Exercise prescription generation method and device, electronic equipment and storage medium

Abstract

The invention discloses an exercise prescription generation method and device, electronic equipment and a storage medium, the generation method and device are applied to the electronic equipment, and specifically, multiple personal data of a user are obtained; analyzing and processing the multiple items of personal data to obtain a user tag; and processing the user tag based on an exercise prescription generation algorithm to obtain an exercise prescription. According to the scheme, the dependence on doctors or therapists is reduced, and a proper exercise prescription can be obtained based on the specific condition of the illness state of the patient.

Description

Technical Field

[0001] The present application relates to the field of sports health technology, and more specifically, to a method, device, electronic device and storage medium for generating an exercise prescription. Background Art

[0002] At present, the research and development and application of intelligent exercise prescription systems based on big data and artificial intelligence have been relatively extensive and have achieved relatively fruitful results. These systems use cutting-edge technologies such as big data and machine learning to provide patients with accurate and personalized exercise rehabilitation guidance. By integrating professional medical information and rehabilitation practice experience, the program has established a rich library of exercise rehabilitation movements and set up a user information database to record the patient's basic information, disease assessment results and rehabilitation progress in detail. With the help of the mobile application platform, patients, doctors and therapists can easily communicate in real time. Doctors and therapists can formulate exercise prescriptions based on the patient's specific situation, and patients can directly view the details of the plan on the platform, learn and perform related movements for rehabilitation training.

[0003] However, the inventor of the present application has found through research on the prior art that the prior art solutions still have certain limitations, which are mainly reflected in the high dependence on doctors and therapists. Although the current solutions can assist in formulating exercise prescriptions, they still require doctors or therapists to personally formulate complete exercise prescriptions. Given that each patient's condition has its own specific circumstances, it is difficult to quickly and accurately formulate the most suitable exercise prescription for the patient when facing complex or special conditions. Summary of the invention

[0004] In view of this, the present application provides a method, device, electronic device and storage medium for generating an exercise prescription, which are used to output an exercise prescription suitable for the patient according to the patient's specific situation.

[0005] In order to achieve the above objectives, the proposed solution is as follows:

[0006] A method for generating an exercise prescription, applied to an electronic device, comprises the steps of:

[0007] Obtain various personal data of users;

[0008] Analyze and process the multiple pieces of personal data to obtain a user tag;

[0009] The user tag is processed based on an exercise prescription generation algorithm to obtain an exercise prescription.

[0010] Optionally, the step of obtaining multiple pieces of personal data of the user comprises the following steps:

[0011] In response to a user's input request, receiving the personal data input by the user;

[0012] The personal data collected by various sensors or wearable devices configured on the user's body is obtained.

[0013] Optionally, the plurality of personal data includes part or all of physical data, exercise habit data and health status data, wherein:

[0014] The physical data includes part or all of age, gender, height, weight, body fat percentage, heart rate and blood pressure;

[0015] The exercise habit data includes part or all of the exercise frequency, exercise duration, exercise intensity and exercise type;

[0016] The health status data includes part or all of disease history, allergy history and current health status.

[0017] Optionally, analyzing and processing the plurality of personal data to obtain a user tag comprises the steps of:

[0018] A machine learning algorithm is used to process part or all of the multiple pieces of personal data to obtain the user label.

[0019] Optionally, the step of analyzing and processing the plurality of pieces of personal data to obtain a user tag further includes the following steps:

[0020] Obtain multiple sets of personal parameters for multiple patients;

[0021] Preprocessing the multiple groups of personal parameters to obtain characteristic parameters;

[0022] Model training is performed based on the feature parameters to obtain the machine learning algorithm.

[0023] Optionally, the generating method further comprises the steps of:

[0024] Receive user feedback on the exercise prescription.

[0025] Optionally, the generating method further comprises the steps of:

[0026] The exercise prescription is optimized according to the feedback.

[0027] , a device for generating an exercise prescription, applied to an electronic device, the generating device comprising:

[0028] A data collection module, configured to obtain a plurality of personal data of the user;

[0029] A data analysis module is configured to analyze and process the multiple pieces of personal data to obtain a user tag;

[0030] The exercise prescription generation module is configured to process the user tag based on an exercise prescription generation algorithm to obtain an exercise prescription.

