CN117954046A - Intelligent feedback type orthopedics rehabilitation guidance training system - Google Patents

Abstract

The invention relates to the technical field of rehabilitation systems, in particular to an intelligent feedback type orthopedic rehabilitation guidance training system, which comprises: the system comprises a patient information input module, a wound assessment and matching module, a training guidance video library, a real-time feedback and monitoring module, a training adjustment algorithm module, a progress tracking and reporting module, an interactive communication module and a User Interface (UI); the system intelligently matches the training mode, time and intensity according to the injury of the patient. The system enables patients to perform autonomous rehabilitation training at home, achieves rehabilitation effect, and prevents symptoms such as joint stiffness, muscle atrophy, dysfunction and the like, thereby improving the effect and quality of orthopedics rehabilitation.

Description

Intelligent feedback type orthopedics rehabilitation guidance training system

Technical Field

The invention relates to the technical field of rehabilitation systems, in particular to an intelligent feedback type orthopedics rehabilitation guidance training system.

Background

Musculoskeletal injury (musculoskeletal injury) refers to injuries that affect various impact, torsion, strain, fracture, etc. of muscle and skeletal structures. Common types include fractures, muscle strains, torn ligaments, dislocation, and the like. Bone fracture (bone fracture) refers to a complete or partial fracture of a bone. Which is classified into open fracture and closed fracture according to whether it protrudes from the skin surface. Open fractures are susceptible to infection and difficult to treat. Closed fracture treatment is relatively simple. The treatment comprises protective fixation, operation reposition, internal fixation, orthopedic treatment and the like. Muscle strain (muscle strain) refers to injury to the muscles that are subject to excessive extension or contraction, commonly found in athletes and those involved in intense exercise. The degree of which is divided into one degree of strain, two degrees of strain and three degrees of strain. The treatment includes pain relieving, ice compress, local massage, physiotherapy, muscle training, etc. Ligament laceration (LIGAMENT TEAR) refers to the damage to the ligament and loss of joint stability. The treatment is mainly conservative treatment, such as pain relieving, ice compress, local massage, physiotherapy, ligament training and the like, and if the conservative treatment is ineffective, the ligament needs to be repaired by operation. Dislocation refers to the break of the connection between two bones of a joint. Is commonly found in shoulder joints, elbow joints, hip joints, knee joints, and the like. The treatment comprises the steps of manual reset or operation reset, protective fixation, physical therapy, muscle training and the like.

Overall, early diagnosis and proper therapeutic action is critical for musculoskeletal injuries. Different types and degrees of injury require different treatment modalities. The goal of the treatment is to restore function as much as possible, relieve pain, and reduce the risk of re-injury.

Many myoskeletal injury patients can not timely provide rehabilitation training and the reason that the self knowledge is mastered after injury or postoperative hospitals, miss the opportunity of early rehabilitation training, cause limb dysfunction, and make a sound to life work.

Aiming at the existing problems, the invention provides an intelligent feedback type orthopaedics rehabilitation guidance training system which provides corresponding training method videos aiming at different musculoskeletal injuries and provides time and intensity guidance for training based on teaching materials, guidelines and expert consensus. The patient can choose the proper training according to his own injury, and the system will match the corresponding training according to the injury provided by the patient. The training ending system adjusts the training process and intensity according to the symptom feedback after training. If no symptoms or slight symptoms exist, the next stage of training can be carried out; if symptoms are aggravated, the user needs to go back to the previous training stage or medical advice prompt. Symptoms include: bleeding, pain, swelling, inflammatory reactions, and the like.

Disclosure of Invention

The invention aims to provide an intelligent feedback type orthopedics rehabilitation guidance training system which is used for intelligently matching training modes, time and intensity according to the injury condition of a patient. The patient can also perform autonomous rehabilitation training at home through the system, thereby achieving the rehabilitation effect and preventing the symptoms of joint stiffness, muscle atrophy, dysfunction and the like.

