Exercise management method and device of personal health intelligent AI based on Internet of Things

Abstract

The invention relates to the technical field of the Internet of Things, in particular to an exercise management method and device for personal health intelligent AI based on the Internet of Things. The method comprises the steps that under the condition that a motion management request initiated by a user is obtained, motion demonstration materials corresponding to a motion type are inquired from a demonstration material library according to the motion type carried in the motion management request; the motion demonstration material comprises a material in a video form; according to the current health state, the body parameter information and the historical motion record of the user, adjusting the motion frequency and motion amplitude of each demonstration motion contained in the motion demonstration material to obtain a personalized demonstration material corresponding to the user; and feeding back the personalized demonstration material to the user.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things technology, and particularly to a motion management method and device for personal health intelligent AI based on the Internet of Things. Background Art

[0002] Intelligent motion management based on personal health refers to using modern information technology, artificial intelligence algorithms, big data analysis and other means to provide all-round motion management services such as customized and scientific motion plans, real-time monitoring, data analysis, and health advice according to factors such as an individual's health status, exercise habits, and physical needs. This management method aims to help individuals achieve more efficient fitness effects, improve physical health levels, and prevent potential health risks.

[0003] Although many intelligent motion management applications on the current market claim to provide personalized services, in fact, their algorithms may only generate motion plans based on a limited number of dimensions (such as age, gender, weight), ignoring more individual difference factors that may affect the motion effect, so there is an urgent need for improvement. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a motion management method and device for personal health intelligent AI based on the Internet of Things that can achieve personalized motion management.

[0005] In a first aspect, the present application provides a motion management method for personal health intelligent AI based on the Internet of Things, the method comprising: When a motion management request initiated by a user is obtained, query, according to the motion type carried in the motion management request, motion demonstration materials corresponding to the motion type from a demonstration material library; the motion demonstration materials include materials in video form; Adjust the action frequency and action amplitude of each demonstration action included in the motion demonstration materials according to the user's current health status, body parameter information, and historical motion records to obtain personalized demonstration materials corresponding to the user; Feed back the personalized demonstration materials to the user.

[0006] In one embodiment, adjusting the action frequency and action amplitude of each demonstration action included in the motion demonstration materials according to the user's current health status, body parameter information, and historical motion records to obtain personalized demonstration materials corresponding to the user includes: Determine the user's health abnormal parts and the abnormal degree corresponding to the health abnormal parts according to the user's current health status; Select at least one target action from each demonstration action of the motion demonstration materials that has an improvement effect on the health abnormal parts; Adjust the action frequency of each target action according to the degree of abnormality corresponding to the health abnormal part and the user's historical exercise records, and obtain the target frequency value corresponding to each target action; Adjust the action amplitude of each target action according to the difference between the body parameter information of the demonstrator corresponding to the demonstration action and the user's body parameter information, and obtain the target amplitude value corresponding to each target action; Determine the number of repetitions corresponding to each target action after adjusting the action amplitude and action frequency according to the expected exercise effect carried in the exercise management request and the improvement effect of each target action after adjusting the action amplitude and action frequency on the health abnormal part; Generate personalized demonstration materials corresponding to the user according to each target action after adjusting the action amplitude and action frequency, and the number of repetitions corresponding to each target action after adjusting the action amplitude and action frequency.

[0007] In one embodiment, adjusting the action frequency of each target action according to the degree of abnormality corresponding to the health abnormal part and the user's historical exercise records, and obtaining the target frequency value corresponding to each target action includes: Determine the upper limit value of the action frequency of each target action according to the degree of abnormality corresponding to the health abnormal part; Adjust the upper limit value of the action frequency of each target action according to the abnormal feedback situation in the historical exercise records, and obtain the reference frequency value of each target action; Adjust the reference frequency value of each target action according to the environmental parameters of the user's current environment and the user's current exercise equipment, and obtain the target frequency value corresponding to each target action.

[0008] In one embodiment, adjusting the reference frequency value of each target action according to the environmental parameters of the user's current environment and the user's current exercise equipment, and obtaining the target frequency value corresponding to each target action includes: Adjust the reference frequency value of each target action according to the influence degree of the environmental parameters of the user's current environment on the user's health status and the influence degree of the user's current exercise equipment on each target action.

[0009] In one embodiment, generating personalized demonstration materials corresponding to the user according to each target action after adjusting the action amplitude and action frequency, and the number of repetitions corresponding to each target action after adjusting the action amplitude and action frequency includes: Determine the historical exercise intensity and historical exercise duration of the historical exercise records, and the historical abnormal moment corresponding to the abnormal feedback situation in the historical exercise records in the historical exercise duration; Determine the current exercise duration of this exercise according to the target amplitude value, target frequency value and number of repetitions of each target action; Determine the current exercise intensity corresponding to this exercise based on each target action adjusted according to the action amplitude and action frequency, and the number of repetitions corresponding to each target action after adjusting the action amplitude and action frequency; Determine the predicted abnormal moment in this exercise based on the intensity difference between the historical exercise intensity and the current exercise intensity, the duration difference between the historical exercise duration and the current exercise duration, and the abnormal moment corresponding to the abnormal feedback situation in the historical exercise record in the historical exercise duration; Generate personalized demonstration materials corresponding to the user according to the predicted abnormal moment, each target action adjusted according to the action amplitude and action frequency, and the number of repetitions corresponding to each target action after adjusting the action amplitude and action frequency.

[0010] In one embodiment, generating personalized demonstration materials corresponding to the user according to the predicted abnormal moment, each target action adjusted according to the action amplitude and action frequency, and the number of repetitions corresponding to each target action after adjusting the action amplitude and action frequency includes: Generate initial demonstration materials corresponding to the user according to each target action adjusted according to the action amplitude and action frequency, and the number of repetitions corresponding to each target action after adjusting the action amplitude and action frequency; Take the target action corresponding to the predicted abnormal moment in the initial demonstration materials as the current action; Generate a relief action corresponding to the current action according to the action characteristics of the current action; Insert the relief action corresponding to the current action at the predicted abnormal moment of the initial demonstration materials corresponding to the user to obtain the personalized demonstration materials corresponding to the user.

[0011] In one embodiment, select at least one target action that has an improvement effect on the health abnormal part from each demonstration action of the exercise demonstration materials, including: When the number of the user's health abnormal parts is multiple, determine the restriction relationship between each health abnormal part; Select at least one target action that has an improvement effect on the health abnormal part from each demonstration action of the exercise demonstration materials according to the restriction relationship between each health abnormal part and the abnormal degree of each health abnormal part.

[0012] In one embodiment, select at least one target action that has an improvement effect on the health abnormal part from each demonstration action of the exercise demonstration materials according to the restriction relationship between each health abnormal part and the abnormal degree of each health abnormal part, including: Select at least one associated action that has an improvement effect on each health abnormal part from each demonstration action of the exercise demonstration materials according to the restriction relationship between each health abnormal part; Adjust the action postures of the associated actions according to the abnormality degrees of the abnormal parts of each health, and obtain the target actions corresponding to each associated action.

[0013] In one embodiment, the method further includes: if no exercise demonstration material is found in the demonstration material library according to the exercise type carried in the exercise management request, generate personalized demonstration material corresponding to the user according to the user's current health status, body parameter information, and historical exercise records.

