user's exercise, the exercise analysis information and / or prompt information are output according to a preset output method. The prompt information is used to prompt the user to maintain the current exercise intensity, increase the exercise intensity, or decrease the exercise intensity.
[0058] Optionally, in this embodiment, prompt information can be generated based on the type of fat-burning zone. For example, if the user is in the optimal fat-burning zone, the generated prompt information can be used to remind the user to maintain the current exercise intensity; if the user is in a fat-burning zone that has not reached the optimal fat-burning zone, the generated prompt information can be used to remind the user to increase the exercise intensity; if the user is in a fat-burning zone that exceeds the optimal fat-burning zone, the generated prompt information can be used to remind the user to reduce the exercise intensity.
[0059] Optionally, in this embodiment, the average fat metabolism rate of the user during exercise can be determined based on the user's fat metabolism rate during exercise; the average glycogen metabolism rate of the user during exercise can be determined based on the user's glycogen metabolism rate during exercise; and the proportion of fat metabolism to total energy expenditure during exercise can be determined based on the amount of fat metabolized during exercise. That is, in this embodiment, the generated user exercise analysis information may include one or more of the following: fat metabolism amount, glycogen metabolism amount, fat metabolism rate, glycogen metabolism rate, average fat metabolism rate, average glycogen metabolism rate, proportion of fat metabolism to total energy expenditure, and fat-burning efficiency. As an optional implementation, other exercise analysis information can also be determined based on one or more of the following: fat metabolism amount, glycogen metabolism amount, fat metabolism rate, glycogen metabolism rate, fat-burning zone type, and fat-burning efficiency, according to actual needs. This embodiment does not limit the specific content of the exercise analysis information.
[0060] Optionally, in this embodiment, the motion analysis information and / or prompts can also be output according to a preset output method during the user's movement. Optionally, the preset output method may include, but is not limited to, charts, text descriptions, voice prompts, vibration feedback, etc.
[0061] In this embodiment, based on the user's fat metabolism rate during exercise, the type of fat-burning zone and fat-burning efficiency of the user can be quickly determined. Therefore, based on one or more of the user's fat metabolism amount, glycogen metabolism amount, fat metabolism rate, glycogen metabolism rate, fat-burning zone type, and fat-burning efficiency during exercise, exercise analysis information and / or prompts can be quickly generated. This allows for timely reminders to the user to engage in more effective exercise during the exercise process. Furthermore, the generated exercise analysis information can provide guidance for the user's next exercise session, helping the user to exercise more effectively in the future.
[0062] In some scenarios, such as when a user is exercising while following a TV screen and it's inconvenient to look at the wearable device's screen, motion analysis information and prompts can be sent to the TV for display. Based on the above embodiments, in one embodiment, the method further includes:
[0063] Step A: Send motion analysis information and prompt information to the target terminal so that the target terminal outputs motion analysis information and / or prompt information according to a preset output method.
[0064] Optionally, the target terminal in this embodiment can be a tablet computer, television, treadmill, fitness equipment, etc. In this embodiment, the wearable device can send motion analysis information and / or prompt information to the target terminal through a communication connection. Optionally, when sending motion analysis information and / or prompt information to the target terminal, the wearable device can also send preset output method information for the motion analysis information and / or prompt information to the target terminal, so as to instruct the target terminal to output the motion analysis information and / or prompt information according to the preset output method; or, the user can pre-set the preset output method for motion analysis information and prompt information in the target terminal, and when the target terminal receives the motion analysis information and / or prompt information sent by the wearable device, it outputs the motion analysis information and / or prompt information according to the preset output method.
[0065] In this embodiment, by sending the motion analysis information and / or prompt information generated during the user's exercise to the target terminal, the target terminal can output the motion analysis information and / or prompt information according to a preset output method. This makes the output method of motion analysis information and / or prompt information more diversified. In particular, for some exercise scenarios where users cannot view the display information of wearable devices during exercise, by having the target terminal output motion analysis information and / or prompt information according to a preset output method, users can obtain motion analysis information and / or prompt information in a timely manner, thereby helping users to exercise more effectively.
[0066] In some scenarios, metabolic data and exercise analysis information during user exercise can also be displayed through the display interface of wearable devices. The exercise analysis method provided in this application will be explained from the perspective of front-end display. Please refer to the description in the following embodiments for details.
