Psychosomatic health assessment method, exercise recommendation method, psychosomatic growth support system

Abstract

This invention relates to the field of physical and mental health monitoring technology, specifically to methods for assessing physical and mental health, methods for recommending exercise, and systems for supporting physical and mental growth. When assessing an individual's physical and mental health, this invention considers not only psychological health levels and physical indicators but also the individual's physical activity status. Since physical activity status is objective data, and existing research has shown a close correlation between physical activity and physical and mental health, this invention uses quantifiable and health-related objective data on physical activity status to assess physical and mental health, thereby improving the accuracy of the assessment results. Furthermore, this invention recommends exercise programs based on the assessment results of physical and mental health, ensuring that the recommended exercises can improve the individual's physical and mental health.

Description

Technical Field

[0001] This invention relates to the field of physical and mental health monitoring technology, specifically to physical and mental health assessment methods, exercise recommendation methods, and physical and mental growth support systems. Background Technology

[0002] An individual's physical and mental health plays a crucial role in their healthy development. For example, the physical and mental health of adolescents is of paramount importance to their growth and therefore receives considerable attention. Existing research demonstrates that physical exercise is a significant way to promote individual physical and mental health. Therefore, based on individual physical and mental health assessments, appropriate sports activities can be recommended to improve an individual's well-being.

[0003] Existing technologies only assess an individual's physical and mental health based on their physical and psychological conditions, while neglecting the impact of physical exercise on physical and mental health, resulting in low accuracy of the assessment results.

[0004] In conclusion, existing technologies result in lower accuracy in the assessment of physical and mental health.

[0005] Therefore, existing technologies still need to be improved and enhanced. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for assessing physical and mental health, a method for recommending exercise, and a system for supporting physical and mental growth, thereby resolving the issue of low accuracy in physical and mental health assessments caused by existing technologies.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for assessing physical and mental health, comprising: Based on an individual's physical data, assess the individual's physical condition and obtain physical indicators; obtain the individual's physical exercise status and mental health level; based on the individual's physical indicators, physical exercise status, and mental health level, obtain the individual's physical and mental health assessment results.

[0008] In one implementation, the shape data includes morphological data and / or visual data.

[0009] In one implementation, the methods for obtaining morphological data include: Depth images of an individual are acquired using a depth camera, and based on these images, the individual's basic morphology and / or facial micro-expressions are obtained. The degree of scoliosis is measured, and the degree of scoliosis and / or basic morphology and / or facial micro-expressions are used as morphological data.

[0010] In one implementation, physical exercise status includes basic physiological and / or exercise data of an individual during exercise.

[0011] In one implementation, motion data includes overall motion data generated across all sports and local motion data generated within each sports.

[0012] Secondly, embodiments of the present invention also provide a method for recommending exercise, comprising: Obtain individual attribute data and determine recommended exercise programs based on the attribute data; during the individual's exercise program, optimize the exercise program based on the individual's physical and mental health assessment results obtained from the above-mentioned physical and mental health assessment methods.

[0013] Thirdly, embodiments of the present invention also provide a physical and mental growth support system, comprising: The data acquisition module is used to acquire an individual's body shape data, physical exercise status, and mental health level; The assessment module is used to assess an individual's physique based on body shape data and obtain the individual's physical characteristics. The early warning analysis module is used to obtain an individual's physical and mental health assessment results based on their physical signs, physical exercise status, and mental health level. The exercise prescription recommendation module is used to recommend exercise programs to individuals based on the results of physical and mental health assessments.

[0014] One implementation also includes: The management platform is used to manage the physical exercise status of each individual acquired by the data acquisition module; A digital profiling platform used to display individual data, including results of physical and mental health assessments.

[0015] Fourthly, embodiments of the present invention also provide a terminal device, wherein the terminal device includes a memory, a processor, and a physical and mental health assessment program stored in the memory and executable on the processor, wherein when the processor executes the physical and mental health assessment program, it implements the steps of the above-mentioned physical and mental health assessment method. Alternatively, the terminal device includes a memory, a processor, and a motion recommendation program stored in the memory and executable on the processor. When the processor executes the motion recommendation program, it implements the steps of the motion recommendation method described above.