[0014] In a second aspect, the present application further provides a motion management method device for an Internet of Things-based personal health intelligent AI, including: An acquisition module, configured to query exercise demonstration materials from a demonstration material library according to the exercise type carried in the exercise management request when an exercise management request initiated by a user is acquired; the exercise demonstration materials include materials in video form; A material generation module, configured to adjust the action frequency and action amplitude of each demonstration action included in the exercise demonstration material according to the user's current health status, body parameter information, and historical exercise records, and obtain personalized demonstration material corresponding to the user; A feedback module, configured to feedback the personalized demonstration material to the user.

[0015] In a third aspect, the present application further provides an Internet of Things device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: When an exercise management request initiated by a user is acquired, query exercise demonstration materials corresponding to the exercise type from a demonstration material library according to the exercise type carried in the exercise management request; the exercise demonstration materials include materials in video form; Adjust the action frequency and action amplitude of each demonstration action included in the exercise demonstration material according to the user's current health status, body parameter information, and historical exercise records, and obtain personalized demonstration material corresponding to the user; Feedback the personalized demonstration material to the user.

[0016] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: When an exercise management request initiated by a user is acquired, query exercise demonstration materials corresponding to the exercise type from a demonstration material library according to the exercise type carried in the exercise management request; the exercise demonstration materials include materials in video form; Adjust the action frequency and action amplitude of each demonstration action included in the exercise demonstration material according to the user's current health status, body parameter information, and historical exercise records, and obtain personalized demonstration material corresponding to the user; Feedback personalized demonstration materials to the user.

[0017] In a fifth aspect, the present application also provides a computer program product, including a computer program, which when executed by a processor implements the following steps: When a motion management request initiated by a user is obtained, query motion demonstration materials corresponding to the motion type from a demonstration material library according to the motion type carried in the motion management request; the motion demonstration materials include materials in video form; According to the user's current health status, body parameter information, and historical motion records, adjust the motion frequency and motion amplitude of each demonstration action included in the motion demonstration materials to obtain personalized demonstration materials corresponding to the user; Feedback the personalized demonstration materials to the user.

[0018] For the above-mentioned motion management method and device of the personal health intelligent AI based on the Internet of Things, the technical solution of the present application adjusts the motion demonstration materials in a personalized manner by comprehensively considering the user's current health status, body parameter information, and historical motion records, so as to more accurately meet the actual needs of the user. This personalized adjustment is not limited to the selection of the motion type, but also includes the detailed adjustment of the motion frequency and motion amplitude, making the motion plan more in line with the user's physical condition and motion habits. By providing personalized demonstration materials, users can more intuitively understand how to correctly perform motion actions and reduce the risk of motion injuries caused by improper actions. Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic flowchart of a motion management method of a personal health intelligent AI based on the Internet of Things in an embodiment; Figure 2 It is a schematic flowchart of the step of adjusting the motion frequency and motion amplitude of each demonstration action included in the motion demonstration materials in an embodiment; Figure 3 It is a schematic flowchart of the step of adjusting the motion frequency of each target action in an embodiment; Figure 4 It is a schematic flowchart of the step of generating personalized demonstration materials corresponding to the user in an embodiment; Figure 5Flow chart of steps for generating personalized demonstration materials corresponding to a user in another embodiment; Figure 6 Flow chart of steps for selecting at least one target action having an improvement effect on a health abnormal part in one embodiment; Figure 7 Flow chart of steps for selecting at least one target action having an improvement effect on a health abnormal part in another embodiment; Figure 8 Structural block diagram of a motion management method device of a personal health intelligent AI based on the Internet of Things in one embodiment; Figure 9 Internal structure diagram of an Internet of Things device in one embodiment. Detailed implementation manners

[0021] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0022] In an exemplary embodiment, as Figure 1 shown, a motion management method of a personal health intelligent AI based on the Internet of Things is provided. Taking the application of this method to an Internet of Things device as an example for illustration, wherein: S101, when a motion management request initiated by a user is obtained, according to the motion type carried in the motion management request, query the motion demonstration material corresponding to the motion type from the demonstration material library.

[0023] Among them, the motion demonstration material includes materials in video form.

[0024] Optionally, receive a motion management request initiated by a user. This request can be submitted through a user interface (such as an APP, a website, etc.), or may be triggered by other means (such as a voice command, an API call, etc.). Parse the motion management request and extract the motion type information carried therein. The motion type may include, but is not limited to, yoga, aerobics, etc.

[0025] Furthermore, according to a preset path or interface, access the database or file storing the motion demonstration materials, that is, the demonstration material library. In the demonstration material library, according to the parsed motion type, query the motion demonstration material matching the motion type. Among the query results, confirm that the found motion demonstration material includes materials in video form. The video material can intuitively display motion actions, skills or processes, providing a more intuitive learning experience for users.

[0026] S102. Adjust the action frequency and amplitude of each demonstration action included in the exercise demonstration material according to the user's current health status, physical parameter information, and historical exercise records to obtain personalized demonstration material corresponding to the user.

[0027] Among them, collect the user's current health status, physical parameter information, and historical exercise records. The current health status may include the user's physical condition, presence or absence of injuries or illnesses, and whether in a special physiological period (such as pregnancy). The physical parameter information may include age, gender, height, weight, BMI (Body Mass Index), cardiopulmonary function, etc. The historical exercise records may include the user's past exercise types, frequencies, durations, intensities, and physical reactions after exercise, etc.

[0028] Optionally, analyze the collected user information to understand the user's exercise ability and needs. For example, by analyzing the user's physical parameter information and historical exercise records, the user's exercise foundation, endurance, strength, and flexibility can be evaluated. By considering the user's current health status, it can be determined which exercise actions or intensities may be uncomfortable or risky for the user.

[0029] Based on the exercise demonstration material queried in S101, select the material that matches the user's exercise type as the basis. According to the user's current health status, physical parameter information, and historical exercise records, adjust each demonstration action in the selected exercise demonstration material. The adjustment content may include the frequency of the action (such as the number of repetitions per minute), the amplitude of the action (such as the size, range, or angle of the action), etc.

[0030] The purpose of the adjustment is to make the demonstration actions more in line with the user's personal ability, reduce exercise risks, and improve exercise effects. Combine the adjusted demonstration actions into a new exercise demonstration material, that is, the personalized demonstration material corresponding to the user. The personalized demonstration material can be presented in the form of video, animation, or graphics and text, etc., so that the user can learn and imitate more intuitively.

[0031] S103. Feedback the personalized demonstration material to the user.

[0032] Optionally, select a suitable feedback method according to the user's usage habits and preferences. Send the personalized demonstration material to the user through the selected feedback method.

[0033] In an exemplary embodiment, the method further includes: if no exercise demonstration material is queried in the demonstration material library according to the exercise type carried in the exercise management request, generate personalized demonstration material corresponding to the user according to the user's current health status, physical parameter information, and historical exercise records.

[0034] Optionally, if the query result shows that there are demonstration materials in the demonstration material library that match the exercise type, directly obtain and use these materials. If the query result shows that there are no demonstration materials in the demonstration material library that match the exercise type, proceed to the next step, that is, generate personalized demonstration materials.

[0035] Collect the user's current health status, body parameter information, and historical exercise records. Analyze this information to understand the user's exercise ability, needs, preferences, and potential limitations or risks. Based on the analysis of the user's information, combined with exercise knowledge and skills, generate personalized demonstration materials for the user. The personalized demonstration materials can include customized exercise movements, frequencies, amplitudes, durations, as well as supporting exercise suggestions, precautions, etc.