[0067] Based on the above embodiments, in one embodiment, the exercise analysis information may include one or more of fat metabolism, glycogen metabolism, fat burning efficiency, and fat burning efficiency evaluation information, and the method further includes:
[0068] Step B: In response to the exercise completion command, the exercise analysis interface is displayed; the exercise analysis interface includes one or more of the following: fat metabolism, glycogen metabolism, fat burning efficiency, and fat burning efficiency evaluation information.
[0069] In this embodiment, upon completion of exercise, the user can trigger the wearable device to generate an exercise completion command. Optionally, in this embodiment, the wearable device may display an exercise completion control, which the user can trigger upon completion of exercise to generate an exercise completion command. Further, the wearable device may display an exercise analysis interface in response to the exercise completion command. Optionally, the exercise analysis interface may include one or more of the following: fat metabolism, glycogen metabolism, fat burning efficiency, and fat burning efficiency evaluation information during exercise. For example, the exercise analysis interface may be... Figure 5 The interface shown may include information such as the user's fat metabolism, glycogen metabolism, fat burning efficiency, and fat burning efficiency evaluation during exercise. Furthermore, as an optional implementation, the exercise analysis interface may also include information such as... Figure 5 The displayed information includes the user's movement type and comprehensive data during the movement process. As an optional implementation method, such as... Figure 5 As shown, the exercise analysis interface may include an area displaying the user's exercise type, an area displaying exercise analysis information, and an area displaying comprehensive data. The exercise type display area may show the user's exercise type information, while the exercise analysis information display area may show at least one of the following: total metabolic rate, fat metabolism, glycogen metabolism, fat burning efficiency, and fat burning efficiency evaluation information during exercise. It should be noted that the fat burning efficiency evaluation information may include, but is not limited to, […]. Figure 5 The results are indicated by terms such as "Excellent," "Needs Improvement," "Good," and "Professional." Additionally, the comprehensive data display area in the motion analysis interface can show... Figure 5 The information shown includes at least one of the following: user exercise duration, exercise distance, steps, and total metabolic rate. As one possible implementation method, such as... Figure 5As shown, it can also display the user's current sugar and fat consumption ratio and provide feedback through visual charts and text descriptions to help users understand their exercise consumption status in real time.
[0070] In this embodiment, the wearable device responds to the exercise completion command and can display an exercise analysis interface. The exercise analysis interface can include information such as the user's fat metabolism, glycogen metabolism, fat burning efficiency, and fat burning efficiency evaluation during the exercise process. In this way, the exercise analysis interface can intuitively display the user's metabolic information and fat burning efficiency information during the exercise process, so that the user can intuitively and clearly obtain the user's metabolic information and fat burning efficiency information during the exercise process.
[0071] Based on the above embodiments, in one embodiment, the above method further includes:
[0072] Step C: In response to the trigger operation on the exercise analysis interface, the guidance interface is displayed; the guidance interface includes a fat burning guidance display area, a total metabolic rate guidance display area, a fat burning amount guidance display area, a fat burning efficiency guidance display area, and an exercise duration guidance display area.
[0073] Step D: In response to the triggering operation of the guidance interface, the exercise assessment interface is displayed; the exercise assessment interface includes a fat-burning efficiency display area and a fat-burning efficiency description display area.
[0074] In this embodiment, after viewing the information displayed on the motion analysis interface, the user can trigger the motion analysis interface, causing the wearable device to respond to the trigger operation and display a guidance interface. Optionally, the user can trigger the motion analysis interface by clicking, double-clicking, swiping left, or swiping right. For example, in this embodiment, the guidance interface can be... Figure 6 The interface shown is as follows: Figure 6 As described above, the guidance interface may include areas for displaying fat burning guidance, total metabolic rate guidance, fat burning amount guidance, fat burning efficiency guidance, and exercise duration guidance. Please refer to [link / reference]. Figure 6 The fat-burning guidance display area can analyze the user's fat-burning information during this exercise and generate guidance based on this information. For example, the generated guidance could be... Figure 6 The example shown is a suggestion like, "This was a fantastic fat-burning workout. Keep it up, and combine it with a healthy diet for even better results." Optionally, in this embodiment, data such as the user's total metabolic rate, fat burned, fat-burning efficiency, and exercise duration can also be generated. Figure 6The guidance shown in the example chart comparison information can be generated by combining the chart comparison information.
