A method and system for rehabilitation of knee motion impairment

Abstract

The application discloses a kind of knee joint movement injury rehabilitation method and system, comprising the following steps: S1, data acquisition: through multimodal sensing module acquisition user's kinematics data, dynamics data and physiological parameters, the present application relates to movement injury rehabilitation technical field.A kind of knee joint movement injury rehabilitation method and system, through five modal sensors collect kinematics, dynamics and physiological parameters, combine individualized biomechanical model to fully reflect the state of knee joint, based on injury type, course of disease and individual characteristics generation sub-stage scheme, solve the pain points of traditional rehabilitation individualization deficiency, improve training safety and adaptability, through tactile and auditory compound prompt, wherein, deviation angle is associated with prompt frequency, force line deviation corresponds to vibration position, can quickly complete action deviation correction, reduce compensatory injury caused by error training, compared with traditional manual observation or lag analysis, significantly improve training standardization and immediate error correction ability.

Description

Technical Field

[0001] This invention relates to the field of sports injury rehabilitation technology, specifically to a method and system for the rehabilitation of knee joint sports injuries. Background Technology

[0002] The reference patent title is: A Method and System for Guiding and Evaluating Knee Joint Rehabilitation Training (Authorization Announcement No.: CN110327591A, Authorization Announcement Date: 2019.10.15). This method includes: sending a pre-made knee joint rehabilitation training plan to the patient's end based on the patient's confirmed training plan, enabling the patient to complete knee joint rehabilitation training according to the plan; acquiring the motion data of the knee joint rehabilitation training recorded by the patient's end and sending it to the doctor's end for the doctor to view the patient's training status each time and evaluate the patient's training status each time; and feeding back the evaluation results to the patient's end so that the patient can improve each movement in the rehabilitation training based on the evaluation results. This method and system can digitize and visualize the patient's training process, allowing doctors to monitor the patient's rehabilitation training progress at any time and improving the efficiency of monitoring and managing the patient's rehabilitation training.

[0003] Based on the aforementioned documents, the knee joint is one of the most weight-bearing and structurally complex joints in the human body, with a high incidence of sports injuries (such as ligament tears, meniscus injuries, and cartilage wear). Its rehabilitation process must balance safety and effectiveness. However, existing rehabilitation techniques are mostly based on group data and do not consider the individual biomechanical characteristics of patients. This can easily lead to secondary injuries due to excessive training intensity or affect rehabilitation efficiency due to insufficient training intensity. Furthermore, existing rehabilitation techniques often rely on a single motion sensor, making it difficult to comprehensively assess the joint's condition. Finally, existing rehabilitation techniques depend on therapists' visual observation or subsequent video analysis, which cannot correct incorrect movements in real time. Long-term incorrect training may lead to compensatory patterns, affecting rehabilitation outcomes. Therefore, this invention provides a method and system for knee joint sports injury rehabilitation. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for the rehabilitation of knee joint sports injuries. It solves the problems of existing technologies, which are based on group data without considering individual biomechanical characteristics, rely on a single sensor to make it difficult to comprehensively assess the joint status, and rely on manual observation to make it impossible to correct incorrect movements in real time, thus easily causing secondary injuries or affecting rehabilitation efficiency and effectiveness.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for rehabilitation of knee joint sports injuries, comprising the following steps: S1. Data Acquisition: The user's knee joint kinematic data, dynamic data, and physiological parameters are acquired through a multimodal sensing module. S2. Model Construction: Based on the data collected in S1, combined with the user's injury type, disease course and body characteristic parameters, a personalized knee joint biomechanical model is constructed. The model includes the force transmission characteristics of the musculoskeletal system and the mechanical parameters of the joint soft tissue. S3. Program Generation: Based on the analysis results of the personalized biomechanical model, the clinical pathways in the rehabilitation medicine database are called to generate a phased initial rehabilitation program. The program includes the type of training movement, the number of repetitions per set, the training interval, and the intensity control index. S4. Real-time motion correction: When the user performs rehabilitation training, the motion sensing module collects motion data in real time and compares it with standard motion parameters. When the deviation exceeds the preset threshold, it corrects the error in real time through a combination of tactile and auditory prompts. S5. Effect Evaluation and Program Optimization: Regularly collect post-rehabilitation data and input it into the personalized biomechanical model. Combine this with the user's subjective rating to generate a quantitative evaluation report. Dynamically adjust the training content, intensity, and cycle of the rehabilitation program through feedback adjustment algorithms.