[0036] In this embodiment, generate personalized demonstration materials to ensure the accuracy and scientific nature of the content, and avoid misleading users or causing exercise injuries.

[0037] In an exemplary embodiment, as Figure 2 shown, according to the user's current health status, body parameter information, and historical exercise records, adjust the action frequency and action amplitude of each demonstration action included in the exercise demonstration materials to obtain personalized demonstration materials corresponding to the user, including: S201, according to the user's current health status, determine the health abnormal parts of the user and the abnormal degree corresponding to the health abnormal parts.

[0038] Optionally, collect the user's current health status information through methods such as health questionnaires filled out by the user, physical examination reports, medical device monitoring data, or user self-reports. Analyze the collected health status information to identify possible health abnormalities or potential risks of the user. The analysis process can involve the assessment of various health indicators (such as blood pressure, blood sugar, heart rate, joint flexibility, etc.), as well as the comparison with the user's normal health status. Based on the analysis results, determine the specific parts of the user with health abnormalities, such as the knee joint, waist, shoulder, or heart, etc. When determining the health abnormal parts, the user's medical history, family genetic history, and current symptoms or discomforts can also be considered.

[0039] Furthermore, evaluate the abnormal degree of the health abnormal parts to determine its severity or scope of influence. The abnormal degree can be represented by quantitative indicators (such as pain level, function limitation degree, inflammation index, etc.), or determined by the professional judgment of a doctor.

[0040] S202, select at least one target action from the various demonstration actions of the exercise demonstration materials that has an improvement effect on the health abnormal parts.

[0041] Among them, at least one target action that has an improvement effect on the health abnormal part refers to those movement actions that have been screened and evaluated and are considered to have a positive impact on the user's specific health abnormal part and help improve or relieve the abnormal condition of that part.

[0042] Optionally, when selecting at least one target action that has an improvement effect on the health abnormal part, the following aspects can be analyzed: (1) Characteristics of the health abnormal part: Consider the specific symptoms, degree of functional limitation, and possible pathological mechanisms of the health abnormal part; select those actions that can target these characteristics and promote blood circulation, enhance muscle strength, improve joint flexibility, or reduce inflammation through movement stimulation.

[0043] (2) Principles of exercise physiology: According to exercise physiology knowledge, select those actions that can produce beneficial physiological effects on the health abnormal part; for example, for knee pain, actions that can enhance the strength of the muscles around the knee joint and improve the stability of the knee joint may be selected.

[0044] (3) User safety and acceptability: Ensure that the selected target actions are safe for the user and will not aggravate the symptoms of the health abnormal part or cause new injuries; consider the user's physical condition, exercise experience, and preferences, and select those actions that the user can accept and is willing to perform.

[0045] (4) Medical advice and professional guidance: When selecting target actions, medical professionals may be consulted or relevant medical literature and research may be referred to; ensure that the selected target actions are coordinated with the user's medical advice or treatment plan and will not cause conflicts or negative impacts.

[0046] Exemplarily, assume that the exercise demonstration materials contain various exercise actions such as squats, lunges, leg presses, seated leg curls, and leg raises. These actions all involve the movement of the knee joint, but their movement patterns and load characteristics are different. Based on the characteristics of knee pain and the movement patterns and load characteristics of each action, the target action can be selected as the seated leg curl: Sit on a leg curl machine, hook your feet on the footrest, and hold the handle with both hands to keep your body stable. Then slowly straighten your legs, feel the contraction of the front thigh muscles (quadriceps), and then slowly return to the starting position. This action can strengthen the strength of the quadriceps, which is an important muscle for stabilizing the knee joint. Enhancing the strength of the quadriceps helps reduce the burden on the knee joint and relieve pain.

[0047] S203. According to the abnormal degree corresponding to the health abnormal part and the user's historical exercise record, adjust the action frequency of each target action to obtain the target frequency value corresponding to each target action.

[0048] It is understandable that the action frequency refers to the number of times an action is repeated per unit time. It is an index to measure the action speed and plays an important role in sports. In cyclic sports events, such as running, swimming, etc., the action frequency (such as stride frequency, stroke frequency) is an important factor determining the moving speed. In fitness and rehabilitation training, the action frequency is also a key factor affecting the training effect and safety.

[0049] Optionally, as Figure 3 shown, according to the degree of abnormality corresponding to the health abnormal part and the user's historical exercise records, adjust the action frequency of each target action to obtain the target frequency value corresponding to each target action, including: S301, according to the degree of abnormality corresponding to the health abnormal part, determine the upper limit value of the action frequency of each target action.

[0050] Among them, the upper limit value of the action frequency of each target action is to ensure that the exercise will not aggravate the symptoms of the health abnormal part or cause new injuries. For mild abnormalities, the upper limit value may be higher; for severe abnormalities, the upper limit value may be lower.

[0051] S302, according to the abnormal feedback situation in the historical exercise records, adjust the upper limit value of the action frequency of each target action to obtain the reference frequency value of each target action.

[0052] Optionally, according to the user's historical exercise records, especially whether there has been abnormal feedback (such as increased pain, discomfort, etc.) during previous exercises. Analyze the relationship between these abnormal feedbacks and specific actions and frequencies.

[0053] Furthermore, according to the abnormal feedback situation in the historical exercise records, adjust the initially set upper limit value of the frequency. If a certain action caused discomfort or aggravated symptoms before, then the upper limit value of the frequency of this action may be reduced. On the contrary, if a certain action has been proven to be safe and effective before, then the upper limit value of the frequency of this action may be maintained or slightly increased. The adjusted upper limit value of the frequency becomes the reference frequency value of each target action. This reference frequency value is set to ensure exercise safety on the basis of considering the user's historical exercise experience.

[0054] S303, according to the environmental parameters of the user's current environment and the user's current sports equipment, adjust the reference frequency value of each target action to obtain the target frequency value corresponding to each target action.

[0055] Among them, the environmental parameters include environmental parameters such as the temperature, humidity, altitude, etc. of the user's current environment. These environmental parameters may affect the user's exercise performance and recovery ability.

[0056] Among them are the user's current sports equipment, such as shoes, clothing, auxiliary equipment, etc. The quality and suitability of the sports equipment may affect the user's sports comfort and safety.

[0057] Optionally, based on the environmental parameters and the evaluation results of the sports equipment, make a final adjustment to the reference frequency value. For example, in a high-temperature or high-altitude environment, the movement frequency may be reduced to relieve the user's physical burden. If the sports equipment is not suitable or comfortable enough, the movement frequency may also be reduced to avoid discomfort or injury.

[0058] It can be understood that the adjusted reference frequency value becomes the target frequency value corresponding to each target action. This target frequency value is set to ensure sports safety and effectiveness based on a comprehensive consideration of the user's health status, historical sports experience, environmental parameters, and sports equipment.

[0059] Optionally, based on the environmental parameters of the user's environment and the user's current sports equipment, adjust the reference frequency value of each target action to obtain the target frequency value corresponding to each target action, including: adjusting the reference frequency value of each target action according to the degree of influence of the environmental parameters of the user's environment on the user's health status and the degree of influence of the user's current sports equipment on each target action.