[0075] Furthermore, in this embodiment, after viewing the information displayed on the guidance interface, the user can trigger the guidance interface, causing the wearable device to display the exercise assessment interface in response to the trigger operation. Optionally, the user can trigger the exercise assessment interface by clicking, double-clicking, swiping left, or swiping right on the guidance interface. For example, in this embodiment, the exercise assessment interface can be... Figure 7 The interface shown is as follows: Figure 7 As shown in the example, the exercise assessment interface may include a fat-burning efficiency display area and a fat-burning efficiency description display area. The fat-burning efficiency display area can show information such as the user's fat-burning efficiency during this exercise, the user's fat-burning efficiency rating during this exercise ("Excellent"), and the percentage of the user's highly efficient fat-burning time out of the total exercise time. The fat-burning efficiency information during the user's current exercise can include... Figure 7 The example shows the user's fat-burning efficiency curve during this exercise. The fat-burning efficiency description display area can show explanatory information about the fat-burning efficiency, as well as evaluation information for different fat-burning efficiency ranges.
[0076] In this embodiment, the wearable device can respond to a trigger operation on the exercise analysis interface and display a guidance interface. The displayed guidance interface includes a fat burning guidance display area, a total metabolic rate guidance display area, a fat burning rate guidance display area, a fat burning efficiency guidance display area, and an exercise duration guidance display area. Through the displayed guidance interface, users can intuitively obtain guidance information during the exercise process, which facilitates the way users obtain guidance information and enables users to exercise more effectively based on the displayed guidance information.
[0077] To facilitate understanding by those skilled in the art, the motion analysis method provided in this disclosure is described in detail below. This method may include:
[0078] S1, obtain the user's user characteristics data, exercise type, and user's exercise data.
[0079] S2 determines the target metabolic prediction model from the preset metabolic prediction model set based on user characteristic data and exercise type.
[0080] S3 obtains the fat metabolism rate based on user characteristic data, exercise data, and target metabolic prediction model.
[0081] S4, obtain the amount of fat metabolism based on the fat metabolism rate and the preset calculation interval.
[0082] S5 obtains glycogen metabolism and glycogen metabolism rate based on total metabolism, fat metabolism and preset calculation intervals.
[0083] S6 determines the user's fat-burning zone type and fat-burning efficiency based on the fat metabolism rate.
[0084] S7 generates exercise analysis information and prompts for the user based on fat metabolism, glycogen metabolism, fat metabolism rate, glycogen metabolism rate, fat burning zone type, and fat burning efficiency. The prompts are used to remind the user to maintain the current exercise intensity, increase the exercise intensity, or decrease the exercise intensity.
[0085] S8, during the user's movement, output motion analysis information and prompt information according to a preset output method; or, send motion analysis information and prompt information to the target terminal so that the target terminal outputs motion analysis information and prompt information according to a preset output method.
[0086] S9, in response to the exercise completion command, displays the exercise analysis interface; the exercise analysis interface includes a user exercise type display area, an exercise analysis information display area, and a comprehensive data display area; the exercise analysis information display area displays at least one of the user's total metabolic rate, fat metabolism rate, glycogen metabolism rate, fat burning efficiency, and fat burning efficiency evaluation information; the comprehensive data display area displays at least one of the user's exercise duration, exercise distance, steps, and total metabolic rate.
[0087] S10, in response to a trigger operation on the exercise analysis interface, displays the guidance interface; the guidance interface includes a fat burning guidance display area, a total metabolic rate guidance display area, a fat burning amount guidance display area, a fat burning efficiency guidance display area, and an exercise duration guidance display area.
[0088] S11, in response to a trigger operation on the guidance interface, displays the exercise assessment interface; the exercise assessment interface includes a fat-burning efficiency display area and a fat-burning efficiency description display area.
[0089] In one embodiment, a metabolic data analysis method is provided. This embodiment illustrates the application of this method to a wearable device. It is understood that this method can also be applied to a system including wearable devices and third-party devices, and implemented through the interaction between the wearable device and the third-party device. The third-party device can be an electronic device, fitness equipment, etc. In this embodiment, the method includes the following steps:
[0090] Step E: Determine the amount of fat metabolism and glycogen metabolism of the user during exercise based on the predicted fat metabolism rate and total metabolism of the user during exercise.
[0091] First, it should be noted that during user exercise, the amount of fat metabolism and glycogen metabolism during exercise can be continuously accumulated and calculated. As an optional implementation, the amount of fat metabolism during the current calculation interval can be obtained by multiplying the predicted fat metabolism rate during exercise by the current calculation interval. Then, the amount of glycogen metabolism during exercise can be obtained based on the total metabolism and fat metabolism. Optionally, in this embodiment, the amount of glycogen metabolism during exercise can be obtained based on the difference between the total metabolism and fat metabolism.