In order to achieve the technical purpose and the technical effect, the invention is realized by the following technical scheme:

An intelligent feedback orthopedic rehabilitation guidance training system, comprising:

Patient information input module:

the module is used for collecting basic information of patients, including age, sex, weight, injury type (such as fracture, muscle strain, etc.), injury site, operation history, etc. Data entry may be through a Graphical User Interface (GUI) or may be automatically obtained through integration with a Hospital Information System (HIS) or an Electronic Health Record (EHR) system.

The injury evaluation and matching module:

Based on the patient's injury information, the module uses an expert system and a medical database to perform injury assessment and match the appropriate rehabilitation training. It uses a preset medical rules engine and fuzzy logic to handle uncertainty and ambiguity, providing a personalized training regimen for the patient.

Training guidance video library:

the library contains a plurality of rehabilitation training videos aiming at different orthopaedics injuries, and the rehabilitation training videos are classified according to training difficulty, training stage, required equipment and the like. In combination with the training scheme of the patient, the system can select videos matched with the current rehabilitation stage of the patient from the video library for display.

And the real-time feedback and monitoring module is used for:

This module uses sensor technology, such as a wearable device (e.g., smart watch, rehabilitation robot, etc.), to monitor the patient's physiological data (e.g., heart rate, electromyography, EMG, etc.) and the standardization of rehabilitation training actions. The data analysis algorithm will analyze the monitored data in real time and provide immediate feedback to ensure that the patient is trained in the correct way.

Training an adjustment algorithm module:

Based on real-time monitoring results and patient feedback, such as pain level, comfort, etc., the module adjusts the progress and intensity of training through machine learning algorithms (e.g., decision trees, support vector machines SVM, etc.). If the patient indicates an increased pain, the system may recommend a decrease in training intensity or return to the previous stage of training.

Progress tracking and reporting module:

the module is responsible for recording the patient's rehabilitation training history and progress, including training frequency, duration, completed exercises, etc. It can generate periodic reports for review by patients and medical professionals to assess rehabilitation effects and make further treatment decisions.

And the interactive communication module is used for:

the patient can communicate remotely with the medical professional through this module, obtaining additional guidance and support. May include text, voice, or video communications, and functions to share rehabilitation data and reports.

User Interface (UI):

An intuitive interface is provided that allows patients and medical professionals to easily operate the system, including starting training, viewing training instructions, receiving feedback, viewing progress reports, and the like.

The invention has the beneficial effects that:

1. Providing a personalized rehabilitation regimen: the intelligent feedback type orthopaedics rehabilitation guidance training system can accurately acquire the personal characteristics and the injury condition of a patient through the patient information input module and the injury evaluation and matching module, thereby providing a personalized rehabilitation scheme for the patient. Traditional rehabilitation methods are generally universal and cannot take into account individual differences and special needs of the patient. The system can be used for preparing a more targeted and adaptive rehabilitation training scheme according to the specific condition of a patient, so that the rehabilitation effect and the curative effect are improved.

2. Real-time feedback and monitoring: the intelligent feedback type orthopaedics rehabilitation guidance training system can monitor the action execution condition of the rehabilitation training of a patient in real time through the visual sensor and the action analysis algorithm and provide immediate and accurate feedback. Patients often have problems of incorrect actions, inaccurate postures and the like in rehabilitation training, and if the problems cannot be corrected in time, the training effect is possibly reduced, and even the rehabilitation process is delayed. Through real-time feedback and monitoring of the system, a patient can timely know own action problems and correct the action problems, so that recovery risks are reduced, and recovery effects are improved.

3. Rehabilitation progress tracking and reporting: the intelligent feedback type orthopedic rehabilitation guidance training system can record rehabilitation training history and progress of a patient and generate a corresponding report. Through data analysis and visual display, the medical professional can clearly understand the rehabilitation condition and progress of the patient so as to quantitatively evaluate and adjust the rehabilitation plan. In the traditional rehabilitation process, doctors and patients often have difficulty in accurately evaluating and tracking rehabilitation progress, and the rehabilitation progress tracking and reporting function of the system can solve the problem, so that rehabilitation training is more scientific and effective.

