Exercise health monitoring method and device, computer equipment, medium and program product

Abstract

The invention discloses an exercise health monitoring method and device, computer equipment, a medium and a program product. The method comprises the following steps: acquiring motion video data and current body component data of current motion of a target user; determining motion amplitude, motion frequency and motion times according to the motion video data; and determining the motion energy consumption of the current motion according to the current body component data, the motion amplitude, the motion frequency and the motion times. According to the technical scheme provided by the invention, accurate calculation of the exercise energy consumption is realized by combining the multi-modal data, so that the exercise effect of the user can be evaluated more accurately. And by adopting the actual motion video, the actual motion details of the user are fully considered, and the motion energy consumption calculation can be more accurate.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of intelligent health monitoring technology, and in particular to a sports health monitoring method, apparatus, computer equipment, medium, and program product. Background Art

[0002] Traditional exercise monitoring devices, such as smart bracelets and body composition analyzers, only provide single-dimensional data. For example, smart bracelets typically calculate exercise volume based on activity status and heart rate data, while body composition analyzers typically measure static body composition data. Currently, the multi-dimensional data generated by various devices cannot be mutually utilized. However, when evaluating exercise effectiveness, these multi-dimensional data may actually affect each other, resulting in inaccurate analysis results provided by current exercise monitoring methods. Summary of the Invention

[0003] Embodiments of the present invention provide a sports health monitoring method, apparatus, computer equipment, medium, and program product to improve the accuracy of sports energy consumption calculation.

[0004] In a first aspect, an embodiment of the present invention provides a method for monitoring sports health, the method comprising:

[0005] Obtain the target user's current exercise video data and current body composition data;

[0006] Determining the movement amplitude, movement frequency, and movement number according to the movement video data;

[0007] The exercise energy consumption of the current exercise is determined according to the current body composition data, the movement amplitude, the movement frequency, and the number of movements.

[0008] Optionally, determining the exercise energy consumption of the current exercise according to the current body composition data, the movement amplitude, the movement frequency, and the number of movements includes:

[0009] E=α×A×f×N+β×(Ds / Dr);

[0010] Among them, E represents the exercise energy consumption, A represents the movement amplitude, f represents the movement frequency, N represents the number of movements, Ds represents the current body fat percentage in the current body composition data, Dr represents the preset baseline body fat percentage, α represents the first preset weight, and β represents the second preset weight.

[0011] Optionally, the method further includes:

[0012] Obtaining the target user's initial body composition data before the exercise and the resulting body composition data after the exercise;

[0013] After determining the exercise energy consumption of the current exercise according to the current body composition data, the movement amplitude, the movement frequency, and the number of movements, the method further includes:

[0014] A fitness score for the current exercise based on a preset exercise goal is determined according to the initial body composition data, the result body composition data, and the exercise energy consumption.

[0015] Optionally, determining the fitness score of the current exercise based on a preset exercise goal according to the initial body composition data, the result body composition data, and the exercise energy consumption includes:

[0016] S=∑ i w i ×(D1[i]-D0[i]) / E;

[0017] Wherein, S represents the fitness score, D0[i] represents the index data of the i-th element in the initial body composition data related to the preset exercise goal, D1[i] represents the index data corresponding to D0[i] in the result body composition data, E represents the exercise energy consumption, w i Indicates the preset indicator weight of the corresponding indicator data.

[0018] Optionally, the method further includes:

[0019] A recommended exercise plan is generated according to historical exercise records and corresponding historical fitness scores, wherein the recommended exercise plan includes a single type of exercise plan or an exercise plan combining multiple types.

[0020] Optionally, the method further includes:

[0021] monitoring the actual motion parameters of the target user in real time, and generating voice prompt content according to the actual motion parameters and corresponding benchmark motion parameters; and / or,

[0022] The historical best action video of the target user is obtained, and the historical best action video is time-aligned and image-fused with the standard action video to obtain the guidance video content.