[0031] Optionally, also include:

[0032] The user feedback module is configured to receive user feedback on the exercise prescription.

[0033] Optionally, also include:

[0034] The prescription optimization module is configured to optimize the exercise prescription according to the feedback.

[0035] An electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0036] The memory is used to store computer programs or instructions;

[0037] The processor is used to execute the computer program or instructions so that the electronic device can implement the method for generating an exercise prescription as described above.

[0038] A computer-readable storage medium is applied to an electronic device, wherein the storage medium carries one or more computer programs, and the one or more computer programs can be executed by the electronic device, so that the electronic device implements the method for generating an exercise prescription as described above.

[0039] It can be seen from the above technical solutions that the present application discloses a method, device, electronic device and storage device for generating an exercise prescription. The generation method and device are applied to electronic devices, specifically to obtain multiple personal data of a user; analyze and process multiple personal data to obtain user tags; and process user tags based on an exercise prescription generation algorithm to obtain an exercise prescription. This solution reduces the dependence on doctors or therapists, and can derive a suitable exercise prescription based on the specific conditions of the patient's condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 A flowchart of a method for generating an exercise prescription according to an embodiment of the present application;

[0042] Figure 2A flowchart of another method for generating an exercise prescription according to an embodiment of the present application;

[0043] Figure 3 A flowchart of another method for generating an exercise prescription according to an embodiment of the present application;

[0044] Figure 4 A block diagram of a device for generating an exercise prescription according to an embodiment of the present application;

[0045] Figure 5 A schematic diagram of the operation content of the data acquisition module of an embodiment of the present application;

[0046] Figure 6 A block diagram of another device for generating exercise prescription according to an embodiment of the present application;

[0047] Figure 7 A block diagram of another device for generating an exercise prescription according to an embodiment of the present application;

[0048] Figure 8 A schematic diagram of the operation content of the prescription optimization module of the embodiment of the present application;

[0049] Fig. 9 A block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] This application has a comprehensive and refined logical framework, covering multiple types of exercise from cardiopulmonary endurance, flexibility, coordination to balance, speed, strength, etc., ensuring the comprehensiveness and scientificity of exercise prescriptions; at the same time, special movement classification and coding are carried out according to the exercise needs and physical characteristics of different age groups such as infants, toddlers, children, teenagers, young people, middle-aged and elderly people, so as to achieve the personalization and effectiveness of exercise prescriptions; in specific implementation, the system records in detail the main joints and muscle contraction patterns involved in each movement, as well as the classification of the difficulty of the movements, and also considers whether small equipment auxiliary tools are needed, so that the process of operators and users prescribing exercise prescriptions is more scientific, accurate and efficient.

[0052] This application implements the generation of exercise prescriptions based on a personalized exercise prescription recommendation algorithm.

[0053] First, the user's basic information (including but not limited to age, gender, height, weight, BMI, medical history, allergy history and previous exercise habits), health data (such as blood pressure, heart rate, blood sugar level, blood lipid level and body fat percentage) and behavioral data (such as the user's previous interaction data, completed exercises and feedback when using the mini program) are obtained through the user information collection module. At the same time, all actions in the system action library are labeled with relevant labels, which cover exercise types (such as aerobic exercise, strength training, flexibility training and balance training, etc.), exercise intensity (such as low, medium and high intensity), applicable groups (exercise prescriptions for different age groups, health conditions and diseases) and goals (such as increasing strength, improving flexibility, and enhancing cardiopulmonary function, etc.).

[0054] Through the tag matching and recommendation algorithm module, personalized exercise prescriptions can be pushed. Tag matching includes similarity calculation and user preference adjustment. By calculating the similarity between user tags and exercise prescription tags, the most suitable exercise prescription for the current user is determined; at the same time, collaborative filtering methods are used to analyze the behavioral data of similar users to recommend exercise prescriptions with good effects for other similar users. The system also includes real-time feedback and dynamic adjustment mechanisms. Users can provide feedback (such as feelings, effects and difficulty) after completing the exercise prescription. These feedbacks will be used to adjust user tags and recommendation strategies to ensure the accuracy and personalization of recommended content.

[0055] This application also uses large language model artificial intelligence technology to assist in decision-making in prescribing exercise prescriptions.