[0060] Optionally, evaluate the possible impact of environmental parameters on the user's health status. For example, a high-temperature environment may cause the user's body temperature to rise and sweating to increase, thus affecting sports endurance and performance; a high-altitude environment may cause hypoxia, affecting the user's cardiopulmonary function and sports ability. Adjust the reference frequency value of each target action according to the degree of influence of the environmental parameters on the user's health status. For example, in a high-temperature or high-altitude environment, the movement frequency may be reduced to relieve the user's physical burden and avoid adverse reactions such as overheating or hypoxia.

[0061] Optionally, analyze the possible impact of sports equipment on each target action. For example, inappropriate shoes may affect the stability and support of the feet, thus increasing the risk of injury; overly tight clothing may limit the user's range of motion and affect the execution of actions. When considering the impact of sports equipment, the reference frequency value of each target action needs to be adjusted accordingly. If the equipment is not suitable or there are safety hazards, the movement frequency may be reduced, or alternative actions may be selected to ensure the safety and effectiveness of the movement.

[0062] Furthermore, comprehensively evaluate the degree of influence of environmental parameters on the user's health status and the degree of influence of the current sports equipment on each target action. Based on the comprehensive evaluation results, make a frequency adjustment decision. This may include reducing or increasing the frequency of certain actions, or adjusting the execution method of the actions (such as reducing the intensity, increasing the rest time, etc.).

[0063] Exemplarily, assume that the user plans to conduct an outdoor running training, and the target actions include jogging, accelerating running, and sprinting.

[0064] (1) Analyze the degree of influence of environmental parameters on the user's health status Environmental parameters: Temperature: 35°C, Humidity: 80%; Influence assessment: High temperature and high humidity environment will cause the user's body temperature to rise rapidly, sweating increases, and it is easy to cause dehydration and electrolyte imbalance. Conducting high-intensity exercises such as sprinting in such an environment may increase the user's cardiovascular burden and cause health problems such as heatstroke. Frequency adjustment strategy: Reduce the reference frequency value of sprinting, and reduce the duration of high-intensity exercise. Increase the rest time in the reference frequency values of jogging and accelerating running to ensure that the user has enough recovery time.

[0065] (2) Evaluate the degree of influence of the current sports equipment on each target action Sports equipment: The running shoes have not been fully broken in. Equipment influence analysis: When performing high-intensity actions such as accelerating running and sprinting, inappropriate shoes may increase the risk of injury. Frequency adjustment strategy: Reduce the reference frequency values of accelerating running and sprinting, and reduce the dependence on and wear of new shoes.

[0066] (3) Comprehensively adjust to obtain the target frequency value Considering the influence of high temperature and high humidity environment on the user's health status and the influence of new shoes on running actions, it is necessary to adjust the user's original running plan. Adjust the reference frequency value of sprinting from once every 10 minutes in the original plan to once every 20 minutes. Shorten the duration of each acceleration run from 2 minutes to 1 minute in the reference frequency value and add 1 minute of rest time. The jogging period remains unchanged, but add 2 minutes of walking rest after every 15 minutes of running to adapt to the new shoes and prevent overheating.

[0067] Finally, the adjusted reference frequency value becomes the target frequency value corresponding to each target action: Jogging: Keep running continuously, and add 2 minutes of walking rest after every 15 minutes. Accelerating running: Once every 10 minutes, each time for 1 minute, and then rest for 1 minute. Sprinting: Once every 20 minutes.

[0068] S204. According to the differences between the body parameter information of the demonstrator corresponding to the demonstration action and the user's body parameter information, adjust the action amplitude of each target action to obtain the target amplitude value corresponding to each target action.

[0069] Among them, the action amplitude is usually used to describe the maximum distance that a joint can move in an action. For example, in the push-up action, the action amplitude can be calculated from the position where the arm is fully extended to the position where the chest touches the ground; in the squat action, the action amplitude can be measured from the standing position to the position where the thigh is parallel to the ground.

[0070] When determining the target motion amplitude, the following factors need to be considered: (1) Physical parameter information of the demonstrator: The physical parameter information of the demonstrator, such as height, weight, muscle mass, flexibility, etc., will affect their motion amplitude. For example, a person with a tall stature may be able to achieve a greater motion amplitude when doing squats.

[0071] (2) Physical parameter information of the user: Compared with the demonstrator, there may be differences in the physical parameter information of the user. These differences may include height, weight, muscle mass, flexibility, joint flexibility, and any possible health limitations or injuries.

[0072] (3) Principles for adjusting motion amplitude: Personalized principle: According to the physical parameter information of the user, adjust the motion amplitude to ensure the safety and effectiveness of the exercise. For example, for a user with poor flexibility, the motion amplitude of certain actions may need to be reduced to avoid injury. Gradual increase principle: For beginners or users with poor physical conditions, the motion amplitude can start from a smaller value and gradually increase as the body's adaptability improves. Goal-oriented principle: According to the user's exercise goals (such as muscle gain, fat loss, improving cardiopulmonary function, etc.), adjust the motion amplitude to maximize the exercise effect. For example, for the goal of muscle gain, a larger motion amplitude may be required to stimulate more muscle fibers.

[0073] Finally, by comparing the physical parameter information of the demonstrator with that of the user, identify the differences between the two. Based on these differences, adjust the motion amplitude of each target action. For example, if the user has poor flexibility, the depth of squatting can be reduced in the squat action; if the user has weak muscle strength, the degree of arm bending can be reduced in the push-up action. The adjusted motion amplitude is the target amplitude value corresponding to each target action.

[0074] Exemplarily, assume that the demonstrator has a height of 180 cm, a weight of 80 kg, and good flexibility and can complete the squat action at the standard depth. While the user has a height of 160 cm, a weight of 60 kg, and poor flexibility and feels discomfort when squatting. In this case, the motion amplitude of the squat action can be adjusted to let the user squat to a position where the thigh is about 45 degrees to the ground, rather than the standard depth of the demonstrator. This adjusted motion amplitude is the target amplitude value of the squat action for this user.

[0075] S205. Determine the number of repetitions corresponding to each target action after adjusting the motion amplitude and motion frequency according to the expected exercise effect carried in the exercise management request and the improvement effect of each target action on the health abnormal part after adjusting the motion amplitude and motion frequency.

[0076] Among them, when determining the improvement effect of each target action on the health abnormal part after adjusting the action amplitude and action frequency, it can be analyzed based on the principles of exercise physiology: different action amplitudes and frequencies will produce different physiological stimuli on the body. For example, a larger action amplitude can usually stimulate more muscle fibers to participate in the movement, while a higher action frequency helps to improve cardiopulmonary function and metabolic level. Or refer to the practical experience of rehabilitation medicine, and evaluate the treatment effects of different exercise programs on specific health problems through clinical trials or observational studies.

[0077] Optionally, when determining the number of repetitions corresponding to each target action after adjusting the action amplitude and action frequency, first clarify the exercise goal of the target user, such as enhancing muscle strength, improving cardiopulmonary function, reducing fat and shaping the body, or improving the condition of a specific health abnormal part. Different exercise goals will directly affect the determination of the number of action repetitions. Generally, when the number of repetitions is large, it helps to enhance muscle endurance and improve cardiopulmonary function; while when the number of repetitions is small but the load is heavy, it is more helpful to enhance muscle strength. Therefore, it is necessary to select an appropriate number of repetitions according to the expected exercise effect.