[0092] Optionally, in this embodiment, the predicted fat metabolism rate can be obtained by using a target metabolism prediction model based on the user's user characteristic data, exercise type, and exercise data. The target metabolism prediction model is obtained by adjusting the machine learning prediction model based on the actual fat metabolism rate of the sample user during exercise and the predicted fat metabolism rate obtained through a machine learning prediction model. Optionally, the actual fat metabolism rate is calculated using a formula based on the amount of oxygen inhaled and carbon dioxide exhaled by the sample user during exercise.
[0093] Furthermore, it should be noted that traditional techniques for analyzing exercise expenditure use deep time-series prediction models to predict respiratory exchange rate (RER) and then calculate the glucose-lipid ratio and fat consumption. However, deep time-series prediction models require a large amount of labeled data for training, which is costly to acquire and label. The models are also sensitive to data quality, exhibiting insufficient generalization ability, over-reliance on training data, and overfitting issues. In contrast, the fat metabolism rate of the sample users during exercise, used in this application to obtain the target metabolic prediction model, is calculated using a formula based on the amount of oxygen inhaled and carbon dioxide exhaled by the sample users during exercise. This method is easier to obtain and requires less computation. Additionally, traditional techniques require obtaining the fat metabolism rate by multiplying the respiratory exchange rate and total metabolic rate, and then determining the user's fat metabolism rate during exercise based on the fat metabolism rate and total metabolic rate. In traditional techniques, the determination of the respiratory exchange rate (RAR) can be inaccurate due to factors such as model training precision. Furthermore, errors in the RRA can lead to errors in the determined fat metabolism rate. This solution addresses this by iteratively learning the machine learning prediction model to gradually reduce its output error, thereby improving the accuracy of the final target metabolism prediction model. This minimizes the accuracy of the predicted fat metabolism rate compared to traditional methods that obtain the fat metabolism rate by multiplying the RRA and total metabolic output. The method provided in this application avoids errors in obtaining the fat metabolism rate to a certain extent, improving its accuracy.
[0094] In this embodiment, the process of determining the user's fat metabolism and glycogen metabolism during exercise based on the predicted fat metabolism rate and total metabolism during exercise is relatively simple and can quickly obtain fat metabolism and glycogen metabolism, thereby rapidly obtaining the user's metabolic data during exercise and improving the efficiency of obtaining the user's metabolic data during exercise.
[0095] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.
[0096] Based on the same inventive concept, this application also provides a motion analysis device for implementing the motion analysis method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more motion analysis device embodiments provided below can be found in the limitations of the motion analysis method described above, and will not be repeated here.
[0097] In one embodiment, such as Figure 8 As shown, a motion analysis device is provided, comprising: a first acquisition module, a second acquisition module, and a generation module, wherein:
[0098] The first acquisition module is used to acquire user characteristic data, movement type, and user movement data.
[0099] The second acquisition module is used to acquire the user's metabolic data during exercise based on user characteristic data, exercise data, and target metabolic prediction model; the target metabolic prediction model is determined from a set of preset metabolic prediction models based on user characteristic data and exercise type.
[0100] The generation module is used to generate exercise analysis information for users based on metabolic data.
[0101] The motion analysis device provided in this embodiment can execute the above-described method embodiment, and its implementation principle and technical effect are similar, so they will not be described again here.
[0102] Based on the above embodiments, optionally, the second acquisition module includes: a first acquisition unit and a second acquisition unit, wherein:
[0103] The first acquisition unit is used to acquire the fat metabolism rate based on user feature data, exercise data, and target metabolic prediction model.
[0104] The second acquisition unit is used to acquire metabolic data based on the fat metabolism rate, preset calculation interval, and total metabolic amount.
[0105] The motion analysis device provided in this embodiment can execute the above-described method embodiment, and its implementation principle and technical effect are similar, so they will not be described again here.
[0106] Based on the above embodiments, optionally, the metabolic data includes fat metabolism rate, fat metabolism amount, glycogen metabolism amount and glycogen metabolism rate. The second acquisition unit is specifically used to acquire fat metabolism amount according to fat metabolism rate and preset calculation interval, and to acquire glycogen metabolism amount and glycogen metabolism rate according to total metabolism amount, fat metabolism amount and preset calculation interval.
[0107] The motion analysis device provided in this embodiment can execute the above-described method embodiment, and its implementation principle and technical effect are similar, so they will not be described again here.