[0016] Fifthly, embodiments of the present invention also provide a computer-readable storage medium storing a physical and mental health assessment program, which, when executed by a processor, implements the steps of the above-described physical and mental health assessment method. Alternatively, a motion recommendation program may be stored on a computer-readable storage medium, which, when executed by a processor, implements the steps of the motion recommendation method described above.

[0017] Beneficial Effects: This invention, when assessing an individual's physical and mental health, considers not only psychological well-being and physical indicators but also the individual's physical activity status. Since physical activity status is objective data, and existing research has demonstrated a close relationship between physical activity and physical and mental health, this invention uses quantifiable and health-related objective data—physical activity status—to assess physical and mental health, thereby improving the accuracy of the assessment results. Attached Figure Description

[0018] Figure 1 This is an overall flowchart of the present invention; Figure 2 This is a structural diagram of the recommendation system of the present invention; Figure 3 This is a block diagram illustrating the internal structure of a terminal device provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments and accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0020] Research has found that an individual's physical and mental health plays a crucial role in their healthy development. For example, the physical and mental health of adolescents is of paramount importance to their growth, and therefore receives considerable attention. Existing research demonstrates that physical exercise is a significant way to promote individual physical and mental health. Therefore, appropriate sports activities can be recommended to individuals based on their physical and mental health assessments to improve their well-being. However, current technology only assesses an individual's physical and psychological condition, neglecting the impact of physical exercise on physical and mental health, thus leading to lower accuracy in the assessment results.

[0021] To address the aforementioned technical problems, this invention provides a method for assessing physical and mental health, a method for recommending exercise, and a system for supporting physical and mental growth, thereby resolving the issue of low accuracy in physical and mental health assessments caused by existing technologies.

[0022] Example 1 provides a method for assessing physical and mental health. This method can be applied to a terminal device, which can be a terminal product with image acquisition and processing functions, such as a computer. In this example, as... Figure 1 As shown, the physical and mental health assessment method specifically includes the following steps: S100 assesses an individual's body based on their physical data to obtain their physical indicators. S200, to obtain an individual's physical exercise status and mental health level; S300 is an assessment of an individual's physical and mental health based on their physical indicators, physical activity status, and mental health level.

[0023] The application scenarios of the physical and mental health assessment method in this embodiment are as follows: This process involves collecting physical data from individual adolescents, assessing their physical condition based on each data point, and determining whether each data point meets standards. These standards serve as physical indicators. The process also includes collecting data on adolescents' physical activity levels and mental health. Finally, based on these physical indicators, physical activity levels, and mental health assessments, the overall physical and mental well-being of the adolescents is evaluated. Physical education teachers, based on these assessments, can then tailor more targeted physical education courses and exercise plans for students, moving away from haphazardly assigning sports activities. This makes physical education and exercise guidance more scientific and precise, effectively improving the quality of physical education and genuinely promoting the healthy physical and mental development of adolescents.

[0024] In this embodiment, the body data in step S100 includes at least one of morphological data and visual data. The morphological data includes at least one of basic shape, facial micro-expression, degree of scoliosis, kyphosis, leg length, degree of body center of gravity shift, and degree of neck forward extension.

[0025] In this embodiment, visual acuity data of an individual is obtained through a visual acuity measuring instrument. The reason why visual acuity is considered as a factor when assessing physical and mental health is that visual loss or decline not only increases mental health problems such as depression, anxiety, and loneliness, but also increases physical health problems such as hypertension and thyroid disease. Therefore, this embodiment adds visual acuity data as a factor when assessing physical and mental health, which can improve the accuracy of the assessment results.

[0026] The degree of scoliosis in an individual is measured using a spinal measurement device worn on the individual. It can also acquire depth images from both sides of the individual, calculate the depth values ​​at various points on the individual's sides, and determine the degree of scoliosis based on these depth values.

[0027] Because facial microexpressions are brief and uncontrolled facial movements that can reveal an individual's true emotions that they are trying to hide, considering facial microexpressions when assessing physical and mental health can improve the accuracy of the assessment results.

[0028] The basic morphology includes one or more of the following: height, arm length, shoulder width, hand size, and head size. This embodiment uses a depth camera to acquire depth images of an individual, and then applies a neural network model to the depth images to obtain the basic morphology of the human body and facial micro-expressions.