[0023] In a second aspect, an embodiment of the present invention further provides a sports health monitoring device, which includes:

[0024] The user data acquisition module is used to obtain the target user's current exercise video data and current body composition data;

[0025] An action content determination module, configured to determine an action amplitude, an action frequency, and an action number according to the motion video data;

[0026] The exercise energy consumption determination module is used to determine the exercise energy consumption of the current exercise based on the current body composition data, the movement amplitude, the movement frequency and the number of movements.

[0027] In a third aspect, an embodiment of the present invention further provides a computer device, the computer device comprising:

[0028] one or more processors;

[0029] a memory for storing one or more programs;

[0030] When the one or more programs are executed by the one or more processors, the one or more processors implement the sports health monitoring method provided by any embodiment of the present invention.

[0031] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the sports health monitoring method provided by any embodiment of the present invention.

[0032] In a fifth aspect, an embodiment of the present invention further provides a computer program product, which includes a computer program, and when the program is executed by a processor, it implements the sports health monitoring method provided by any embodiment of the present invention.

[0033] An embodiment of the present invention provides a method for monitoring sports health. First, the sports video data and current body composition data of the target user's current exercise are obtained. Then, the movement amplitude, movement frequency and number of movements of the current exercise are determined based on the obtained sports video data. Then, the movement energy consumption of the current exercise is determined based on the obtained current body composition data, movement amplitude, movement frequency and number of movements. The sports health monitoring method provided by the embodiment of the present invention realizes the accurate calculation of sports energy consumption by combining multimodal data, thereby more accurately evaluating the user's exercise effect. Moreover, by using actual sports videos, the actual movement details of the user are fully taken into account, which can also make the calculation of sports energy consumption more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of the sports health monitoring method provided in Example 1 of the present invention;

[0035] Figure 2 A schematic diagram of the structure of a sports health monitoring device provided in Example 2 of the present invention;

[0036] Figure 3 This is a structural diagram of a computer device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0037] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0038] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the steps as sequential processes, many of the steps can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0039] Example 1

[0040] Figure 1 This is a flowchart of the sports health monitoring method provided in the first embodiment of the present invention. This embodiment is applicable to the situation where the user's sports effect is evaluated in scenarios such as home fitness, rehabilitation training, and gyms. The method can be executed by the sports health monitoring device provided in the embodiment of the present invention. The device can be implemented by hardware and / or software and can generally be integrated into a computer device. Preferably, the computer device can be a Raspberry Pi 4B or other equivalent devices. Figure 1 As shown, the specific steps include:

[0041] S11. Obtain the target user's current exercise video data and current body composition data.

[0042] S12. Determine the movement amplitude, movement frequency, and movement times according to the movement video data.

[0043] S13. Determine the exercise energy consumption of the current exercise based on the current body composition data, the movement amplitude, the movement frequency, and the number of movements.

[0044] Specifically, the motion video data of the target user can be collected through the visual capture module, where the target user can be any user or a designated user who has completed identity authentication (such as face recognition or account login, etc.). Long-term data tracking can be established for the designated user for better analysis. The visual capture module may include a camera, specifically a wide-angle camera that supports 1080P resolution and 60fps frame rate, and can be deployed 1.5-3 meters in front of the user's movement position with an inclination angle of 15° to ensure that the whole body movement can be fully captured. The visual capture module may also include a built-in dedicated processor or edge computing unit for running deep learning key point detection algorithms such as OpenPose or MediaPipe Pose to extract the user's skeletal joint coordinates in real time. Specifically, it can output the skeletal joint coordinates of 17 positions and the corresponding confidence levels. In this case, the motion video data may include the skeletal joint coordinates of each frame.

[0045] Alternatively, a body composition analysis module, such as an eight-electrode body composition scale using bioelectrical impedance analysis (BIA) technology, can be used to measure the user's body composition data, including body fat percentage, muscle mass, water content, basal metabolic rate, and other indicators. During use, the user needs to stand barefoot and distribute their weight as evenly as possible to ensure measurement stability and an error of less than ±1%. For the current body composition data required for exercise analysis, the initial body composition data before the start of the exercise or the final body composition data after the exercise can be used, or the average of the two can be used.