[0078] Exemplarily, assume that the user's exercise goal is to enhance the leg muscle strength and the appropriate action amplitude and frequency have been determined. At this time, squats can be selected as the target action, and the number of repetitions can be determined according to the following principles: Initial stage: Repeat 4 - 6 times per group, with a moderate load to ensure the standardization and stability of the action. Adaptation stage: As the body's adaptability improves, the load and the number of repetitions can be gradually increased, for example, repeat 6 - 8 times per group, and the load gradually increases. Improvement stage: When the body has adapted to a higher load and the number of repetitions, the difficulty can be further increased, for example, repeat 8 - 10 times per group, and the load reaches the maximum tolerance of the user.

[0079] S206, generate personalized demonstration materials corresponding to the user according to each target action after adjusting the action amplitude and action frequency, and the number of repetitions corresponding to each target action after adjusting the action amplitude and action frequency.

[0080] Optionally, sort out all the adjusted target actions, including the action name, action amplitude, action frequency, and the corresponding number of repetitions.

[0081] In this embodiment, according to the user's preference and actual situation, select a suitable demonstration form. Video demonstration can intuitively show the whole process of the action, including the starting posture, action execution, ending posture, etc. Produce demonstration materials, and in the demonstration materials, some auxiliary information can be added, such as the key points, difficult points, error - prone points of the action, etc.

[0082] In an exemplary embodiment, such as Figure 4As shown, personalized demonstration materials corresponding to the user are generated based on each target action adjusted according to the action amplitude and action frequency, and the number of repetitions corresponding to each target action after the adjustment of the action amplitude and action frequency, including: S401. Determine the historical exercise intensity and historical exercise duration of the historical exercise record, and the historical abnormal moments corresponding to the abnormal feedback situation in the historical exercise duration in the historical exercise record.

[0083] Among them, the historical exercise intensity refers to the intensity level of the user's exercise in the past, which can usually be measured by indicators such as heart rate, power output, speed, and load. By analyzing the data collected by the user's worn exercise monitoring device (such as a heart rate monitor, smart watch, fitness tracker, etc.) or the exercise log manually input by the user, the historical exercise intensity is determined.

[0084] Among them, the historical exercise duration refers to the duration of each exercise of the user in the past, usually in minutes or hours. By analyzing the data of the exercise monitoring device or the exercise log manually input by the user, the start and end times of each exercise are determined, so as to calculate the historical exercise duration.

[0085] Among them, the abnormal feedback situation refers to any adverse reactions or feedback such as discomfort, pain, excessive fatigue, incorrect action execution, and abnormal heart rate that occur during the user's exercise. By analyzing the abnormal feedback situation in the historical exercise record through the feedback manually input by the user, the abnormal data of the exercise monitoring device (such as a sudden increase or decrease in heart rate), or by automatically identifying abnormal changes in the user's exercise pattern through a machine learning algorithm.

[0086] Among them, the historical abnormal moment refers to the specific time point when the user has abnormal feedback or adverse reactions during the historical exercise duration. Combining the above analysis of the historical exercise record and the abnormal feedback situation, the specific moment when the abnormality occurs in each exercise can be determined, providing an important reference for subsequent exercise plan adjustment or safety monitoring.

[0087] S402. Determine the current exercise duration of this exercise according to the target amplitude value, target frequency value, and number of repetitions of each target action.

[0088] Among them, the target amplitude value refers to the maximum range of motion or displacement when the action is executed, which affects the completion quality and effect of the action. The target frequency value refers to the number of times the action is completed per unit time, which determines the execution speed and rhythm of the action. The number of repetitions refers to the number of times the action needs to be repeated, which determines the intensity and total amount of training.

[0089] Optionally, for each target action, the time required to complete the action can be estimated based on its amplitude value, frequency value, and number of repetitions. For example, if the target frequency of an action is 20 times per minute and the number of repetitions is 10 times, then it takes about 30 seconds to complete this action (10 times / 20 times per minute = 0.5 minutes, i.e., 30 seconds). By accumulating the execution times of all target actions, the total duration of this exercise is obtained.

[0090] Furthermore, considering the transition time between actions, rest time, etc., appropriate adjustments may be needed to the total duration.

[0091] Exemplarily, Action A: The target amplitude value is moderate, the target frequency value is 15 times per minute, and the number of repetitions is 12 times. Action B: The target amplitude value is large, the target frequency value is 10 times per minute, and the number of repetitions is 8 times. Action C: The target amplitude value is small, the target frequency value is 20 times per minute, and the number of repetitions is 10 times.

[0092] Action A: 12 times / 15 times per minute = 0.8 minutes = 48 seconds Action B: 8 times / 10 times per minute = 0.8 minutes = 48 seconds Action C: 10 times / 20 times per minute = 0.5 minutes = 30 seconds Assuming the transition time and rest time between actions is 10 seconds, the total duration of this exercise is: 48 seconds + 48 seconds + 30 seconds + 10 seconds (two transition times) = 136 seconds ≈ 2 minutes and 16 seconds S403. Determine the current exercise intensity corresponding to this exercise based on the adjusted target actions according to the action amplitude and action frequency, and the number of repetitions corresponding to the adjusted target actions according to the action amplitude and action frequency.

[0093] It can be understood that a larger action amplitude usually requires higher muscle strength and flexibility, so the exercise intensity can be increased. A higher action frequency means a faster action execution speed, which usually leads to an increase in heart rate, thus increasing the exercise intensity. More repetitions mean a longer exercise time and greater energy consumption, which also increases the exercise intensity.

[0094] Combined with the adjusted action amplitude, action frequency, and number of repetitions, the current exercise intensity of this exercise can be comprehensively evaluated. The exercise intensity can be measured by various indicators, such as heart rate, power output, metabolic rate, or subjective feeling (such as RPE, i.e., Rate of Perceived Exertion, subjective fatigue sensation scale).

[0095] Exemplarily, assume that a user is performing an exercise plan that includes multiple target actions. Each action has been adjusted in terms of amplitude and frequency, and the corresponding number of repetitions has been set.

[0096] Action 1: Moderate amplitude, fast frequency, and a relatively large number of repetitions.

[0097] Action 2: Large amplitude, moderate frequency, and a relatively small number of repetitions.

[0098] Action 3: Small amplitude, slow frequency, and a moderate number of repetitions.

[0099] After comprehensively evaluating these action parameters, the current exercise intensity of this exercise can be determined. For example, by monitoring the user's heart rate changes, observing their action execution, and subjective fatigue feelings, it can be judged whether the exercise intensity is within the appropriate range.

[0100] S404. Determine the predicted abnormal moment in this exercise based on the intensity difference between the historical exercise intensity and the current exercise intensity, the duration difference between the historical exercise duration and the current exercise duration, and the abnormal moment corresponding to the abnormal feedback situation in the historical exercise record within the historical exercise duration.

[0101] Optionally, the historical exercise intensity reflects the user's past exercise load level. The current exercise intensity is determined according to step S403 and represents the expected load of this exercise plan. Intensity difference: By comparing the two, it can be understood whether this exercise plan has increased or decreased relative to the user's historical exercise level.

[0102] Optionally, the historical exercise duration refers to the duration of each of the user's past exercises. The current exercise duration is determined according to step S402 and is the expected duration of this exercise plan. Comparing the two can evaluate the impact that the change in time of this exercise plan can have on the user.

[0103] Optionally, the abnormal feedback situation refers to any adverse reactions such as discomfort, pain, and excessive fatigue that the user has experienced during past exercises. The abnormal moment refers to the specific occurrence time of these adverse reactions within the historical exercise duration.