[0056] With its excellent parsing ability, the Large Language Model (LLM) deeply analyzes key information such as the user's health records, detailed questionnaires, and precise medical questionnaires. Through this data, the model can accurately understand the user's physical condition, daily exercise habits, and the goals they hope to achieve through exercise. This deep understanding not only provides an important reference for doctors and therapists when formulating exercise prescriptions, but also enables the machine to "read" the user's real needs, thereby providing them with personalized exercise prescription recommendations. These recommendations cover multiple key parameters such as exercise type, exercise intensity, and exercise frequency, and are designed to help doctors and therapists quickly and accurately develop exercise plans that meet the needs of patients. Whether it is for patients in the recovery period or ordinary people pursuing a healthy life, these recommendations can provide scientific guidance to ensure that the effect of exercise is maximized while reducing the risk of sports injuries.

[0057] At the same time, machine learning technology is also playing an indispensable role. It collects user data from multiple sources, including wearable devices, fitness apps, medical records, etc., which provide rich materials for model training and decision-making. By cleaning, integrating and extracting features from these data, machine learning algorithms can extract valuable information from them, laying a solid foundation for subsequent model training and auxiliary decision-making. Based on the collected user data, machine learning algorithms can train exercise prescription decision-making assistance models suitable for different users. These models can not only identify the personalized needs of users, but also predict the effects of different exercise programs, thereby providing scientific decision-making support for doctors and therapists. With the continuous addition of new data, these models can be continuously optimized and updated to adapt to the actual conditions and changing needs of different users.

[0058] This application uses intelligent motion capture technology to implement training supervision and feedback.

[0059] Using advanced AI motion capture technology based on the FlexMSK system architecture, accurate motion analysis and feedback are provided through video analysis, motion capture and data processing, so as to achieve accurate guidance and supervision of patients during home exercise. The system uses devices with video shooting functions such as smartphones to capture video data of patients exercising at home. Video data is pre-processed through computer vision and image processing technology, including denoising, enhancement, and inter-frame difference, to improve the accuracy of subsequent analysis. While the patient is executing the exercise prescription, the system captures the patient's motion video in real time through the camera, and uses a deep learning model to identify and analyze the patient's motion posture. Once the patient's movement is detected to be deviated or insufficient, the system will immediately provide accurate guidance to the patient through voice or text prompts. The system improves the exercise effect and reduces the risk of sports injuries, and has a high level of real-time, personalization and intelligence.

[0060] Figure 1 This is a flowchart of a method for generating an exercise prescription according to an embodiment of the present application.

[0061] like Figure 1 As shown, the method for generating an exercise prescription provided in this embodiment is applied to an electronic device, and is used to provide an exercise prescription based on information input by a user such as a patient or consultant, so that the user can implement rehabilitation treatment based on the obtained exercise prescription. In view of this, the exercise prescription belongs to a kind of medical consultation information, and this scheme can actually be regarded as a medical information consultation scheme. The electronic device can be understood as a computer, server or cloud platform with information processing and data computing capabilities. The method for generating an exercise prescription includes the following steps:

[0062] S1. Obtain multiple personal data of users.

[0063] The multiple personal data in this application include but are not limited to physical data, exercise habit data and health status data. Among them, physical data includes part or all of age, gender, height, weight, body fat percentage, heart rate and blood pressure; exercise habit data includes part or all of exercise frequency, exercise duration, exercise intensity and exercise type; health status data includes part or all of disease history, allergy history and current health status. Specifically, the collection of multiple personal data is achieved through the following steps.

[0064] On the one hand, in response to the user's input request, it receives personal data input by the user that can be based on text or voice input, such as age, gender, height, body fat percentage, heart rate and blood pressure, etc. It can also receive medical history, allergy history and current health status input by the user.

[0065] On the other hand, data about the user's body can be collected based on a variety of sensors or wearable devices configured by the user, such as real-time collection of blood pressure, heart rate, exercise frequency, exercise duration, exercise intensity, etc.

[0066] S2. Analyze and process multiple pieces of personal data to obtain user tags.