[0046] Taking the Raspberry Pi as an example, the visual capture module and body composition analysis module can communicate with the Raspberry Pi via wireless or wired protocols (such as Wi-Fi, Bluetooth, or USB) to transmit the acquired motion video data and body composition data to the Raspberry Pi for processing. The Raspberry Pi can also store historical data in a database (such as MySQL) and synchronize and back up data with the cloud.

[0047] After acquiring motion video data, the amplitude, frequency, and number of movements during the given motion can be determined based on the video data. Specifically, the coordinates of the skeletal joints (e.g., 30 × 17 × 2) for a certain number of consecutive frames (e.g., 30 frames) can be fed as input vectors into a trained motion recognition model. This model then leverages the temporal data characteristics to identify the type of movement, such as squats, jumping jacks, or burpees, in real time. The motion recognition model can be constructed using a long short-term memory (LSTM) network. The output layer can use a softmax function to generate a probability distribution for the movement type. The model can be pre-trained and fine-tuned using public datasets and self-collected videos (approximately 5,000 samples) covering at least ten common movements. Once the movement type is determined, the movement frequency and number during the given motion can be calculated based on the change patterns of the skeletal joints corresponding to the movement type and the changes in the skeletal joint coordinates in the complete motion video data. Regarding the movement frequency, the unit time frequency can be calculated based on a preset period and then averaged over the entire motion period.

[0048] For the range of motion, we can first capture the user's static posture through the visual capture module at the beginning of the movement to calibrate the coordinate reference of the skeletal joints and establish the initial reference position of each skeletal joint. After acquiring the motion video data, we can extract the skeletal joint coordinates of each frame, such as P t ={(x1,y1),…,(x 17 ,y 17 )}, respectively, compared with the initial reference position to determine the range of motion during the current movement. Further, the maximum deviation of the coordinates of each skeletal joint point relative to the initial reference position in each frame can be determined separately. The action range can be determined according to each maximum deviation, for example, the maximum value can be used as the action range, where (x i0 ,y i0 ) indicates the initial reference position.

[0049] After determining the movement amplitude, movement frequency and number of movements, the exercise energy consumption of the current exercise can be determined based on the current body composition data, movement amplitude, movement frequency and movement number. Specifically, the movement amplitude, movement frequency and movement number can be multiplied to obtain the original energy consumption. However, due to changes in body composition data, such as low muscle rate, high body fat rate, etc., different metabolic efficiency may result, and thus the calculated original energy consumption may have certain errors. Therefore, the original energy consumption can be dynamically corrected in real time based on the current body composition data of the target user. Optionally, the influence of body fat rate is mainly considered, and the determination of the exercise energy consumption of the current exercise based on the current body composition data, the movement amplitude, the movement frequency and the movement number includes:

[0050] E=α×A×f×N+β×(Ds / Dr);

[0051] Wherein, E represents the energy consumption of the exercise, A represents the amplitude of the movement, f represents the frequency of the movement, N represents the number of movements, Ds represents the current body fat rate in the current body composition data, Dr represents the preset benchmark body fat rate, α represents the first preset weight, and β represents the second preset weight. α and β are weights related to the movement type, which can be determined by fitting a large amount of experimental data. For the preset benchmark body fat rate, a set of standard body fat rates can be pre-set based on large-scale health data statistics. Specifically, they can be ideal body fat rates for different genders and age groups. The target user can then obtain the corresponding preset benchmark body fat rate. If the current body fat rate of the target user is higher than the preset benchmark body fat rate, it means that the body fat is too high, which may lead to reduced metabolic efficiency. At this time, the model can make certain weight adjustments to make the final calculated exercise energy consumption more accurate.

[0052] Based on the above technical solution, optionally, the method further includes: obtaining the initial body composition data of the target user before the current exercise and the resulting body composition data after the current exercise; after determining the exercise energy consumption of the current exercise based on the current body composition data, the movement amplitude, the movement frequency and the number of movements, it also includes: determining the fitness score of the current exercise based on the preset exercise goal based on the initial body composition data, the resulting body composition data and the exercise energy consumption.