4. Providing remote communication and support: the intelligent feedback type orthopaedics rehabilitation guidance training system can be used for carrying out remote communication and consultation with medical professionals through the interactive communication module. Patients may encounter problems and confusion during rehabilitation and not get guidance and support in time, which may affect the effectiveness of rehabilitation training. Through the remote communication and support functions of the system, a patient can conveniently communicate with a doctor to obtain timely guidance and solutions, so that the effect and convenience of rehabilitation training are improved.

5. The continuity and convenience of rehabilitation training are improved: one of the biggest advantages of the intelligent feedback type orthopedic rehabilitation guidance training system is that rehabilitation training can be carried out at home, and feedback and guidance can be obtained anytime and anywhere. Traditional rehabilitation training usually requires that the patient visit a hospital or a rehabilitation center, which is a trouble for some patients who cannot perform rehabilitation training in time due to traffic, time and other reasons. The system can effectively solve the problem, improve the continuity and convenience of rehabilitation training, enable patients to perform rehabilitation training more conveniently and keep the continuity of rehabilitation effect.

In summary, the intelligent feedback type orthopedic rehabilitation guidance training system can solve the problems of insufficient generality, inaccurate action guidance, difficult rehabilitation progress tracking, inconvenient communication and support and the like of the traditional rehabilitation scheme in the traditional rehabilitation process by providing the personalized rehabilitation scheme, real-time feedback and monitoring, rehabilitation progress tracking and reporting, remote communication and support, improving the continuity and convenience of rehabilitation training and the like, so that the orthopedic rehabilitation effect and quality are improved.

Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Example 1

An intelligent feedback type orthopedics rehabilitation guidance training system according to the embodiment includes:

1. patient information input module

The module is the first point of contact of the user with the rehabilitation training system, and collects basic information and injury details of the patient through a friendly user interface:

User authentication: a safe user authentication system is realized, which comprises functions of registering, logging in and retrieving passwords, and the account security is protected by adopting an OAuth 2.0 protocol and double-factor verification.

Personal data management: forms are provided for the patient to enter or update personal information, such as name, date of birth, contact, gender, weight, etc., which are used to personalize the subsequent rehabilitation regimen.

Medical information input: specific input fields and selectors are designed so that the patient can accurately record the category of injury (drop down menu), the location of injury (picture selection or check box), the history of surgery (radio buttons), and the current symptoms (text box).

And (3) data verification: field verification is implemented at the front end to ensure the correctness and integrity of the user input format, and data cleaning and verification are further performed at the back end.

Privacy and security: ensuring confidentiality of personal and medical information, encrypting all transport layer data using SSL/TLS, and storing sensitive data at the server side using an encryption algorithm (e.g., AES-256).

2. Injury evaluation and matching module

This module is responsible for matching the preliminary rehabilitation regimen that is most appropriate for the patient:

Expert system integration: an expert system based on knowledge rules is developed that combines medical guidelines and historical data to evaluate the condition of a patient.

Injury classification algorithm: decision tree algorithms are used to classify the patient for the patient's injury level. The decision tree learns through training data how to predict the most likely rehabilitation regimen based on medical record characteristics (e.g., type and location of injury).

Personalized rehabilitation program: after the injury level is determined, a rehabilitation program matching the injury level is selected or generated. This may include planned duration, projected milestones, daily or weekly training frequency.

Data driven update: the decision rules and classification models of the expert system are updated periodically to optimize the algorithm based on new medical study and historical rehabilitation data.

Model transparency: explicit rules and parameter interpretations are provided to the healthcare provider to facilitate understanding of the decision making process and intervention as needed.

3. Training instruction video library

This module is the core educational resource for the rehabilitation process:

Video content planning: and in cooperation with professionals in the rehabilitation field, a set of comprehensive rehabilitation training video is developed, and the content effectiveness and accuracy are ensured.

Video management system: a management interface is established to allow an administrator to categorize, annotate and update the video. The tags include difficulty level, target muscle group, required equipment, etc.

Intelligent recommendation engine: the historical viewing habits of the patient are analyzed by a machine learning model (e.g., collaborative filtering) to recommend personalized video content.

Interactive video functionality: and providing functions such as variable speed playing, marking points, leaving messages and asking questions on a video playing interface.

User feedback integration: and collecting feedback of the user on the video content, and optimizing the quality of the video library and the accuracy of the recommendation system.