[0104] Furthermore, by combining the intensity difference, duration difference, and historical abnormal moment, the risks that the user may face in the current exercise plan can be analyzed. If the current exercise intensity or duration has increased significantly, and the user has had abnormal feedback at a similar intensity or duration in the past, then it can be predicted that the user may have an abnormality again at the corresponding moment of this exercise.

[0105] Exemplarily, assume that a user's historical exercise records show that when the exercise intensity reaches a certain high level, the user often experiences fatigue and a rapid heart rate in the second half of the exercise. Now, the user plans to perform an exercise with a higher intensity and longer duration.

[0106] Intensity difference: The intensity of this exercise is higher than the historical average level.

[0107] Duration difference: The duration of this exercise is longer than the historical average duration.

[0108] Historical abnormal moment: In the user's past high-intensity and long-duration exercises, abnormalities usually occurred 40 - 50 minutes after the start of the exercise.

[0109] Combining this information, it can be predicted that during this exercise, the user may experience fatigue and a rapid heart rate 40 - 50 minutes (or slightly later, because a higher intensity can cause fatigue to occur earlier) after the start of the exercise.

[0110] S405, Generate personalized demonstration materials for the user according to the predicted abnormal moment, each target action adjusted according to the action amplitude and action frequency, and the number of repetitions corresponding to each target action adjusted according to the action amplitude and action frequency.

[0111] Optionally, based on historical data and the current exercise plan, predict the specific time points when the user may experience discomfort or adverse reactions during the exercise. When generating the demonstration materials, these moments need to be considered specifically, and the risk may be reduced by adjusting the action difficulty, increasing the rest time, or providing special guidance.

[0112] It can be understood that parameters such as the action amplitude and frequency have been personalized adjusted according to factors such as the user's physical fitness, flexibility, and exercise experience. In the demonstration materials, it is necessary to clearly indicate the number of repetitions of each action, and the number of repetitions or rest arrangements may be adjusted according to the predicted abnormal moment.

[0113] Combining the predicted abnormal moment, the adjusted action parameters and the number of repetitions, a series of personalized demonstration materials can be generated. These materials may include various forms such as videos, animations, graphic descriptions, etc. to meet the needs and preferences of different users. The demonstration materials should clearly show the start, execution, and end processes of each action, as well as the transitions and rest times between actions.

[0114] When generating the demonstration materials, it is necessary to particularly emphasize the safety and correctness of the actions. For the predicted abnormal moment, special tips or suggestions can be provided, such as slowing down the action speed, increasing the rest time, etc.

[0115] Optionally, such as Figure 5As shown, personalized demonstration materials corresponding to the user are generated based on each target action adjusted according to the predicted abnormal moment, movement amplitude, and movement frequency, as well as the number of repetitions corresponding to each target action after adjusting the movement amplitude and movement frequency, including: S501, generate initial demonstration materials corresponding to the user based on each target action after adjusting the movement amplitude and movement frequency, as well as the number of repetitions corresponding to each target action after adjusting the movement amplitude and movement frequency.

[0116] Optionally, determine the amplitude of each target action according to the user's physical fitness, flexibility, and exercise experience. The amplitude should be moderate, neither too large to cause injury to the user nor too small to affect the exercise effect. Determine the execution speed or rhythm of each action. The frequency should match the user's cardiopulmonary function, avoiding being too fast to cause fatigue or too slow to affect exercise efficiency. Provide a detailed description of each target action, including the starting posture, execution process, ending posture, and key points to note. Set the number of repetitions of each target action according to the user's exercise goals and physical fitness level.

[0117] Furthermore, use animation software to create 3D or 2D models to simulate the execution process of the action and generate initial demonstration materials corresponding to the user.

[0118] S502, take the target action corresponding to the predicted abnormal moment in the initial demonstration materials as the current action.

[0119] Optionally, review the previously predicted abnormal moments through data analysis or algorithms. These moments are obtained through comprehensive consideration of various factors such as the user's physical fitness, historical exercise data, and environmental factors, and may indicate the time points when the user may experience adverse reactions such as fatigue, rapid heart rate, and difficulty breathing during exercise.

[0120] In the initial demonstration materials, find the target action corresponding to the predicted abnormal moment. This may require carefully checking the timeline of the demonstration materials or using the time code function of the editing software for precise positioning. Determine the located target action as the current action. The current action is the object that needs to be particularly concerned about and processed next because it may be related to the user's abnormal reaction. By particularly focusing on and processing the current action, it can help the user better cope with the challenges during exercise and improve the safety and effectiveness of exercise.

[0121] S503, generate a relief action corresponding to the current action according to the action characteristics of the current action.

[0122] Optionally, design one or more relief actions for the discomfort or adverse reactions that the current action may cause to help the user reduce or avoid these discomforts during exercise.

[0123] Observe the execution manner of the current action, including the amplitude, frequency, involved muscle groups, movement trajectory, etc. Analyze the possible impacts of these action characteristics on the user, such as which actions may cause muscle tension, rapid heart rate, or difficulty breathing, etc.

[0124] Based on the analysis of the current action characteristics, design one or more relief actions. The relief actions should aim to relax the tense muscle groups in the current action, reduce the heart rate, improve the breathing condition, or provide other forms of physical adjustment. Provide a detailed description for each relief action, including the starting posture, execution process, ending posture, and key points to note. Consider the duration and repetition times of the relief actions to ensure their effectiveness.

[0125] S504, at the predicted abnormal moment of the initial demonstration material corresponding to the user, insert the relief action corresponding to the current action to obtain the personalized demonstration material corresponding to the user.

[0126] Optionally, review the previously predicted abnormal moments through data analysis or algorithms. These moments are the key points for inserting relief actions. Find the corresponding positions in the initial demonstration material for these abnormal moments as the insertion points for the relief actions.

[0127] Insert the designed relief actions into the initial demonstration material according to the determined insertion points. Ensure the smooth connection between the relief actions and the initial demonstration material to avoid abrupt or broken situations. After inserting the relief actions, it may be necessary to make overall adjustments to the initial demonstration material to ensure the coherence and integrity of the demonstration. The adjustments may include timeline adjustment, rearrangement of action sequences, and adaptation of background music or sound effects, etc.

[0128] In this embodiment, by inserting relief actions at the predicted abnormal moments, it can help users better cope with the challenges during exercise, reduce the exercise risk, and improve the exercise experience.

[0129] In an exemplary embodiment, as Figure 6 shown, select at least one target action from the demonstration actions of the exercise demonstration material that has an improvement effect on the health abnormal part, including: S601, when the number of the user's health abnormal parts is multiple, determine the restrictive relationship between the health abnormal parts.

[0130] Among them, in the field of health, the restrictive relationship can be understood as the mutual influence and restriction existing between different health abnormal parts due to physiological structure, function, or pathological process. This relationship may be manifested as the abnormal symptoms or dysfunctions of one part aggravating or affecting the symptoms or functions of another part.

[0131] Optionally, the following steps can be adopted to determine the restrictive relationship between the health abnormal parts: (1) Collect health information: Thoroughly understand the patient's medical history, including past illnesses, surgical history, trauma history, etc. Conduct a comprehensive physical examination to assess the patient's physical condition, including muscle strength, muscle tone, joint range of motion, etc.