[0053] Specifically, the target user can set their own exercise goals in advance, such as fat loss or muscle gain, and measure their body composition once before and after the exercise to obtain the initial body composition data D0 and the result body composition data D1 of the exercise. After the exercise, the effectiveness of the exercise can also be evaluated, that is, the contribution of the target user to the improvement of the body composition indicators of interest to the target user can be evaluated. Specifically, the fitness score can be determined based on the changes in the target body composition data before and after the relative exercise energy consumption to provide a reference for the user. Traditional exercise effect evaluation relies more on subjective feelings or simple changes in body composition, and lacks objective quantitative analysis of the effects of exercise. This method combines the calculated exercise energy consumption and the changes in body composition data before and after the exercise to calculate a fitness score, which can clearly and accurately evaluate the effectiveness of the exercise.

[0054] Further optionally, determining the fitness score of the current exercise based on a preset exercise goal according to the initial body composition data, the result body composition data, and the exercise energy consumption includes:

[0055] S=∑ i w i ×(D1[i]-D0[i]) / E;

[0056] Wherein, S represents the fitness score, D0[i] represents the index data of the i-th element in the initial body composition data related to the preset exercise goal, D1[i] represents the index data corresponding to D0[i] in the result body composition data, E represents the exercise energy consumption, w i Indicates the preset indicator weight of the corresponding indicator data. i , the weights for each body composition indicator can be pre-organized, empirically accounting for post-exercise body composition data measurement bias. These weights can then be dynamically assigned to calculate the fitness score based on the user's preset exercise goals. This formula converts the change in each body composition indicator of interest into an improvement per unit of energy consumption, thereby assessing the benefits of exercise per unit of input. The total benefit for each body composition indicator, or fitness score, is then derived by combining the weights.

[0057] Further optionally, the method further includes: generating a recommended exercise plan based on historical exercise records and corresponding historical fitness scores, wherein the recommended exercise plan includes a single type of exercise plan or an exercise plan of a combination of multiple types.

[0058] Specifically, the content and fitness score of each exercise of the target user can be stored, and after each exercise, the historical exercise plans can be sorted based on the historical fitness scores of all or the most recent period of time, and then the best one or the top three (such as the top three) exercise plans can be selected for recommendation. Further, based on the historical exercise records, the relationship curve between the action type, action intensity (frequency, number of times, etc.) and the fitness score can be obtained through model fitting, and the best combined exercise plan can be recommended, which can include each action intensity. In addition, the action intensity can also be directly optimized according to the recent historical fitness score. For the generated recommended exercise plan, it can be displayed by a display module such as a connected TV or mobile phone, so that the user can choose, thereby the personalized dynamic adjustment exercise plan can be combined with the user's body composition changes to optimize the exercise effect.

[0059] On the basis of the above technical solution, optionally, the method further includes: monitoring the actual motion parameters of the target user in real time, and generating voice prompt content according to the actual motion parameters and the corresponding benchmark motion parameters. The actual motion parameters may include motion amplitude and motion frequency, etc., and the benchmark motion parameters may be determined according to the corresponding historical motion parameters, or may be preset values. For example, if the current motion frequency is less than 90% of the historical motion frequency, a voice prompt is triggered, and the content of the voice prompt may be "Please speed up to XX times / minute". For another example, if a squat with an amplitude of 50 cm is currently recommended, and it is detected in real time that the user's squat amplitude is only 40 cm, a voice prompt is triggered, and the content of the voice prompt may be "Please squat deeper to thigh level". For the generated voice prompt content, TTS technology can be integrated to implement voice prompts, and the voice prompts can be played through built-in speakers or external devices.

[0060] On the basis of the above technical solution, optionally, the method further includes: obtaining the historical best action video of the target user, and performing time alignment and image fusion on the historical best action video and the standard action video to obtain the guidance video content.