[0108] Based on the above embodiments, optionally, the above generation module includes: a determining unit and a generating unit, wherein:
[0109] The determination unit is used to determine the type of fat-burning zone and fat-burning efficiency of the user based on the fat metabolism rate.
[0110] The generation unit is used to generate exercise analysis information and / or prompt information for the user based on one or more of the following: fat metabolism amount, glycogen metabolism amount, fat metabolism rate, glycogen metabolism rate, fat burning zone type, and fat burning efficiency. During the user's exercise, the exercise analysis information and / or prompt information are output according to a preset output method. The prompt information is used to prompt the user to maintain the current exercise intensity, increase the exercise intensity, or decrease the exercise intensity.
[0111] The motion analysis device provided in this embodiment can execute the above-described method embodiment, and its implementation principle and technical effect are similar, so they will not be described again here.
[0112] Optionally, based on the above embodiments, the apparatus further includes a transmitting module, wherein:
[0113] The sending module is used to send motion analysis information and prompt information to the target terminal, so that the target terminal outputs motion analysis information and prompt information according to a preset output method.
[0114] The motion analysis device provided in this embodiment can execute the above-described method embodiment, and its implementation principle and technical effect are similar, so they will not be described again here.
[0115] Based on the above embodiments, optionally, the exercise analysis information includes one or more of the following: fat metabolism, glycogen metabolism, fat burning efficiency, and fat burning efficiency evaluation information; the above device further includes: a first display module, wherein:
[0116] The first display module is used to display the exercise analysis interface in response to the exercise completion command; the exercise analysis interface includes one or more of the following: fat metabolism, glycogen metabolism, fat burning efficiency, and fat burning efficiency evaluation information.
[0117] Optionally, the exercise analysis interface includes a display area for the user's exercise type, an exercise analysis information display area, and a comprehensive data display area; the exercise analysis information display area displays at least one of the following: the user's total metabolic rate, fat metabolism rate, glycogen metabolism rate, fat burning efficiency, and fat burning efficiency evaluation information; the comprehensive data display area displays at least one of the following: the user's exercise duration, exercise distance, number of steps, and total metabolic rate.
[0118] The motion analysis device provided in this embodiment can execute the above-described method embodiment, and its implementation principle and technical effect are similar, so they will not be described again here.
[0119] Based on the above embodiments, optionally, the device further includes: a second display module and a third display module, wherein:
[0120] The second display module is used to display the guidance interface in response to the trigger operation of the exercise analysis interface; the guidance interface includes a fat burning guidance display area, a total metabolic rate guidance display area, a fat burning amount guidance display area, a fat burning efficiency guidance display area, and an exercise duration guidance display area.
[0121] The third display module is used to display the exercise assessment interface in response to the trigger operation of the guidance interface; the exercise assessment interface includes a fat-burning efficiency display area and a fat-burning efficiency description display area.
[0122] The motion analysis device provided in this embodiment can execute the above-described method embodiment, and its implementation principle and technical effect are similar, so they will not be described again here.
[0123] Each module in the aforementioned motion analysis device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.
[0124] In one embodiment, such as Figure 9 As shown, a metabolic data analysis device is provided, comprising: an analysis module, wherein:
[0125] The analysis module is used to determine the amount of fat and glycogen metabolism of a user during exercise based on the predicted fat metabolism rate and total metabolic rate during exercise.
[0126] Optionally, the predicted fat metabolism rate is obtained by using a target metabolism prediction model based on the user's user characteristic data, exercise type, and exercise data; the target metabolism prediction model is obtained by adjusting the machine learning prediction model based on the actual fat metabolism rate of the sample users during exercise and the predicted fat metabolism rate predicted by the machine learning prediction model.
[0127] The metabolic data analysis device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.
[0128] In one embodiment, a computer device is provided, which may be a wearable device, and its internal structure diagram may be as follows: Figure 10 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a motion analysis method or a metabolic data analysis method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.
[0129] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0130] This application also provides a computer-readable storage medium. One or more non-volatile computer-readable storage media containing computer-executable instructions, which, when executed by one or more processors, cause the processors to perform steps of a motion analysis method or a metabolic data analysis method.
[0131] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to execute a motion analysis method or a metabolic data analysis method.
[0132] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.
[0133] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0134] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0135] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.
Claims
1. A motion analysis method, characterized in that, The method includes: Obtain the user's user characteristic data, movement type, and the user's movement data; The metabolic data of the user during exercise is obtained based on the user characteristic data, the exercise data, and the target metabolic prediction model; the target metabolic prediction model is determined from a preset set of metabolic prediction models based on the user characteristic data and the exercise type. Based on the metabolic data, the user's exercise analysis information is generated.