[0006] Preferably, the multimodal sensing module in S1 includes a flexible array pressure sensor, an inertial measurement unit, a surface electromyography sensor, an infrared temperature sensor, and a photoelectric blood oxygen sensor, wherein: The flexible array pressure sensor is attached to the medial and lateral condyles and patellar region of the knee joint, with a sampling frequency ≥50Hz; The inertial measurement unit is fixed to the mid-thigh, the outer side of the knee joint, and the mid-calf, respectively, to collect three-dimensional acceleration and angular velocity data; The surface electromyography sensor is attached to the belly of the quadriceps femoris, hamstring, and gastrocnemius muscles. The infrared temperature sensor is attached to the skin 2-3 cm above the patella of the knee joint; The photoelectric blood oxygen sensor is attached to the skin on the inside of the knee joint to collect local tissue blood oxygen saturation, with a sampling frequency ≥10Hz.

[0007] Preferably, the process of constructing the personalized biomechanical model in S2 includes: S2-1. Establish a digital model of skeletal structure based on the user's height, weight, lower limb length, and MRI image data; S2-2. The joint torque is calculated using the inverse dynamics algorithm, and the degree of muscle activation is analyzed in combination with the amplitude of surface electromyography signals. S2-3. Optimize the model parameters using the least squares method to ensure that the error between the simulated joint contact force and the measured data from the pressure sensor is ≤8%.

[0008] Preferably, the phased rules of the initial rehabilitation program in S3 include 0-7 days as the acute inflammation phase, 2-4 weeks as the fiber repair phase, and 5-12 weeks as the functional recovery phase, wherein: During the acute inflammatory phase: focus on static joint stabilization training, with each training session lasting ≤10 minutes, twice a day, and the intensity of electromyographic activity controlled at 20%-25% of the maximum voluntary contraction. During the fiber repair period: increase progressive resistance training, with resistance increasing by 5%-8% each week, and keep joint range of motion within a pain-free range; Functional recovery period: Introduce balance and coordination training, gradually increasing the single-leg standing time from 10 seconds to 60 seconds.

[0009] Preferably, the prompting method for real-time motion correction in S4 includes: Auditory cues: Different frequencies of cues are emitted through wearable bone conduction headphones. For every 3° increase in the deviation angle, the frequency of the cues increases by 50Hz. Tactile feedback: The protective gear generates graded vibrations through a built-in vibration module. The varus and valgus deviations of the knee joint correspond to the medial and lateral vibrations. When the deviation exceeds 10°, the vibration intensity increases to 500Hz.

[0010] This invention also discloses a knee joint sports injury rehabilitation system, comprising: Multimodal sensing module: used to collect knee joint kinematics, dynamics data and physiological parameters, including flexible pressure sensor, IMU, surface electromyography sensor, temperature sensor and blood oxygen sensor; Data processing module: Connected to the multimodal sensing module, it filters and fuses the collected data to build and update personalized biomechanical models; The program generation module generates phased rehabilitation training programs based on the analysis results of biomechanical models and the rehabilitation database. Motion correction module: Receives motion data in real time and compares it with standard parameters, and outputs correction prompts through auditory and tactile modules; Feedback adjustment module: Receives rehabilitation assessment data and user feedback, and adjusts the training parameters of the rehabilitation program through a preset algorithm.

[0011] Preferably, the multimodal sensing module is integrated into the wearable knee brace. The brace adopts a segmented design, and the tightness is adjusted by Velcro. Sensor data is transmitted via Bluetooth Low Energy, and the battery life is ≥12 hours. The data processing module includes a local processing unit and a cloud analysis unit. The local processing unit uses an ARM Cortex-M4 processor to calculate joint range of motion and muscle activation in real time. The cloud analysis unit uses a random forest algorithm to optimize the prediction accuracy of the biomechanical model based on historical training data.