4. Real-time feedback and monitoring module

The module provides real-time exercise feedback to the user via the sensor and analysis software:

Sensor selection and integration: suitable biofeedback sensors, such as accelerometers, are selected for sensing movement for common rehabilitation exercises, pressure sensors monitor weight distribution, and electromyographic sensors (EMG) evaluate muscle activity.

Data acquisition protocol: a bluetooth or Wi-Fi communication protocol is defined, as well as specifications for data collection frequency, resolution, etc., to normalize the sensor data stream.

Real-time data analysis: real-time data processing algorithms, such as sliding window algorithms, are developed to analyze time series data, or feature extraction algorithms are used for pattern recognition of electromyographic signals.

User feedback mechanism: a visual feedback interface is designed to inform the user whether the exercise is being performed correctly, in the form of a chart, color coding, or vibration alarm, etc.

Abnormality detection system: machine learning algorithms, such as a Class of support vector machines (One-Class SVMs), are implemented to detect abnormal behavior or potential risk of injury and to give a warning.

5. Training and adjusting algorithm module

This module ensures continuous adaptation of the rehabilitation training program:

user feedback processing: the form and interface are set up so that the user can evaluate their symptoms, such as pain, discomfort, etc., after each training and record the data into the system.

Training adjustment logic: based on machine learning algorithms, such as multi-layer perceptron (MLP), user feedback and sensor data are used to predict the personalized adjustments of the training.

Parameter optimization: the hyper-parameters of the MLP, such as learning rate, number of layers and activation functions, are optimized by algorithms such as grid search (GRID SEARCH) and cross-validation.

Continuous learning mechanism: implementing an online learning strategy allows the system to dynamically adjust the internal model based on new user data without having to reconstruct the model from scratch.

Medical specialist auditing: the tool is provided for enabling medical professionals to review and adjust training plan adjustment suggestions proposed by the algorithm, and medical accuracy and safety are ensured.

6. Progress tracking and reporting module

This module is used to measure and record the progress of the patient during rehabilitation:

and (3) data storage: a database architecture is built to hold a training history of the patient, including the date, duration, type of training completed, etc. of each training.

Progress analysis engine: development analysis tools evaluate training data and calculate Key Performance Indicators (KPIs) such as consistency, speed of progression, etc.

Report generator: the construction algorithm automatically aggregates these data with KPIs, generating an easily understood progress report.

Graphical interface: visual progress charts and trend lines are dynamically generated by using a chart library (such as D3.js or HIGHCHARTS) to assist patients and doctors in understanding rehabilitation situations.

Periodic report transmission: an automatic sending mechanism, such as mail service or App push, is set up to send the generated report to the patient and the healthcare team periodically.

7. Interactive communication module

The module provides a multi-functional communication platform to facilitate communication between patients and medical teams:

an online consultation system: a counseling system is deployed to allow users to communicate with doctors through text, voice or video.

Data sharing: a sharing function is implemented in the system that allows users to selectively share their training data and progress reports with doctors.

Instant messaging service: instant messaging solutions are implemented to facilitate quick questions and feedback between the user and the healthcare provider.

Schedule coordination tool: an embedded calendar and appointment system is designed to help users arrange remote consultation or face-to-face rehabilitation conferences.

Recording and auditing: all communications are ensured to be recorded and stored securely for medical administration and future reference.

8. User Interface (UI)

An intuitive, efficient and friendly UI is crucial to ensure the user experience:

And (3) response type design: the UI should be able to accommodate a variety of screen sizes and devices, including smartphones, tablets, and computers.

Interactivity: dynamic element and micro-interaction design is implemented to improve user engagement and ease of use of the system.

Navigation and layout: clear and intuitive navigation structures and layouts are designed so that users quickly find the functions and information they need.

Individualizing: based on the user's behavior and preferences, personalized UI operations are provided, such as custom themes, font size selections, and the like.

Auxiliary functions: ensuring that the UI complies with barrier-free standards and enabling disabled persons to use the system easily.

Example 2

An intelligent feedback type orthopedics rehabilitation guidance training system according to the embodiment includes:

1. patient information input module: the module is responsible for collecting patient personal data and rehabilitation related medical information.