[0132] (2) Analyze symptom manifestations: Pay attention to whether there are correlations between the symptoms of different health abnormal parts of the patient. For example, whether the pain in a certain part will cause pain or dysfunction in other parts. Analyze the severity of the symptoms of each health abnormal part and their impact on the patient's daily life and activities.

[0133] (3) Identify physiological structure relationships: Master the anatomical structures and physiological functions of various parts of the human body, which helps to identify the restrictive relationships between different parts. Understand the pathological processes of various diseases, including inflammation, injury, degenerative diseases, etc., and how they affect adjacent or related parts.

[0134] S602. According to the restrictive relationships between the health abnormal parts and the abnormal degrees of the health abnormal parts, select at least one target action from the demonstration actions of the motion demonstration materials that has an improvement effect on the health abnormal parts.

[0135] Optionally, according to the restrictive relationships and abnormal degrees, select the demonstration actions that have a significant effect on improving the health abnormal parts of the user as the target actions. When selecting, give priority to those actions that have an improvement effect on multiple health abnormal parts or have a significant improvement effect on key parts (such as core muscle groups, affected joints, etc.). At the same time, avoid selecting those actions that may exacerbate the restrictive relationships or abnormal symptoms.

[0136] Optionally, as Figure 7 shown, according to the restrictive relationships between the health abnormal parts and the abnormal degrees of the health abnormal parts, select at least one target action from the demonstration actions of the motion demonstration materials that has an improvement effect on the health abnormal parts, including: S701. According to the restrictive relationships between the health abnormal parts, select at least one associated action from the demonstration actions of the motion demonstration materials that has an improvement effect on each health abnormal part.

[0137] Optionally, analyze the restrictive relationships between the health abnormal parts to understand how the abnormality of one part affects other parts. In the motion demonstration materials, screen out the demonstration actions that have an improvement effect on at least one health abnormal part. These actions may directly target a certain abnormal part or indirectly relieve the abnormalities of other parts by improving the functions of related parts. Take the screened-out actions as the associated actions, which are candidate actions with potential improvement effects on each health abnormal part.

[0138] S702. Adjust the action postures of the associated actions according to the abnormality degrees of the various health abnormal parts to obtain the target actions corresponding to the various associated actions.

[0139] Optionally, conduct a detailed assessment of the abnormality degrees of the various health abnormal parts to understand the severity of each part and the key points that need to be improved. According to the abnormality degrees, appropriately adjust the action postures of the associated actions. For example, for a part with severe pain, it may be necessary to reduce the amplitude of the action or change the action direction; for a part with dysfunction, it may be necessary to increase the activation degree of specific muscle groups. The associated actions after adjustment are the target actions, which are more in line with the user's health condition and needs.

[0140] Exemplarily, assume that a user has the following health abnormal parts: Low back pain (possibly lumbar muscle strain or lumbar disc herniation) Knee pain (possibly knee arthritis or knee joint injury) Meanwhile, there is a restrictive relationship between these two parts: low back pain may limit the flexibility and range of motion of the knee joint, and knee pain may also increase the burden on the low back.

[0141] Low back pain may limit the user from performing large-amplitude bending or twisting actions.

[0142] Knee pain may limit the user from performing squats or rapid flexion and extension of the knee joint.

[0143] Screen demonstration actions: Screen out those demonstration actions from the movement demonstration materials that have an improvement effect on the low back and knee. For example, find some gentle low back stretching actions, knee flexion and extension exercises, and actions to strengthen the muscles around the low back and knee.

[0144] Determine the associated actions: Select the following associated actions: Gentle forward flexion of the low back: Stand, place both hands on the low back, and slowly bend forward, feeling the stretch of the low back, but not exceeding the pain range. Knee flexion and extension exercise: Sit on a chair, place both feet flat on the ground, and slowly flex and extend the knee joint, keeping the movement stable and avoiding sudden force.

[0145] Evaluate the abnormality degree: The low back pain is relatively severe, and excessive bending and twisting need to be avoided. The knee pain is moderate, and moderate flexion and extension exercises can be performed, but squats and rapid actions are avoided.

[0146] Adjust the action postures: For gentle forward flexion of the low back: Reduce the bending amplitude, keep the back straight, and only perform a slight stretch of the low back. For knee flexion and extension exercise: Reduce the flexion and extension amplitude, keep the movement slow and stable, and avoid sudden force or excessive flexion and extension.

[0147] The adjusted target actions are as follows: Slight lumbar extension: Stand with your hands on your waist, bend forward slightly while keeping your back straight, and feel the slight extension of the lumbar region. Slow knee flexion and extension: Sit on a chair with your feet flat on the ground, and slowly and smoothly flex and extend your knees, avoiding sudden or excessive movement.

[0148] In this embodiment, at least one target action that has an improvement effect on the health abnormal part is selected and adjusted from the motion demonstration materials, fully considering the user's health status and individual differences.

[0149] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed 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 executed alternately or in turn with at least a part of other steps or steps in other steps.

[0150] Based on the same inventive concept, the embodiment of the present application also provides a device for the motion management method of the personal health intelligent AI based on the Internet of Things involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the device for the motion management method of the personal health intelligent AI based on the Internet of Things provided below can refer to the limitations on the motion management method of the personal health intelligent AI based on the Internet of Things in the above text, and will not be repeated here.

[0151] In an exemplary embodiment, as Figure 8 shown, a device for the motion management method of the personal health intelligent AI based on the Internet of Things is provided, configured in an Internet of Things device, including: An acquisition module 11, configured to query motion demonstration materials from a demonstration material library according to the motion type carried in the motion management request when a motion management request initiated by a user is acquired; the motion demonstration materials include materials in video form; A material generation module 12, configured to adjust the action frequency and action amplitude of each demonstration action included in the motion demonstration materials according to the user's current health status, body parameter information, and historical motion records, to obtain personalized demonstration materials corresponding to the user; A feedback module 13, configured to feedback the personalized demonstration materials to the user.

[0152] Each module in the above-mentioned motion management method and device of the personal health intelligent AI based on the Internet of Things can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of the Internet of Things device in hardware form or be independent of it, or can be stored in the memory of the Internet of Things device in software form, so as to facilitate the processor to call and execute the operations corresponding to each of the above modules.

[0153] In an exemplary embodiment, an Internet of Things device is provided. The Internet of Things device can be a terminal, and its internal structure diagram can be as Figure 9 shown. The Internet of Things device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the Internet of Things device is used to provide computing and control capabilities. The memory of the Internet of Things device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the Internet of Things device is used to exchange information between the processor and external devices. The communication interface of the Internet of Things device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a motion management method of a personal health intelligent AI based on the Internet of Things. The display unit of the Internet of Things device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the Internet of Things device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the shell of the Internet of Things device, or an external keyboard, touchpad, or mouse, etc.

[0154] In an exemplary embodiment, an Internet of Things device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented: When a motion management request initiated by the user is obtained, according to the motion type carried in the motion management request, query the motion demonstration material corresponding to the motion type from the demonstration material library; the motion demonstration material includes materials in video form; Adjust the action frequency and amplitude of each demonstration action included in the exercise demonstration material according to the user's current health status, body parameter information, and historical exercise records to obtain personalized demonstration material corresponding to the user; Feed back the personalized demonstration material to the user.