[0029] The neural network model includes a preprocessing module, a human body 3D branch network, and a face 3D branch network. In this embodiment, the human body 3D branch network adopts the SMPL model, where SMPL stands for Skinned Multi-Person Linear, and the face 3D branch network adopts the 3DMM model, where 3DMM stands for 3D Morphable Model.

[0030] The preprocessing module is used to perform facial region contour recognition and human body contour recognition on the depth image to obtain a facial depth image that includes only the face and a human body depth image that includes only the human body.

[0031] The Human 3D Branch Network is used to analyze human depth images to obtain the basic morphology of an individual. Specifically, the Human 3D Branch Network extracts features from the human depth image to obtain the individual's feature points and their spatial location information. Based on this spatial location information, it calculates the topological relationships between these feature points (the topological relationships are the topological graph formed by the various feature points of the human body), thereby obtaining three-dimensional human shape information. Finally, based on this human shape information, the basic morphology of the individual is obtained, including height, arm length, shoulder width, hand size, and head size.

[0032] The 3D face subnet analyzes facial depth images to obtain an individual's facial micro-expressions. Specifically, the 3D face subnet extracts features from the facial depth image to extract facial feature points such as the eyes, nose, and corners of the mouth, along with their spatial location information. Based on this spatial location information, it determines the size, position, and distance of the facial contours, and calculates the offset between these offsets and the corresponding feature point attributes on a standard face template. The resulting offset is then used to determine the facial micro-expressions corresponding to that offset.

[0033] The aforementioned human body 3D branch network and face 3D branch network in the neural network model are two parallel networks. Therefore, the neural network model can output the basic shape and facial micro-expressions based on the same depth image.

[0034] In this embodiment, step S100 involves inputting multimodal data, including basic morphology (height, arm length, shoulder width, hand size, and head size), facial micro-expressions, scoliosis degree, and vision data, into a preset multidimensional physical and mental assessment model. The model outputs physical indicators, which are used to characterize whether height, arm length, shoulder width, hand size, head size, facial micro-expressions, scoliosis degree, and vision are within the normal range.

[0035] The aforementioned multidimensional physical and mental health assessment model consists of convolutional layers, residual layers, batch normalization layers, and activation layers. The training method for the multidimensional physical and mental health assessment model includes constructing a sample dataset and using the sample dataset to train the model.

[0036] The construction of the sample dataset includes: recruiting hundreds of thousands of people (who can be teenagers or other groups) as research subjects through cooperation with multiple schools and communities, and regularly measuring each person's basic morphology, facial micro-expressions, vision, and degree of scoliosis in order to construct a sample dataset corresponding to the body shape data.

[0037] The sample dataset consisting of the above multi-source data is input into the multi-dimensional physical and mental assessment model so that the model can learn different body shape data, thereby enabling the model to determine whether the body shape data conforms to the standard data after learning.

[0038] The physical exercise status in step S200 includes at least one of the basic physiological data and exercise data generated during individual exercise. The basic physiological data includes heart rate, and the exercise data includes overall exercise data generated in all sports and local exercise data generated in each sports. The overall exercise data includes the number of steps and duration of exercise generated during individual exercise, and the local exercise data includes the individual's posture, speed and strength in different sports.

[0039] The basic physiological data and overall movement data mentioned above are monitored in real time by smart bracelets worn on individuals, and local movement data of individuals in different sports are monitored in real time by motion sensors installed in school stadiums and community sports fields.

[0040] The mental health level in step S200 is obtained by having individuals (such as adolescents) fill out a psychological test form regularly.

[0041] This embodiment can also periodically collect an individual's electroencephalogram (EEG) signals, and analyze the EEG signals to obtain the individual's mental health level.

[0042] In step S300 of this embodiment, the individual's physical indicators, physical exercise status, and mental health level are input into a preset early warning analysis model. The early warning analysis model outputs a value, which is compared with the standard value corresponding to no physical or mental abnormalities to obtain the category of physical or mental abnormality. The abnormality categories include anxiety, depression, and inattention. Then, based on the degree to which the value output by the early warning analysis model deviates from the above standard value, the degree of anxiety, the degree of depression, and the degree of inattention are judged.