The implementation steps are as follows:

The user accesses the system through a secure login interface to log in or register a new account. The system encrypts all communication through HTTPS protocol to ensure data transmission safety.

In the personal information input page, the user fills out fields including name, age (birth date automatically generates age), sex (single option), weight (numerical input box of unit kg), and the like.

The patient fills out a medical history form, entering the type of injury (drop down selection menu, including fracture, muscle strain, etc.), the wound site (multiple boxes listing all possible body parts), the history of surgery (single choice: yes/no), and the current symptoms (text boxes, user detailed description).

The database uses MySQL and the application InnoDB stores the engine management table structure, ensuring internationalization support with UTF-8 coding. Sensitive personal information fields such as name, address, etc. are stored encrypted using the AES-256 encryption algorithm. The defined API interface adopts JSON format to exchange data and sets request limit to prevent DDoS attack.

2. The injury evaluation and matching module: based on the data entered by the user, the patient's severity of the injury is assessed and an appropriate rehabilitation program is assigned.

The data provided by the patient in the database is read using a server-side script (e.g., python Flask application).

And classifying the severity of the injury by using a pre-trained SVM model according to the injury data of the patient, and distributing an initial rehabilitation plan according to the classification result.

The results of the rehabilitation program are written back to the database and a link is provided in the user interface that the patient can click on to view the training instruction video tailored to their fit.

The injury evaluation and matching module uses scikit-learn library of Python to realize a support vector machine classifier. Training data of the SVM model is based on historic patient injury and rehabilitation effect data. The classifier training process uses a cross-validation approach to optimize parameters such as regularization coefficient C and kernel type.

3. Training guidance video library: and providing classified and marked rehabilitation training video resources.

The implementation steps are as follows:

Video content is classified according to the type (such as strength and action complexity) of rehabilitation training, and each video is provided with metadata such as brief introduction, recommended injury type and difficulty level.

After the matching module determines the training program of the user, the corresponding training video is retrieved from the video library, and an access link is provided for the user.

The user clicks the link, and the video is loaded and watched by the video player embedded in the web page.

The video file is stored in the MP4 format in a cloud storage service such as Amazon S3, and access rights are set to ensure that only authenticated users can view the video file. And writing an interaction script by combining the < video > tag of the HTML5 with JavaScript to realize video playing functions including playing, pausing, adjusting volume and the like.

4. And the real-time feedback and monitoring module is used for: monitoring of patient rehabilitation training is performed by paired sensor devices and real-time feedback is provided.

The implementation steps are as follows:

The patient wears the bluetooth sensor on the corresponding limb before training.

In the training process, the sensor continuously measures and records motion data and electromyographic signals and transmits the motion data and electromyographic signals to a smart phone or a tablet personal computer of a patient in real time through Bluetooth.

The mobile application receives the data, builds an electromyographic signal image, judges whether the electromyographic signal image exceeds a safety range or not through a preset threshold value, and sends out a warning once the electromyographic signal image exceeds the safety range.

The accelerometer uses a triaxial ADXL345 with variable range + -2 g to + -16 g, and a configurable data output rate. The myoelectric sensor uses MyoWare Muscle Sensor, and the output analog signal corresponds to muscle activity, and is digitized by an analog-to-digital converter (such as ArduinoADC) and then transmitted to the mobile device. Real-time data processing uses a Fast Fourier Transform (FFT) algorithm to convert the time domain signal of the sensor to the frequency domain for analysis.

5. Training an adjustment algorithm module: the user's personalized rehabilitation training program is adjusted according to the training feedback and the monitoring data of the user.

The implementation steps are as follows:

The patient enters the feedback interface after each training to fill in their symptoms, such as pain intensity, swelling, etc.

The system stores these user feedback and feeds into the training adjustment model along with the monitoring data.

The model evaluates the training effect according to the new data and adjusts the difficulty or type of the next training according to the requirement. If symptom exacerbations are detected, the system may automatically reduce the difficulty or recommend a medical visit.