[0155] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: In the case of obtaining a motion management request initiated by the user, query the motion demonstration material corresponding to the motion type from the demonstration material library according to the motion type carried in the motion management request; the motion demonstration material includes materials in video form; Adjust the action frequency and amplitude of each demonstration action included in the exercise demonstration material according to the user's current health status, body parameter information, and historical exercise records to obtain personalized demonstration material corresponding to the user; Feed back the personalized demonstration material to the user.

[0156] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the following steps are implemented: In the case of obtaining a motion management request initiated by the user, query the motion demonstration material corresponding to the motion type from the demonstration material library according to the motion type carried in the motion management request; the motion demonstration material includes materials in video form; Adjust the action frequency and amplitude of each demonstration action included in the exercise demonstration material according to the user's current health status, body parameter information, and historical exercise records to obtain personalized demonstration material corresponding to the user; Feed back the personalized demonstration material to the user.

[0157] 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 for analysis, stored data, displayed data, 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 relevant data need to comply with relevant regulations.

[0158] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory 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), magnetoresistive 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 be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0159] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope recorded in the present application.

[0160] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A motion management method for personal health intelligent AI based on the Internet of Things, characterized in that, Applied to Internet of Things devices, the method includes: When a motion management request initiated by a user is obtained, query the motion demonstration material corresponding to the motion type from a demonstration material library according to the motion type carried in the motion management request; the motion demonstration material includes materials in video form; Adjust the action frequency and action amplitude of each demonstration action included in the motion demonstration material according to the user's current health status, body parameter information, and historical motion record to obtain personalized demonstration material corresponding to the user; Feed back the personalized demonstration material to the user.

2. The method according to claim 1, wherein The adjusting the action frequency and action amplitude of each demonstration action included in the motion demonstration material according to the user's current health status, body parameter information, and historical motion record to obtain personalized demonstration material corresponding to the user includes: Determine the user's health abnormal part and the abnormal degree corresponding to the health abnormal part according to the user's current health status; Select at least one target action from each demonstration action of the motion demonstration material that has an improvement effect on the health abnormal part; Adjust the action frequency of each target action according to the abnormal degree corresponding to the health abnormal part and the user's historical motion record to obtain the target frequency value corresponding to each target action; Adjust the action amplitude of each target action according to the difference between the body parameter information of the demonstrator corresponding to the demonstration action and the user's body parameter information to obtain the target amplitude value corresponding to each target action; Determine the number of repetitions corresponding to each target action after adjusting the action amplitude and action frequency according to the expected motion effect carried in the motion management request and the improvement effect of each target action on the health abnormal part after adjusting the action amplitude and action frequency; Generate personalized demonstration material corresponding to the user according to each target action after adjusting the action amplitude and action frequency and the number of repetitions corresponding to each target action after adjusting the action amplitude and action frequency.

3. The method according to claim 2, characterized in that, The adjusting the action frequency of each target action according to the abnormal degree corresponding to the health abnormal part and the user's historical motion record to obtain the target frequency value corresponding to each target action includes: Determine the upper limit value of the action frequency of each target action according to the abnormal degree corresponding to the health abnormal part; Adjust the upper limit value of the action frequency of each target action according to the abnormal feedback situation in the historical motion record to obtain the reference frequency value of each target action; Adjust the reference frequency value of each target action according to the environmental parameters of the user's location and the user's current sports equipment to obtain the target frequency value corresponding to each target action.

4. The method according to claim 3, characterized in that, The adjusting the reference frequency value of each target action according to the environmental parameters of the user's location and the user's current sports equipment to obtain the target frequency value corresponding to each target action includes: Adjust the reference frequency values of each target action according to the degree of influence of the environmental parameters of the user's environment on the user's health status and the degree of influence of the user's current sports equipment on each target action.

5. The method according to claim 3, characterized in that, Generating the personalized demonstration material corresponding to the user according to each target action adjusted according to the action amplitude and action frequency, and the number of repetitions corresponding to each target action adjusted according to the action amplitude and action frequency, includes: Determine the historical exercise intensity and historical exercise duration of the historical exercise record, and the historical abnormal moment corresponding to the abnormal feedback situation in the historical exercise duration in the historical exercise record; Determine the current exercise duration of this exercise according to the target amplitude value, target frequency value and number of repetitions of each target action; Determine the current exercise intensity corresponding to this exercise according to each target action adjusted according to the action amplitude and action frequency, and the number of repetitions corresponding to each target action adjusted according to the action amplitude and action frequency; Determine the predicted abnormal moment in this exercise according to the intensity difference between the historical exercise intensity and the current exercise intensity, the duration difference between the historical exercise duration and the current exercise duration of this exercise, and the abnormal moment corresponding to the abnormal feedback situation in the historical exercise duration in the historical exercise record; Generate the personalized demonstration material corresponding to the user according to the predicted abnormal moment, each target action adjusted according to the action amplitude and action frequency, and the number of repetitions corresponding to each target action adjusted according to the action amplitude and action frequency.

6. The method according to claim 5, wherein The generating the personalized demonstration material corresponding to the user according to the predicted abnormal moment, each target action adjusted according to the action amplitude and action frequency, and the number of repetitions corresponding to each target action adjusted according to the action amplitude and action frequency, includes: Generate the initial demonstration material corresponding to the user according to each target action adjusted according to the action amplitude and action frequency, and the number of repetitions corresponding to each target action adjusted according to the action amplitude and action frequency; Take the target action corresponding to the predicted abnormal moment in the initial demonstration material as the current action; Generate the relief action corresponding to the current action according to the action characteristics of the current action; Insert the relief action corresponding to the current action at the predicted abnormal moment of the initial demonstration material corresponding to the user to obtain the personalized demonstration material corresponding to the user.

7. The method according to claim 2, wherein The selecting at least one target action from the demonstration actions of the exercise demonstration material that has an improvement effect on the health abnormal part includes: When the number of the user's health abnormal parts is multiple, determine the restriction relationship between each health abnormal part; Select at least one target action from the demonstration actions of the exercise demonstration material that has an improvement effect on the health abnormal part according to the restriction relationship between each health abnormal part and the abnormal degree of each health abnormal part.

8. The method according to claim 7, wherein The selecting at least one target action from the demonstration actions of the exercise demonstration material that has an improvement effect on the health abnormal part according to the restriction relationship between each health abnormal part and the abnormal degree of each health abnormal part, includes: According to the restrictive relationship between each health abnormal part, select at least one associated action from each demonstration action of the movement demonstration material that has an improvement effect on each health abnormal part; According to the abnormal degree of each health abnormal part, adjust the action posture of the associated action to obtain the target action corresponding to each associated action.

9. The method according to claim 1, wherein The method further includes: If the movement demonstration material is not queried in the demonstration material library according to the movement type carried in the movement management request, generate personalized demonstration material corresponding to the user according to the user's current health status, body parameter information, and historical movement record.

10. A motion management device of a personal health intelligent AI based on the Internet of Things, characterized in that, Configured in an Internet of Things device, the device includes: An acquisition module, configured to query movement demonstration material from a demonstration material library according to the movement type carried in the movement management request when an movement management request initiated by a user is acquired; the movement demonstration material includes material in video form; A material generation module, configured to adjust the action frequency and action amplitude of each demonstration action included in the movement demonstration material according to the user's current health status, body parameter information, and historical movement record, to obtain personalized demonstration material corresponding to the user; A feedback module, configured to feedback the personalized demonstration material to the user.

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