[0061] Specifically, the best historical action video V of the target user can be retrieved from the database best , such as the video with the parameters such as movement amplitude and movement frequency that are closest to the corresponding standard movement. Then the dynamic time warping (DTW) algorithm can be used to warp the historical best action video V best With standard action video V std The joint point motion trajectories extracted from the video are time-aligned to ensure the action matching between video frames. For each frame, the image fusion technology is used to align the best action video V best With standard action video V std The corresponding pixels in are calculated according to a certain ratio, such as the output pixel V guide =0.7V std +0.3V best , and you can use FFmpeg to superimpose semi-transparent layers to generate guidance video content. Further, you can also give the best action video in history V best With standard action video V std A specific weight ratio is set to allow the AI model to synthesize action guidance videos that match the user's body shape. For the generated guidance video content, the built-in video synthesis module can be used to generate real-time guidance videos, which can be played on a connected display module such as a TV or mobile phone for user learning, and further corresponding action correction suggestions can be provided.

[0062] To illustrate, let's use a specific example. The first involves a home fat loss scenario. The user's initial body fat percentage is 28%, with a monthly goal of 2%. After the initial measurement, the program recommends "high-frequency jumping jacks (120 reps / minute) + squats (50cm). Real-time detection indicates the user's squat is only 40cm deep, prompting a voice prompt: "Please squat deeper to thigh level." After the exercise, the user's body fat percentage drops to 27.7%, resulting in a fitness score of 88% based on the aforementioned fitness score formula. A video guide is then created for this exercise, and historical exercise records are combined to analyze the optimal combination of movements and the intensity of each movement. The second involves a gym-based muscle-building scenario. The user's initial muscle mass is low, with a monthly goal of 1kg. Based on historical data, the program recommends "slow squats (8 reps / minute) + high-protein intake reminders." Real-time detection indicates the user's movement frequency is too fast (12 reps / minute), triggering a voice prompt: "Reduce speed and focus on force." After the exercise, muscle mass increases by 0.1kg, and the fitness score for the same exercise is updated. If the score decreases, a higher weight is recommended.

[0063] The technical solution provided by the embodiment of the present invention first obtains the motion video data and current body composition data of the target user's current exercise, then determines the movement amplitude, movement frequency, and number of movements of the current exercise based on the obtained motion video data, and then determines the exercise energy consumption of the current exercise based on the obtained current body composition data, movement amplitude, movement frequency, and number of movements. By combining multimodal data, accurate calculation of exercise energy consumption is achieved, thereby more accurately evaluating the user's exercise effect. Moreover, by using actual exercise videos, the user's actual movement details are fully taken into account, which can also make the calculation of exercise energy consumption more accurate.

[0064] Example 2

[0065] Figure 2 This is a schematic diagram of the structure of the sports health monitoring device provided in the second embodiment of the present invention. The device can be implemented by hardware and / or software, and can generally be integrated into a computer device to execute the sports health monitoring method provided in any embodiment of the present invention. Figure 2 As shown, the device includes:

[0066] The user data acquisition module 21 is used to acquire the target user's current exercise video data and current body composition data;

[0067] An action content determination module 22 is configured to determine an action amplitude, an action frequency, and an action number based on the motion video data;

[0068] The exercise energy consumption determination module 23 is used to determine the exercise energy consumption of the current exercise according to the current body composition data, the movement amplitude, the movement frequency and the number of movements.

[0069] The technical solution provided by the embodiment of the present invention first obtains the motion video data and current body composition data of the target user's current exercise, then determines the movement amplitude, movement frequency, and number of movements of the current exercise based on the obtained motion video data, and then determines the exercise energy consumption of the current exercise based on the obtained current body composition data, movement amplitude, movement frequency, and number of movements. By combining multimodal data, accurate calculation of exercise energy consumption is achieved, thereby more accurately evaluating the user's exercise effect. Moreover, by using actual exercise videos, the user's actual movement details are fully taken into account, which can also make the calculation of exercise energy consumption more accurate.