The training and adjusting model is realized by using a logistic regression algorithm, and is trained by a scikit-learn library of Python, and the probability value is used as an evaluation index of the rehabilitation progress of the patient. The logistic regression model parameters were set as: the solver= 'liblinear' optimizes the classification problem and the penalty= 'L2' introduces L2 regularization to reduce the overfitting. The adjustment algorithm will determine if the training program needs to be modified based on the probability values predicted by the logistic regression model (threshold set to 0.5).

6. Progress tracking and reporting module: and recording the detailed information of the rehabilitation training of the patient, and generating a regular progress report.

The implementation steps are as follows:

at the end of each training session, the system automatically records training frequency, duration, completed exercises, etc. data.

Based on the recorded data, the system automatically generates a progress report at predetermined time periods (e.g., weekly).

The system sends the report to the patient and his medical team via email or mobile in-app messages.

The progress tracking and reporting module processes and analyzes the data sets using NumPy and Pandas to generate training statistics. A Matplotlib library of Python was used to generate progress charts, such as bar charts and line charts, for the report. The report generating tool is configured to integrate the statistical information and the chart into a PDF formatted report using the ReportLab library of Python.

7. And the interactive communication module is used for: an instant messaging and remote consultation platform between patients and medical staff.

The implementation steps are as follows:

The patient selects the "contact doctor" function on the system interface to enter an online chat room.

The medical staff receives the inquiry and communicates with the patient through the system to provide professional medical consultation.

The communication records are stored in a database for review and follow-up of the patient's condition at any time.

The interactive communication module realizes an instant communication function by using WebSocket, and the server realizes message pushing by using node. Js and Socket. IO. All communication data are encrypted through TLS, so that the privacy and the safety of medical consultation are ensured. MongoDB is used as NoSQL database to store and search instant message record, and uses its efficient read-write performance to optimize chat experience.

8. User Interface (UI): a graphical interface is provided for the patient to interact with the system.

The implementation steps are as follows:

The UI designer based on the user experience (UX) best practice design interface ensures that the user smoothly completes all phases of the rehabilitation training.

The interface comprises a training guide, feedback after training is filled, a training progress is checked, a doctor interacts with the interface and other functional inlets, and convenience of touch operation is considered.

And (3) periodically performing iterative optimization on the UI design according to user feedback, so as to ensure the intuitiveness and usability of the interface.

The User Interface (UI) uses the Vue.js framework to construct a Single Page Application (SPA), so that the interface response speed and the user interaction smoothness are improved. The use of a Bootstrap framework ensures that the UI has responsiveness and suitability for a variety of device and screen sizes. Following the WCAG 2.1 standard, it is ensured that the UI design meets the requirements for unobstructed access, including consideration for achromatopsia and visually impaired users.

The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.

Claims (5)

1. An intelligent feedback type orthopedic rehabilitation guidance training system, which is characterized by comprising:

Patient information input module:

The module is used for collecting basic information of a patient, including age, sex, weight, injury type, injury part and operation history; the data input can be performed through a graphical user interface, and can also be automatically acquired through integration with a hospital information system or an electronic health record system;

The injury evaluation and matching module:

based on the injury information of the patient, the module utilizes an expert system and a medical database to perform injury assessment and match proper rehabilitation training; the method uses a preset medical rule engine and fuzzy logic to process uncertainty and ambiguity and provide a personalized training scheme for patients;

Training guidance video library:

The library comprises a plurality of rehabilitation training videos aiming at different orthopaedics injuries, and the rehabilitation training videos are classified according to training difficulty, training stage and required equipment; in combination with the training scheme of the patient, the system can select a video matched with the current rehabilitation stage of the patient from a video library for display;

And the real-time feedback and monitoring module is used for:

This module uses sensor technology, including wearable devices, to monitor the patient's physiological data and the standardization of rehabilitation training actions; the data analysis algorithm can analyze the monitoring data in real time and provide instant feedback so as to ensure that the patient trains according to a correct method;

Training an adjustment algorithm module:

According to real-time monitoring results and feedback of patients, including pain degree and comfort level, the module adjusts the progress and strength of training through a machine learning algorithm; if the patient indicates an increased pain, the system may recommend a decrease in training intensity or return to the previous stage of training;