[0067] Through analysis and processing, the user's basic information, physical condition and exercise ability are evaluated to form a label, which provides a scientific basis for the subsequent generation of exercise prescriptions. Specifically, this application realizes the generation of user labels through the following specific means:

[0068] Specifically, a pre-generated machine learning algorithm is used to process part or all of the multiple personal data to obtain the user label.

[0069] The above means is to perform data processing on the basis of the obtained machine learning algorithm. In order to obtain the machine learning algorithm, the present application may also include the following means:

[0070] First, multiple sets of personal parameters of multiple patients are obtained.

[0071] The multiple sets of personal parameters here refer to the multiple sets of personal data above, but the multiple sets of personal data are different from the above in that they include not only the personal data of patients who need to be given exercise prescriptions, but also the personal data of healthy people. After obtaining the multiple sets of personal parameters, they are stored in a database, such as MySQL, MongoDB, etc.

[0072] Then, the above-mentioned multiple personal data are preprocessed, and the preprocessing process includes but is not limited to data cleaning, data standardization and feature engineering. Data cleaning refers to the processing of missing values ​​and outliers; data standardization refers to standardization and normalization; feature engineering refers to the processing of multiple preprocessed personal parameters to obtain feature parameters. Specifically, unsupervised learning algorithms (PCA principal component analysis) can be used to extract key features in the data, which can maximize the variability and information content in user data. At the same time, feature selection techniques (such as feature selection based on mutual information, chi-square test, etc.) are used to reduce feature dimensions and improve the learning efficiency and performance of subsequent models.

[0073] Finally, the model is trained based on the feature parameters to obtain the machine learning algorithm. The specific process is as follows:

[0074] Model training is performed using classification or clustering algorithms. Classification algorithms (using logistic regression, decision trees) are used to classify users into different categories, such as fitness level, athletic ability level, etc. These algorithms predict the classification labels of new users by learning the relationship between labels and features in historical data. Clustering algorithms (K-means) are used to automatically group data based on user similarities when there are no clear labels in the user data. This helps to discover potential patterns and structures in user groups, supporting the development of broader exercise guidance strategies.

[0075] In addition, regression algorithms are also included. For scenarios where specific values ​​need to be predicted, the random forest regression algorithm is used to predict the corresponding indicators of new users by learning the relationship between the features in historical data and the target variable.

[0076] S3. Generate an exercise prescription based on an exercise prescription generation algorithm.

[0077] Based on the user tags, the user tags are processed based on the exercise prescription generation algorithm to obtain the corresponding exercise prescription, and the exercise prescription is fed back to the user. This enables the user to perform rehabilitation training or receive rehabilitation treatment based on the exercise prescription. The content of the prescription includes but is not limited to the type of exercise, exercise intensity, exercise duration, exercise frequency, etc. At the same time, the doctor user can also personalize the formed exercise prescription according to the actual situation to meet the specific needs and health conditions of different patients.

[0078] The exercise prescription generation algorithm includes but is not limited to a rule engine and a recommendation algorithm. The rule engine is a rule base for generating exercise prescriptions based on professional sports medicine knowledge. The rules included in the rule base define the mapping relationship between different user tags and various elements of exercise prescriptions (exercise type, intensity, duration, frequency). The recommendation algorithm can utilize the user tag and exercise prescription action library classification system, combined with the user's historical data and the behavior of similar users, through collaborative filtering or content-based recommendation algorithms, to recommend appropriate exercise prescriptions to users.

[0079] It can be seen from the above technical solution that this embodiment provides a method for generating an exercise prescription, which is applied to an electronic device, specifically obtaining multiple personal data of a user; analyzing and processing the multiple personal data to obtain a user tag; and processing the user tag based on an exercise prescription generation algorithm to obtain an exercise prescription. This solution reduces the dependence on doctors or therapists, and can derive a suitable exercise prescription based on the specific conditions of the patient's condition.

[0080] In addition, in a specific implementation of the present application, the following steps are also included: Figure 2 shown.

[0081] S4. Receive user feedback on exercise prescription.

[0082] That is, after the user receives the exercise prescription, when he / she gives feedback based on the prescription itself or the rehabilitation process, rehabilitation effect and feelings, the user's feedback is received. This can be achieved through the user's mobile device or other network device, so that the doctor or therapist can directly get feedback and realize direct communication between the user and the doctor or therapist.