[0043] The early warning analysis model consists of convolutional layers, residual layers, batch normalization layers, and activation layers. The training method involves: using pre-trained multi-dimensional physical and mental health assessment models as sample body shape data; then inputting physical exercise status and mental health levels, along with physical indicators output by the multi-dimensional physical and mental health assessment models, into the early warning analysis model; and adjusting the parameters of the early warning analysis model based on its output data to complete the training. In other words, the multi-dimensional physical and mental health assessment model is trained first, and then the early warning analysis model is trained based on the trained multi-dimensional physical and mental health assessment model.

[0044] Example 2, based on Example 1, provides an exercise recommendation method, including: acquiring an individual's attribute data and determining recommended exercise programs for the individual based on the attribute data; obtaining the individual's physical and mental health assessment results based on Example 1 during the individual's execution of the exercise program; and optimizing the exercise program based on the physical and mental health assessment results.

[0045] The attribute data in this embodiment includes an individual's age, gender, physical condition, athletic ability, interests, and psychological stress. A pre-set exercise prescription knowledge base stores the attribute data and corresponding exercise programs. Therefore, suitable exercise programs can be matched to an individual based on their attribute data. For example, if an individual has poor flexibility, yoga or stretching exercises are recommended; if an individual experiences high psychological stress, jogging, swimming, or basketball are recommended.

[0046] This embodiment prioritizes six attribute data points—age, gender, physical condition, athletic ability and hobbies, and psychological stress—based on their deviation from their respective standard values. Then, it recommends exercise programs to individuals based on the attribute with the lowest priority. This is because the lowest priority indicates the attribute deviates the most from its standard value, meaning it significantly impacts physical and mental health. Therefore, recommending exercise programs tailored to this attribute data aims to improve the individual's physical and mental well-being by addressing that attribute.

[0047] After an individual performs the recommended exercise program, their body shape data, physical exercise status, and mental health level are collected. Using the physical and mental health assessment method in Example 1, the individual's physical and mental health level is assessed. Based on the assessment results, the recommended exercise program is optimized, and the optimized exercise program is recommended to the individual again to continuously improve the individual's physical and mental health.

[0048] This embodiment recommends exercise programs based on individual differences, which not only improves the corrective effect on physical and mental health, but also, because the personalized exercise programs fully consider individual interests and hobbies, encourages active participation, thus enhancing individual enthusiasm and adherence to physical exercise. For example, for teenagers who enjoy dance, an exercise prescription incorporating dance elements can correct physical and mental health abnormalities while also increasing their love for exercise, creating a virtuous cycle. Long-term adherence to exercise has a sustained positive impact on physical and mental health.

[0049] Example 3, based on Example 2, provides a physical and mental growth support system, such as... Figure 2 As shown, the system includes a data acquisition module and an evaluation module (i.e., ...). Figure 2 The system includes a multi-dimensional physical and mental assessment module, an early warning analysis module, an exercise prescription recommendation module, a management platform, and a digital profiling platform.

[0050] The data acquisition module is used to acquire body shape data, physical exercise status, and mental health level of Example 1.

[0051] The evaluation module is used to apply the neural network model of Example 1 to the body data to obtain the physical indicators of Example 1.

[0052] The early warning analysis module is used to apply the neural network model of Example 1 to physical indicators, physical exercise status, and mental health level to obtain the physical and mental health assessment results of Example 1.

[0053] The exercise prescription recommendation module is used to recommend exercise programs in Example 2.

[0054] The management platform and the digital profiling platform are integrated platforms. The management platform is connected to the smart bracelet in Example 1 and is used to manage and store the data collected by the smart bracelet.

[0055] The management platform is also connected to the motion sensors installed in the school's sports stadium as described in Example 1. The management platform is used to manage and store the motion data collected by the motion sensors during physical education classes. Teachers can use the management platform to view whether each student's exercise intensity meets the standards and their mastery of motor skills.

[0056] The management platform is also connected to cameras that capture videos of students exercising during physical education classes. The platform analyzes each frame of the video to determine whether the students' movements are performed correctly.

[0057] The management platform is connected to both the parents' mobile app and the child's smart device. The smart device is worn by the child to collect data on the child's completion of physical education tasks such as jump rope and sit-ups. The smart device then sends the completion status of these tasks to the parents' mobile app through the management platform, allowing parents to monitor their child's home physical education homework in a timely manner.