Progress tracking and reporting module:

the module is responsible for recording rehabilitation training history and progress of a patient, including training frequency, duration and completed exercises; the system can generate periodic reports for patients and medical professionals to review so as to evaluate rehabilitation effect and make further treatment decisions;

And the interactive communication module is used for:

the patient can communicate with the medical professional remotely through the module to obtain additional guidance and support; may include text, voice, or video communications, and functions to share rehabilitation data and reports;

User interface:

an intuitive interface is provided to allow patients and medical professionals to easily operate the system, including starting training, viewing training instructions, receiving feedback, and viewing progress reports.

2. The intelligent feedback orthopedic rehabilitation guidance training system of claim 1, wherein the injury assessment and matching module specifically comprises:

expert system integration: developing an expert system based on knowledge rules that combines medical guidelines and historical data to evaluate the condition of a patient;

Injury classification algorithm: using a decision tree algorithm to classify the patient as a lesion level; the decision tree learns how to predict the most probable rehabilitation scheme according to the characteristics of medical records through training data;

personalized rehabilitation program: after determining the injury level, selecting or generating a rehabilitation plan matched with the injury level; this may include planned duration, projected milestones, daily or weekly training frequency;

data driven update: updating decision rules and classification models of the expert system regularly, and optimizing algorithms according to new medical research and historical rehabilitation data;

Model transparency: explicit rules and parameter interpretations are provided to the healthcare provider to facilitate understanding of the decision making process and intervention as needed.

3. The intelligent feedback orthopedic rehabilitation coaching system of claim 1 wherein the coaching video library specifically comprises:

Video content planning: developing a set of comprehensive rehabilitation training video in cooperation with professionals in the rehabilitation field, and ensuring content effectiveness and accuracy;

video management system: establishing a management interface to allow an administrator to classify, mark and update videos; the label comprises a difficulty level, a target muscle group and required equipment;

Intelligent recommendation engine: analyzing the historical watching habit of the patient through a machine learning model, and recommending personalized video content;

Interactive video functionality: providing functions on a video playing interface, including variable speed playing, marking points and leaving messages for asking questions;

User feedback integration: and collecting feedback of the user on the video content, and optimizing the quality of the video library and the accuracy of the recommendation system.

4. The intelligent feedback orthopedic rehabilitation coaching system of claim 1 wherein the real-time feedback and monitoring module specifically comprises:

Sensor selection and integration: selecting a proper biofeedback sensor for common rehabilitation exercises, wherein the biofeedback sensor comprises an accelerometer for sensing movement, a pressure sensor for monitoring weight distribution, and a myoelectric sensor for evaluating muscle activity;

data acquisition protocol: defining a Bluetooth or Wi-Fi communication protocol, and a data collection frequency and resolution specification to normalize the sensor data stream;

Real-time data analysis: developing a real-time data processing algorithm, wherein the real-time data processing algorithm comprises a sliding window algorithm for analyzing time sequence data or a characteristic extraction algorithm for pattern recognition of electromyographic signals;

User feedback mechanism: a visual feedback interface is designed to inform a user whether to correctly execute exercises or not in a form of a chart, color codes or vibration alarms;

abnormality detection system: machine learning algorithms are implemented for detecting abnormal behavior or potential risk of injury and giving warnings.

5. The intelligent feedback orthopedic rehabilitation coaching system of claim 1 wherein the coaching adjustment algorithm module specifically comprises:

User feedback processing: the form and interface are set to allow the user to evaluate their symptoms, including pain, discomfort, and record data into the system after each exercise;

training adjustment logic: based on a machine learning algorithm, predicting a personalized adjustment of the training using feedback of the user and the sensor data;

Parameter optimization: optimizing the hyper-parameters of the MLP through algorithms including grid search and cross validation, including learning rate, layer number and activation function;

Continuous learning mechanism: implementing an online learning strategy to enable the system to dynamically adjust the internal model according to new user data without reconstructing the model from scratch;

medical specialist auditing: the tool is provided for enabling medical professionals to review and adjust training plan adjustment suggestions proposed by the algorithm, and medical accuracy and safety are ensured.