[0083] In addition, in another specific embodiment of the present application, the following steps are also included: Figure 3 shown.

[0084] S5. Optimize the exercise prescription based on the feedback.

[0085] After receiving the feedback, the doctor or therapist can optimize the exercise prescription according to the feedback, and feed back the optimized exercise prescription to the user so that the user can get better treatment according to the optimized exercise prescription.

[0086] The complete technical solution of this application includes the front end and the back end.

[0087] Technology selection for front-end development: Use front-end languages ​​such as HTML, CSS, JavaScript, etc., and use front-end development frameworks such as Vue.js, React, etc. to realize the display and interaction of the user interface. Implementation method: Develop a responsive design interface to ensure a good user experience on various devices.

[0088] Technology selection for back-end development: Choose appropriate back-end development languages ​​and frameworks such as Python (Django, Flask), Java (Spring Boot), Node.js (Express.js), etc. to implement business logic processing and database interaction. Implementation method: Build a RESTful API to provide data services for the front end and implement complex business logic and data processing.

[0089] Technical selection of the database for this application: Select a suitable database management system such as MySQL, MongoDB, etc. to design and manage the storage and query of patient rehabilitation data. Implementation method: Design a reasonable database architecture to ensure efficient storage and fast query of data.

[0090] The technology selection for data analysis and mining in this application: Use machine learning, data mining and other technologies to analyze and model a large amount of rehabilitation data, extract features, and build a personalized exercise prescription model. Implementation method: Develop and train machine learning models to conduct in-depth analysis and prediction of user data.

[0091] This application also includes mobile and cloud computing technologies. Technology selection: Based on mobile app development technology and cloud computing platform, develop online sports rehabilitation software for patients and doctors to achieve data sharing and real-time monitoring. Implementation method: Use cloud computing platform to provide data storage and computing power to achieve high availability and scalability of data services.

[0092] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0093] Although operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order.Multitasking and parallel processing may be advantageous under certain circumstances.

[0094] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0095] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0096] Figure 4 This is a block diagram of a device for generating an exercise prescription according to an embodiment of the present application.

[0097] like Figure 4 As shown, the exercise prescription generation device provided in this embodiment is applied to an electronic device, and is used to feedback an exercise prescription based on information input by a user such as a patient or a consultant, so that the user can implement rehabilitation treatment based on the obtained exercise prescription. In view of this, the exercise prescription belongs to a kind of medical consultation information, and this scheme can actually be regarded as a medical information consultation scheme. The electronic device can be understood as a computer, server or cloud platform with information processing capability and data computing capability. The exercise prescription generation device includes a data acquisition module 10, a data analysis module 20 and an exercise prescription generation module 30.

[0098] The data collection module is used to obtain multiple personal data of users. The operation content of this module is as follows Figure 5 shown.

[0099] The multiple personal data in this application include but are not limited to physical data, exercise habit data and health status data. Among them, physical data includes part or all of age, gender, height, weight, body fat percentage, heart rate and blood pressure; exercise habit data includes part or all of exercise frequency, exercise duration, exercise intensity and exercise type; health status data includes part or all of disease history, allergy history and current health status. Specifically, the collection of multiple personal data is achieved through the following steps.

[0100] On the one hand, in response to the user's input request, it receives personal data input by the user that can be based on text or voice input, such as age, gender, height, body fat percentage, heart rate and blood pressure, etc. It can also receive medical history, allergy history and current health status input by the user.

[0101] On the other hand, data about the user's body can be collected based on a variety of sensors or wearable devices configured by the user, such as real-time collection of blood pressure, heart rate, exercise frequency, exercise duration, exercise intensity, etc.

[0102] The data analysis module is used to analyze and process multiple personal data to obtain user labels. Through analysis and processing, the user's basic information, physical condition and athletic ability are evaluated to form labels, providing a scientific basis for the subsequent generation of exercise prescriptions. Specifically, this application realizes the generation of user labels through the following specific means:

[0103] Specifically, a pre-generated machine learning algorithm is used to process part or all of the multiple personal data to obtain the user label.

[0104] The above means is to perform data processing on the basis of the obtained machine learning algorithm. In order to obtain the machine learning algorithm, the present application may also include the following means:

[0105] First, multiple sets of personal parameters of multiple patients are obtained.