[0012] Preferably, the standard parameter library of the motion correction module contains three-dimensional motion parameters of 30+ common rehabilitation movements, and the angle error threshold of each movement can be dynamically adjusted according to the user's rehabilitation stage; the evaluation indicators of the feedback adjustment module include objective indicators and subjective indicators. When the improvement rate of objective indicators is <8% or the subjective indicators deteriorate in two consecutive evaluations, the program adjustment is automatically triggered, and the adjustment range is ±15% of the training intensity or 20%-30% of the training movements are replaced.

[0013] Preferably, the objective indicators include joint range of motion improvement rate, muscle strength symmetry, and pressure distribution uniformity, and the subjective indicators include pain score and fatigue score.

[0014] This invention provides a method and system for rehabilitation of knee joint sports injuries. Compared with existing technologies, it has the following advantages: 1. This method and system for rehabilitation of knee joint sports injuries collects kinematic, dynamic and physiological parameters through five-modal sensors, and combines them with a personalized biomechanical model to comprehensively reflect the state of the knee joint. Based on the injury type, disease course and individual characteristics, it generates a phased plan to solve the pain point of insufficient personalization in traditional rehabilitation and improve the safety and adaptability of training.

[0015] 2. This method and system for rehabilitation of knee joint sports injuries uses a combination of tactile and auditory cues. The deviation angle is associated with the frequency of the cues and the force line deviation corresponds to the vibration position. This allows for rapid correction of movement deviations and reduces compensatory injuries caused by incorrect training. Compared with traditional manual observation or delayed analysis, this significantly improves the standardization of training and the ability to correct errors in real time.

[0016] 3. This method and system for rehabilitation of knee joint sports injuries integrates objective indicators such as joint range of motion improvement rate, muscle strength symmetry, and pressure distribution uniformity with subjective feedback such as pain score and fatigue score. When continuous assessments fail to meet the standards, the program is automatically adjusted to ensure that the rehabilitation program dynamically matches the recovery progress, solving the problem of lag in traditional experience-based adjustments and accelerating the rehabilitation process. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the operation of the knee joint sports injury rehabilitation method of the present invention. Figure 2 This is a block diagram of the knee joint sports injury rehabilitation system of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1-2 The present invention provides a technical solution: A method for rehabilitation of knee joint sports injuries includes the following steps: S1. Data Acquisition: The user's knee joint kinematic data, dynamic data, and physiological parameters are acquired through a multimodal sensing module. S2. Model Construction: Based on the data collected in S1, combined with the user's injury type, disease course and body characteristic parameters, a personalized knee joint biomechanical model is constructed. The model includes the force transmission characteristics of the musculoskeletal system and the mechanical parameters of the joint soft tissue. S3. Program Generation: Based on the analysis results of the personalized biomechanical model, the clinical pathways in the rehabilitation medicine database are called to generate a phased initial rehabilitation program. The program includes the type of training movement, the number of repetitions per set, the training interval, and the intensity control index. S4. Real-time motion correction: When the user performs rehabilitation training, the motion sensing module collects motion data in real time and compares it with standard motion parameters. When the deviation exceeds the preset threshold, it corrects the error in real time through a combination of tactile and auditory prompts. S5. Effect Evaluation and Program Optimization: Regularly collect post-rehabilitation data and input it into the personalized biomechanical model. Combine this with the user's subjective rating to generate a quantitative evaluation report. Dynamically adjust the training content, intensity, and cycle of the rehabilitation program through feedback adjustment algorithms.

[0020] Kinematic data include: joint range of motion, trajectory, and angular velocity; Dynamic data includes: joint forces and pressure distribution; Physiological parameters include: surface electromyography signal, skin temperature, and blood oxygen saturation; By collecting kinematic, dynamic, and physiological parameters through five-modal sensors and combining them with a personalized biomechanical model to comprehensively reflect the knee joint status, a phased plan is generated based on the injury type, disease course, and individual characteristics. This addresses the pain point of insufficient personalization in traditional rehabilitation and improves training safety and adaptability.