[0058] The management platform is connected to the community's smart sports facilities, which include smart fitness equipment and smart running tracks. When an individual uses a smart sports facility through facial recognition, the smart sports facility uploads the facial information to the management platform. The management platform then assigns an ID code to the facial information. The smart sports facility then uploads the individual's exercise data to the management platform, which stores the exercise data in the storage area corresponding to the ID code.

[0059] The management platform integrates the aforementioned cross-domain data to generate individual sports learning profiles, including information on sports participation, skill progress, and the development of exercise habits. Through data analysis, it provides targeted learning suggestions for each individual; for example, for individuals with weaker skills in a particular sport, it pushes relevant instructional videos and training plans.

[0060] To improve the accuracy of monitoring physiological and psychological indicators, individual wearable devices can also have blood oxygen and blood pressure detection functions. For example, near-infrared spectroscopy can be used to monitor blood oxygen levels to assess an individual's cognitive load and emotional state; flexible pressure sensors can be used to make the straps of wearable devices for more accurate blood pressure measurement. This monitoring data is transmitted to a cloud server in real time and analyzed using big data analytics combined with a pre-set early warning analysis model. Once the pre-set early warning analysis model determines an individual's physical and mental health level, the management platform immediately sends an early warning message to the individual's guardian and provides corresponding intervention suggestions, such as arranging psychological counseling and adjusting exercise plans.

[0061] The digital profiling platform displays various data about an individual through a graphical interface, including basic information, physical and mental health indicators, athletic performance, and academic achievements. Through data visualization technology, complex data is transformed into intuitive and easy-to-understand charts and graphs. For example, line graphs show an individual's or user's emotional fluctuations over a period of time, and radar charts show their ability levels in different sports. The platform provides online exercise prescription generation, querying, and adjustment functions. Individuals and their guardians can log in to the platform to view exercise prescriptions. The platform also includes an interactive communication module to facilitate communication and collaboration among all parties to promote healthy physical and mental development.

[0062] This embodiment combines a digital profiling platform with an exercise prescription recommendation module, breaking down barriers between home, school, and community to achieve information sharing and collaborative linkage. All parties can work together on a unified platform to focus on the physical and mental development of adolescents, forming a closely cooperative educational network. For example, school-developed physical exercise plans can be shared with parents and the community through the platform. Parents can provide feedback on their children's implementation at home, and the community can offer support and supplements based on overall needs, jointly creating a positive growth environment for adolescents and comprehensively promoting their healthy physical and mental development.

[0063] Based on the above embodiments, the present invention also provides a terminal device, the principle block diagram of which can be as follows: Figure 3 As shown, the terminal device includes a processor, memory, network interface, and display screen connected via a system bus. The processor provides computing 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 network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for assessing physical and mental health. The display screen can be a liquid crystal display (LCD) or an e-ink display.

[0064] Those skilled in the art will understand that Figure 3 The schematic diagram shown is only a partial structural diagram related to the present invention and does not constitute a limitation on the terminal device to which the present invention is applied. The specific terminal device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0065] In one embodiment, a terminal device is provided, comprising a memory, a processor, and a physical and mental health assessment program stored in the memory and executable on the processor. When the processor executes the physical and mental health assessment program, it implements the following operation instructions: Based on an individual's body shape data, the individual's body is assessed to obtain the individual's physical characteristics; To obtain the individual's physical exercise status and the individual's mental health level; Based on the individual's physical indicators, physical exercise status, and mental health level, the individual's physical and mental health assessment results are obtained.

[0066] Those skilled in the art will understand that all or part of the processes in the methods of 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 of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing physical and mental health based on multi-dimensional analysis of body shape data, characterized in that, include: Depth images of an individual are acquired using a depth camera; A neural network model is applied to the depth image to simultaneously obtain the basic shape and facial micro-expressions of the individual, wherein the neural network model includes a parallel human 3D branch network for outputting the basic shape and a human face 3D branch network for outputting the facial micro-expressions. The degree of scoliosis of the individual is obtained by using a wearable spinal measurement device and / or by calculation based on the depth image; Using the basic morphology, facial micro-expressions, and degree of scoliosis as morphological data, the individual's body is assessed to obtain the individual's physical indicators; To obtain the individual's physical exercise status and the individual's mental health level; Based on the individual's physical indicators, physical exercise status, and mental health level, the individual's physical and mental health assessment results are obtained.