[0070] Based on the above technical solution, optionally, the exercise energy consumption determination module 23 is specifically configured to:

[0071] E=α×A×f×N+β×(Ds / Dr);

[0072] Among them, E represents the exercise energy consumption, A represents the movement amplitude, f represents the movement frequency, N represents the number of movements, Ds represents the current body fat percentage in the current body composition data, Dr represents the preset baseline body fat percentage, α represents the first preset weight, and β represents the second preset weight.

[0073] On the basis of the above technical solution, optionally, the device further includes:

[0074] A body composition data acquisition module is used to acquire the initial body composition data of the target user before the current exercise and the resulting body composition data after the current exercise;

[0075] A fitness score determination module is used to determine the fitness score of the current exercise based on a preset exercise goal according to the initial body composition data, the result body composition data and the exercise energy consumption after determining the exercise energy consumption of the current exercise according to the current body composition data, the movement amplitude, the movement frequency and the number of movements.

[0076] Based on the above technical solution, optionally, the fitness score determination module is specifically used to:

[0077] S=∑ i w i ×(D1[i]-D0[i]) / E;

[0078] Wherein, S represents the fitness score, D0[i] represents the index data of the i-th element in the initial body composition data related to the preset exercise goal, D1[i] represents the index data corresponding to D0[i] in the result body composition data, E represents the exercise energy consumption, w i Indicates the preset indicator weight of the corresponding indicator data.

[0079] On the basis of the above technical solution, optionally, the device further includes:

[0080] The exercise plan recommendation module is used to generate a recommended exercise plan based on historical exercise records and corresponding historical fitness scores. The recommended exercise plan includes a single type of exercise plan or a combination of multiple types of exercise plans.

[0081] On the basis of the above technical solution, optionally, the device further includes:

[0082] a voice prompt module, configured to monitor the actual motion parameters of the target user in real time and generate voice prompt content according to the actual motion parameters and corresponding benchmark motion parameters; and / or,

[0083] The video guidance module is used to obtain the historical best action video of the target user, and perform time alignment and image fusion on the historical best action video and the standard action video to obtain guidance video content.

[0084] The sports health monitoring device provided in the embodiment of the present invention can execute the sports health monitoring method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0085] It is worth noting that in the embodiment of the above-mentioned sports health monitoring device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0086] Example 3

[0087] Figure 3 The structural diagram of the computer device provided in the third embodiment of the present invention shows a block diagram of an exemplary computer device suitable for implementing the embodiment of the present invention. Figure 3 The computer device shown is only an example and should not limit the functionality and scope of use of the embodiments of the present invention. Figure 3 As shown, the computer device includes a processor 31, a memory 32, an input device 33 and an output device 34; the number of processors 31 in the computer device can be one or more. Figure 3 Taking a processor 31 as an example, the processor 31, memory 32, input device 33 and output device 34 in the computer device can be connected through a bus or other means. Figure 3 The bus connection is taken as an example.

[0088] The memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the sports health monitoring method in the embodiments of the present invention (for example, the user data acquisition module 21, the action content determination module 22, and the exercise energy consumption determination module 23 in the sports health monitoring device). The processor 31 executes the software programs, instructions, and modules stored in the memory 32 to execute various functional applications and data processing of the computer device, thereby implementing the above-mentioned sports health monitoring method.

[0089] The memory 32 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on the use of the computer device. Furthermore, the memory 32 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some instances, the memory 32 may further include memory remotely located relative to the processor 31, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0090] The input device 33 can be used to obtain the target user's current exercise video data and current body composition data, and generate key signal input related to user settings and function control of the computer device, etc. The output device 34 can be used to display various monitoring results to the user, etc.

[0091] Example 4

[0092] A fourth embodiment of the present invention further provides a storage medium containing computer-executable instructions. When the computer-executable instructions are executed by a computer processor, the computer-executable instructions are used to perform a sports health monitoring method. The method includes:

[0093] Obtain the target user's current exercise video data and current body composition data;

[0094] Determining the movement amplitude, movement frequency, and movement number according to the movement video data;

[0095] The exercise energy consumption of the current exercise is determined according to the current body composition data, the movement amplitude, the movement frequency, and the number of movements.