[0021] In this embodiment, the multimodal sensing module in S1 includes a flexible array pressure sensor, an inertial measurement unit, a surface electromyography sensor, an infrared temperature sensor, and a photoelectric blood oxygen sensor, wherein: The flexible array pressure sensor is attached to the medial and lateral condyles and patellar region of the knee joint, with a sampling frequency ≥50Hz; The inertial measurement unit is fixed to the mid-thigh, the outer side of the knee joint, and the mid-calf, respectively, to collect three-dimensional acceleration and angular velocity data; The surface electromyography sensor is attached to the belly of the quadriceps femoris, hamstring, and gastrocnemius muscles. The infrared temperature sensor is attached to the skin 2-3 cm above the patella of the knee joint; The photoelectric blood oxygen sensor is attached to the skin on the inside of the knee joint to collect local tissue blood oxygen saturation, with a sampling frequency ≥10Hz.

[0022] The infrared temperature sensor has a measurement range of 32-42℃ and an accuracy of ±0.2℃. In this embodiment, the construction process of the personalized biomechanical model in S2 includes: S2-1. Establish a digital model of skeletal structure based on the user's height, weight, lower limb length, and MRI image data; S2-2. The joint torque is calculated using the inverse dynamics algorithm, and the degree of muscle activation is analyzed in combination with the amplitude of surface electromyography signals. S2-3. Optimize the model parameters using the least squares method to ensure that the error between the simulated joint contact force and the measured data from the pressure sensor is ≤8%.

[0023] In this embodiment, the phased rules of the initial rehabilitation program in S3 include 0-7 days as the acute inflammation phase, 2-4 weeks as the fiber repair phase, and 5-12 weeks as the functional recovery phase, wherein: During the acute inflammatory phase: focus on static joint stabilization training, with each training session lasting ≤10 minutes, twice a day, and the intensity of electromyographic activity controlled at 20%-25% of the maximum voluntary contraction. During the fiber repair period: increase progressive resistance training, with resistance increasing by 5%-8% each week, and keep joint range of motion within a pain-free range; Functional recovery period: Introduce balance and coordination training, gradually increasing the single-leg standing time from 10 seconds to 60 seconds.

[0024] In this embodiment, the prompting methods for real-time motion correction in S4 include: Auditory cues: Different frequencies of cues are emitted through wearable bone conduction headphones. For every 3° increase in the deviation angle, the frequency of the cues increases by 50Hz. Tactile feedback: The protective gear generates graded vibrations through a built-in vibration module. The varus and valgus deviations of the knee joint correspond to the medial and lateral vibrations. When the deviation exceeds 10°, the vibration intensity increases to 500Hz.

[0025] This invention also discloses a knee joint sports injury rehabilitation system, comprising: Multimodal sensing module: used to collect knee joint kinematics, dynamics data and physiological parameters, including flexible pressure sensor, IMU, surface electromyography sensor, temperature sensor and blood oxygen sensor; Data processing module: Connected to the multimodal sensing module, it filters and fuses the collected data to build and update personalized biomechanical models; The program generation module generates phased rehabilitation training programs based on the analysis results of biomechanical models and the rehabilitation database. Motion correction module: Receives motion data in real time and compares it with standard parameters, and outputs correction prompts through auditory and tactile modules; Feedback adjustment module: Receives rehabilitation assessment data and user feedback, and adjusts the training parameters of the rehabilitation program through a preset algorithm.

[0026] In this embodiment, the multimodal sensing module is integrated into the wearable knee brace. The brace adopts a segmented design, and the tightness is adjusted by Velcro. Sensor data is transmitted via Bluetooth Low Energy, and the battery life is ≥12 hours. The data processing module includes a local processing unit and a cloud analysis unit. The local processing unit uses an ARM Cortex-M4 processor to calculate joint range of motion and muscle activation in real time. The cloud analysis unit uses a random forest algorithm to optimize the prediction accuracy of the biomechanical model based on historical training data.