2. The method for assessing physical and mental health according to claim 1, characterized in that, The application of a neural network model to the depth image includes: The preprocessing module performs facial region contour recognition and human body contour recognition on the depth image to obtain a facial depth image that includes only the face and a human body depth image that includes only the human body. The human body depth image is analyzed by the human body 3D branch network to obtain the basic shape, wherein the basic shape includes height, arm length, shoulder width, hand size and head size; The facial depth image is feature extracted using the 3D face branch network. Facial feature points of the eyes, nose, and mouth, as well as the spatial location information of each facial feature point, are extracted. The facial contour attributes are determined based on the spatial location information of each facial feature point, and offset calculation is performed with a standard face template to obtain the facial micro-expression.

3. The method for assessing physical and mental health according to claim 1, characterized in that, The process of obtaining the degree of scoliosis of the individual through a wearable spinal measurement device and / or calculation based on the depth image includes: The degree of scoliosis of the individual is measured by a spinometer worn on the individual; and / or The depth images of both sides of the individual are acquired, the depth values ​​of each point on the side of the individual are calculated using the depth images of both sides, and the degree of scoliosis is calculated based on each depth value.

4. The method for assessing physical and mental health according to claim 1, characterized in that, Also includes: Visual acuity data of the individual was obtained using a visual acuity measuring instrument; The visual acuity data and the morphological data are used together as body shape data to assess the individual's physique.

5. The method for assessing physical and mental health according to claim 1, characterized in that, The method of using the basic morphology, facial micro-expressions, and degree of scoliosis as morphological data to assess the individual's body includes: The basic morphology, facial micro-expressions, and degree of scoliosis are input into a preset multidimensional physical and mental assessment model. The multidimensional physical and mental assessment model outputs the physical indicators, which are used to characterize whether the height, arm length, shoulder width, hand size, and head size meet the standard data, whether the facial micro-expressions are normal, and whether the degree of scoliosis is within the normal range.

6. The method for assessing physical and mental health according to claim 5, characterized in that, The multidimensional physical and mental assessment model consists of convolutional layers, residual layers, batch normalization layers, and activation layers. The model is trained on a sample dataset constructed by recruiting a large number of people as research subjects through cooperation with multiple schools and communities, and regularly measuring their body shape data.

7. A motion recommendation method based on multi-dimensional analysis of body data, characterized in that, include: Obtain individual attribute data, and based on the attribute data, determine the sports activities recommended to the individual; During the individual's performance of the exercise, the exercise is optimized based on the individual's physical and mental health assessment results obtained by the physical and mental health assessment method according to any one of claims 1-6.

8. A physical and mental growth support system based on multi-dimensional analysis of body data, characterized in that, include: Depth image acquisition module, used to acquire depth images of an individual using a depth camera; The body data analysis module is used to apply a neural network model containing parallel human 3D branch networks and human face 3D branch networks to the depth image, simultaneously obtain the basic shape and facial micro-expressions of the individual, and obtain the degree of scoliosis of the individual through a wearable spinal measuring device and / or based on the depth image. The assessment module is used to assess the individual's body by using the basic morphology, facial micro-expressions, and degree of scoliosis as morphological data, and to obtain the individual's physical indicators. The data acquisition module is used to acquire the individual's physical exercise status and mental health level; The early warning analysis module is used to obtain the individual's physical and mental health assessment results based on the individual's physical indicators, physical exercise status, and mental health level. The exercise prescription recommendation module is used to recommend exercise programs to the individual based on the results of the physical and mental health assessment.

9. A terminal device, characterized in that, The terminal device includes a memory, a processor, and a physical and mental health assessment program stored in the memory and executable on the processor. When the processor executes the physical and mental health assessment program, it implements the steps of the physical and mental health assessment method as described in any one of claims 1-6. Alternatively, the terminal device includes a memory, a processor, and a motion recommendation program stored in the memory and executable on the processor. When the processor executes the motion recommendation program, it implements the steps of the motion recommendation method as described in claim 7.

10. A computer-readable storage medium, characterized in that, The calculation A computer-readable storage medium stores a physical and mental health assessment program, which, when executed by a processor, implements the steps of the physical and mental health assessment method as described in any one of claims 1-6. Alternatively, the computer-readable storage medium stores an exercise recommendation program, which, when executed by a processor, implements the steps of the exercise recommendation method as described in claim 7.

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