[0096] The storage medium can be any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media, such as CD-ROMs, floppy disks, or tape devices; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (such as hard disks or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the computer system in which the program is executed, or may be located in a different second computer system that is connected to the computer system via a network (such as the Internet). The second computer system may provide program instructions to the computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations (e.g., in different computer systems connected via a network). The storage medium may store program instructions (e.g., embodied as a computer program) that may be executed by one or more processors.

[0097] Of course, the storage medium containing computer-executable instructions provided by an embodiment of the present invention is not limited to the method operations described above, and can also execute related operations in the sports health monitoring method provided by any embodiment of the present invention.

[0098] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0099] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0100] Through the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented with the help of software and necessary general-purpose hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0101] Example 5

[0102] Embodiment 5 of the present invention also provides a computer program product, which includes a computer program (also referred to as code, instructions). The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it is used to execute the sports health monitoring method provided in any of the above embodiments, and has the corresponding beneficial effects of the execution method.

[0103] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the concept of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A sports health monitoring method, characterized in that: include: Obtain the target user's current exercise video data and current body composition data; Determining the movement amplitude, movement frequency, and movement number according to the movement video data; The exercise energy consumption of the current exercise is determined according to the current body composition data, the movement amplitude, the movement frequency, and the number of movements.

2. The sports health monitoring method according to claim 1, characterized in that: The determining of the exercise energy consumption of the current exercise according to the current body composition data, the movement amplitude, the movement frequency, and the number of movements includes: E=α×A×f×N+β×(Ds / Dr); Among them, E represents the exercise energy consumption, A represents the movement amplitude, f represents the movement frequency, N represents the number of movements, Ds represents the current body fat percentage in the current body composition data, Dr represents the preset baseline body fat percentage, α represents the first preset weight, and β represents the second preset weight.

3. The sports health monitoring method according to claim 1, characterized in that: The method further comprises: Obtaining the target user's initial body composition data before the exercise and the resulting body composition data after the exercise; After determining the exercise energy consumption of the current exercise according to the current body composition data, the movement amplitude, the movement frequency, and the number of movements, the method further includes: A fitness score for the current exercise based on a preset exercise goal is determined according to the initial body composition data, the result body composition data, and the exercise energy consumption.

4. The sports health monitoring method according to claim 3, characterized in that: The determining, based on the initial body composition data, the result body composition data, and the exercise energy consumption, of a fitness score for the current exercise based on a preset exercise goal includes: S=∑ i w i ×(D1[i]-D0[i]) / E; Wherein, S represents the fitness score, D0[i] represents the index data of the i-th element in the initial body composition data related to the preset exercise goal, D1[i] represents the index data corresponding to D0[i] in the result body composition data, E represents the exercise energy consumption, w i Indicates the preset indicator weight of the corresponding indicator data.

5. The sports health monitoring method according to claim 3, characterized in that: The method further comprises: A recommended exercise plan is generated according to historical exercise records and corresponding historical fitness scores, wherein the recommended exercise plan includes a single type of exercise plan or an exercise plan combining multiple types.

6. The sports health monitoring method according to claim 1, characterized in that: The method further comprises: monitoring the actual motion parameters of the target user in real time, and generating voice prompt content according to the actual motion parameters and corresponding benchmark motion parameters; and / or, The historical best action video of the target user is obtained, and the historical best action video is time-aligned and image-fused with the standard action video to obtain the guidance video content.

7. A sports health monitoring device, characterized in that: include: The user data acquisition module is used to obtain the target user's current exercise video data and current body composition data; An action content determination module, configured to determine an action amplitude, an action frequency, and an action number according to the motion video data; The exercise energy consumption determination module is used to determine the exercise energy consumption of the current exercise based on the current body composition data, the movement amplitude, the movement frequency and the number of movements.

8. A computer device, characterized in that: include: one or more processors; a memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the sports health monitoring method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the exercise health monitoring method as described in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the sports health monitoring method according to any one of claims 1 to 6.

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