2. The method according to claim 1, characterized in that, The step of obtaining the user's metabolic data during exercise based on the user feature data, the exercise data, and the target metabolic prediction model includes: Based on the user characteristic data, the exercise data, and the target metabolic prediction model, the fat metabolism rate is obtained; The metabolic data are obtained based on the fat metabolism rate, the preset calculation interval, and the total metabolic amount.
3. The method according to claim 2, characterized in that, The metabolic data includes fat metabolism rate, fat metabolism amount, glycogen metabolism amount, and glycogen metabolism rate. The process of obtaining the metabolic data based on the fat metabolism rate, a preset calculation interval, and total metabolism includes: The amount of fat metabolism is obtained based on the fat metabolism rate and the preset calculation interval; The glycogen metabolism amount and the glycogen metabolism rate are obtained based on the total metabolic amount, the fat metabolism amount, and the preset calculation interval.
4. The method according to any one of claims 1 to 3, characterized in that, The metabolic data includes fat metabolism rate, fat metabolism amount, glycogen metabolism amount, and glycogen metabolism rate. The step of generating the user's exercise analysis information based on the metabolic data includes: Based on the fat metabolism rate, the type of fat-burning zone and fat-burning efficiency of the user are determined; Based on one or more of the following: fat metabolism amount, glycogen metabolism amount, fat metabolism rate, glycogen metabolism rate, fat burning zone type, and fat burning efficiency, exercise analysis information and / or prompt information for the user are generated, and the exercise analysis information and / or prompt information are output according to a preset output method during the user's exercise; the prompt information is used to prompt the user to maintain the current exercise intensity, increase the exercise intensity, or decrease the exercise intensity.
5. The method according to claim 1, characterized in that, The exercise analysis information includes one or more of the following: fat metabolism, glycogen metabolism, fat burning efficiency, and fat burning efficiency evaluation information; the method further includes: In response to the exercise completion command, an exercise analysis interface is displayed; the exercise analysis interface includes one or more of the following: fat metabolism amount, glycogen metabolism amount, fat burning efficiency, and fat burning efficiency evaluation information.
6. The method according to claim 5, characterized in that, The exercise analysis interface includes a display area for the user's exercise type, an exercise analysis information display area, and a comprehensive data display area. The exercise analysis information display area displays at least one of the user's total metabolic rate, fat metabolism, glycogen metabolism, fat burning efficiency, and fat burning efficiency evaluation information. The comprehensive data display area displays at least one of the user's exercise duration, exercise distance, steps, and total metabolic rate.
7. The method according to claim 6, characterized in that, The method further includes: In response to a trigger operation on the exercise analysis interface, a guidance interface is displayed; the guidance interface includes a fat burning guidance display area, a total metabolic rate guidance display area, a fat burning amount guidance display area, a fat burning efficiency guidance display area, and an exercise duration guidance display area. In response to a trigger operation on the guidance interface, an exercise assessment interface is displayed; the exercise assessment interface includes a fat-burning efficiency display area and a fat-burning efficiency description display area.
8. A method for analyzing metabolic data, characterized in that, The method includes: Based on the user's predicted fat metabolism rate and total metabolic rate during exercise, the user's fat metabolism and glycogen metabolism during exercise are determined.
9. The method according to claim 8, characterized in that, The predicted fat metabolism rate is obtained by using a target metabolism prediction model based on the user's user characteristic data, exercise type, and exercise data. The target metabolism prediction model is obtained by adjusting the machine learning prediction model based on the actual fat metabolism rate of the sample user during exercise and the predicted fat metabolism rate predicted by the machine learning prediction model.
10. A motion analysis device, characterized in that, The device includes: The first acquisition module is used to acquire the user's user characteristic data, movement type, and the user's movement data; The second acquisition module is used to acquire the user's metabolic data during exercise based on the user feature data, the exercise data, and the target metabolic prediction model; the target metabolic prediction model is determined from a preset set of metabolic prediction models based on the user feature data and the exercise type. The generation module is used to generate the user's exercise analysis information based on the metabolic data.
11. A metabolic data analysis device, characterized in that, The device includes: The analysis module is used to determine the amount of fat metabolism and glycogen metabolism of the user during exercise based on the predicted fat metabolism rate and total metabolism of the user during exercise.
12. A wearable device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, the processor performs the steps of the motion analysis method as described in any one of claims 1 to 9.
13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 9.
14. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 9.