[0106] The multiple sets of personal parameters here refer to the multiple sets of personal data above, but the multiple sets of personal data are different from the above in that they include not only the personal data of patients who need to be given exercise prescriptions, but also the personal data of healthy people. After obtaining the multiple sets of personal parameters, they are stored in a database, such as MySQL, MongoDB, etc.

[0107] Then, the above-mentioned multiple personal data are preprocessed, and the preprocessing process includes but is not limited to data cleaning, data standardization and feature engineering. Data cleaning refers to the processing of missing values ​​and outliers; data standardization refers to standardization and normalization; feature engineering refers to the processing of multiple preprocessed personal parameters to obtain feature parameters. Specifically, unsupervised learning algorithms (PCA principal component analysis) can be used to extract key features in the data, which can maximize the variability and information content in user data. At the same time, feature selection techniques (such as feature selection based on mutual information, chi-square test, etc.) are used to reduce feature dimensions and improve the learning efficiency and performance of subsequent models.

[0108] Finally, the model is trained based on the feature parameters to obtain the machine learning algorithm. The specific process is as follows:

[0109] Model training is performed using classification or clustering algorithms. Classification algorithms (using logistic regression, decision trees) are used to classify users into different categories, such as fitness level, athletic ability level, etc. These algorithms predict the classification labels of new users by learning the relationship between labels and features in historical data. Clustering algorithms (K-means) are used to automatically group data based on user similarities when there are no clear labels in the user data. This helps to discover potential patterns and structures in user groups, supporting the development of broader exercise guidance strategies.

[0110] In addition, regression algorithms are also included. For scenarios where specific values ​​need to be predicted, the random forest regression algorithm is used to predict the corresponding indicators of new users by learning the relationship between the features in historical data and the target variable.

[0111] The exercise prescription generation module is used to generate an exercise prescription based on an exercise prescription generation algorithm.

[0112] Based on the user tags, the user tags are processed based on the exercise prescription generation algorithm to obtain the corresponding exercise prescription, and the exercise prescription is fed back to the user. This enables the user to perform rehabilitation training or receive rehabilitation treatment based on the exercise prescription. The content of the prescription includes but is not limited to the type of exercise, exercise intensity, exercise duration, exercise frequency, etc. At the same time, the doctor user can also personalize the formed exercise prescription according to the actual situation to meet the specific needs and health conditions of different patients.

[0113] The exercise prescription generation algorithm includes but is not limited to a rule engine and a recommendation algorithm. The rule engine is a rule base for generating exercise prescriptions based on professional sports medicine knowledge. The rules included in the rule base define the mapping relationship between different user tags and various elements of exercise prescriptions (exercise type, intensity, duration, frequency). The recommendation algorithm can utilize the user tag and exercise prescription action library classification system, combined with the user's historical data and the behavior of similar users, through collaborative filtering or content-based recommendation algorithms, to recommend appropriate exercise prescriptions to users.

[0114] It can be seen from the above technical solution that this embodiment provides a device for generating an exercise prescription, which is applied to an electronic device, specifically to obtain multiple personal data of a user; analyze and process the multiple personal data to obtain a user tag; and process the user tag based on an exercise prescription generation algorithm to obtain an exercise prescription. This solution reduces the dependence on doctors or therapists, and can derive a suitable exercise prescription based on the specific conditions of the patient's condition.

[0115] In addition, in a specific embodiment of the present application, a user feedback module 40 is also included, such as Figure 6 shown.

[0116] The user feedback module is used to receive user feedback on exercise prescription.

[0117] That is, after the user receives the exercise prescription, when he / she gives feedback based on the prescription itself or the rehabilitation process, rehabilitation effect and feelings, the user's feedback is received. This can be achieved through the user's mobile device or other network device, so that the doctor or therapist can directly get feedback and realize direct communication between the user and the doctor or therapist.

[0118] Also, if Figure 7 In another specific embodiment of the present application, a prescription optimization module 50 is also included, and the operation content of the module is as follows: Figure 8 shown.

[0119] The prescription optimization module is used to optimize the exercise prescription based on feedback.

[0120] After receiving the feedback, the doctor or therapist can optimize the exercise prescription according to the feedback, and feed back the optimized exercise prescription to the user so that the user can get better treatment according to the optimized exercise prescription.

[0121] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware. The name of a unit does not limit the unit itself in some cases. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses".

[0122] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0123] Fig. 9 A block diagram of an electronic device according to an embodiment of the present application.

[0124] Reference below Fig. 9 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. The electronic device is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0125] The electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory ROM 902 or a program loaded from an input device 906 into a random access memory RAM 903. In the RAM, various programs and data required for the operation of the electronic device are also stored. The processing device, ROM, and RAM are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0126] Typically, the following devices may be connected to the I / O interface: input devices including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 907 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 908 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 909. The communication device 909 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various devices, it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have alternatively.

[0127] This embodiment provides an embodiment of a computer-readable storage medium.

[0128] The computer-readable storage medium is applied to an electronic device, which carries one or more computer programs. When the one or more computer programs are executed by the electronic device, the electronic device obtains multiple personal data of the user; analyzes and processes the multiple personal data to obtain user tags; and processes the user tags based on the exercise prescription generation algorithm to obtain an exercise prescription. This solution reduces the dependence on doctors or therapists and can derive a suitable exercise prescription based on the specific conditions of the patient's condition.

[0129] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device.

[0130] In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0131] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0132] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0133] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0134] The technical solution provided by the present invention is introduced in detail above. Specific examples are used in this article to illustrate the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for generating an exercise prescription, applied to an electronic device, characterized in that: The generation method comprises the steps of: Obtain various personal data of users; Analyze and process the multiple pieces of personal data to obtain a user tag; The user tag is processed based on an exercise prescription generation algorithm to obtain an exercise prescription.

2. The generation method according to claim 1, characterized in that: The method of obtaining multiple personal data of the user comprises the following steps: In response to a user's input request, receiving the personal data input by the user; The personal data collected by various sensors or wearable devices configured on the user's body is obtained.

3. The generation method according to claim 2, characterized in that: The multiple pieces of personal data include part or all of physical data, exercise habit data and health status data, where: The physical data includes part or all of age, gender, height, weight, body fat percentage, heart rate, blood oxygen saturation, blood sugar, and blood pressure; The exercise habit data includes part or all of the exercise frequency, exercise duration, exercise intensity and exercise type; The health status data includes part or all of disease history, allergy history and current health status.

4. The generation method according to claim 1, characterized in that: The analyzing and processing of the plurality of personal data to obtain a user tag comprises the steps of: A machine learning algorithm is used to process part or all of the multiple pieces of personal data to obtain the user label.

5. The generation method according to claim 4, characterized in that: The analyzing and processing of the plurality of personal data to obtain user tags also includes the steps of: Obtain multiple sets of personal parameters for multiple patients; Preprocessing the multiple groups of personal parameters to obtain characteristic parameters; Model training is performed based on the feature parameters to obtain the machine learning algorithm.

6. The generation method according to any one of claims 1 to 5, characterized in that: The generation method further comprises the steps of: Receive user feedback on the exercise prescription.

7. The generation method according to claim 6, characterized in that: The generation method further comprises the steps of: The exercise prescription is optimized according to the feedback.

8. A device for generating exercise prescription, applied to electronic equipment, characterized in that: The generating device comprises: A data collection module, configured to obtain a plurality of personal data of the user; A data analysis module is configured to analyze and process the multiple pieces of personal data to obtain a user tag; The exercise prescription generation module is configured to process the user tag based on an exercise prescription generation algorithm to obtain an exercise prescription.

9. The generating device according to claim 8, characterized in that Also includes: The user feedback module is configured to receive user feedback on the exercise prescription.

10. The generating device according to claim 9, characterized in that Also includes: The prescription optimization module is configured to optimize the exercise prescription according to the feedback.

11. An electronic device, characterized in that: The electronic device comprises at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs or instructions; The processor is used to execute the computer program or instruction so that the electronic device implements the method for generating an exercise prescription as described in any one of claims 1 to 7.

12. A computer-readable storage medium, applied to an electronic device, characterized in that: The storage medium carries one or more computer programs, and the one or more computer programs can be executed by the electronic device, so that the electronic device implements the method for generating an exercise prescription as described in any one of claims 1 to 7.

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