[0027] In this embodiment, the standard parameter library of the motion correction module contains three-dimensional motion parameters of 30+ common rehabilitation movements. The angle error threshold of each movement can be dynamically adjusted according to the user's rehabilitation stage. The evaluation indicators of the feedback adjustment module include objective indicators and subjective indicators. When the improvement rate of objective indicators is <8% or the subjective indicators deteriorate in two consecutive evaluations, the program adjustment is automatically triggered. The adjustment range is ±15% of the training intensity or 20%-30% of the training movements are replaced.

[0028] The annotation parameter library includes three-dimensional motion parameters for 30+ rehabilitation movements such as straight leg raises, wall squats, and single-leg standing. For example, the standard value for the knee flexion angle of the wall squat is 60°±3° in the acute phase and 90°±5° in the recovery phase. In this embodiment, the objective indicators include the improvement rate of joint mobility, muscle strength symmetry, and pressure distribution uniformity, while the subjective indicators include pain score and fatigue score.

[0029] By combining tactile and auditory cues, including the correlation between deviation angle and cue frequency, and the correlation between force line deviation and vibration position, movement deviation can be quickly corrected, reducing compensatory injuries caused by incorrect training. Compared with traditional manual observation or delayed analysis, this significantly improves training standardization and immediate error correction capabilities.

[0030] By integrating objective indicators such as joint range of motion improvement rate, muscle strength symmetry, and pressure distribution uniformity with subjective feedback such as pain score and fatigue score, the rehabilitation plan is automatically adjusted when continuous assessments fail to meet the standards. This ensures that the rehabilitation plan dynamically matches the recovery progress, solves the problem of lag in traditional experience-based adjustments, and accelerates the rehabilitation process.

[0031] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0032] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0033] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for rehabilitation of knee joint sports injuries, characterized in that, Includes the following steps: S1. Data Acquisition: The user's knee joint kinematic data, dynamic data, and physiological parameters are acquired through a multimodal sensing module. S2. Model Construction: Based on the data collected in S1, combined with the user's injury type, disease course and body characteristic parameters, a personalized knee joint biomechanical model is constructed. The model includes the force transmission characteristics of the musculoskeletal system and the mechanical parameters of the joint soft tissue. S3. Program Generation: Based on the analysis results of the personalized biomechanical model, the clinical pathways in the rehabilitation medicine database are called to generate a phased initial rehabilitation program. The program includes the type of training movement, the number of repetitions per set, the training interval, and the intensity control index. S4. Real-time motion correction: When the user performs rehabilitation training, the motion sensing module collects motion data in real time and compares it with standard motion parameters. When the deviation exceeds the preset threshold, it corrects the error in real time through a combination of tactile and auditory prompts. S5. Effect Evaluation and Program Optimization: Regularly collect post-rehabilitation data and input it into the personalized biomechanical model. Combine this with the user's subjective rating to generate a quantitative evaluation report. Dynamically adjust the training content, intensity, and cycle of the rehabilitation program through feedback adjustment algorithms.

2. The method for rehabilitation of knee joint sports injuries according to claim 1, characterized in that: The multimodal sensing module in S1 includes a flexible array pressure sensor, an inertial measurement unit, a surface electromyography sensor, an infrared temperature sensor, and a photoelectric blood oxygen sensor, wherein: The flexible array pressure sensor is attached to the medial and lateral condyles and patellar region of the knee joint, with a sampling frequency ≥50Hz; The inertial measurement unit is fixed to the mid-thigh, the outer side of the knee joint, and the mid-calf, respectively, to collect three-dimensional acceleration and angular velocity data; The surface electromyography sensor is attached to the belly of the quadriceps femoris, hamstring, and gastrocnemius muscles. The infrared temperature sensor is attached to the skin 2-3 cm above the patella of the knee joint; The photoelectric blood oxygen sensor is attached to the skin on the inside of the knee joint to collect local tissue blood oxygen saturation, with a sampling frequency ≥10Hz.

3. The method for rehabilitation of knee joint sports injuries according to claim 1, characterized in that: The construction process of the personalized biomechanical model in S2 includes: S2-1. Establish a digital model of skeletal structure based on the user's height, weight, lower limb length, and MRI image data; S2-2. The joint torque is calculated using the inverse dynamics algorithm, and the degree of muscle activation is analyzed in combination with the amplitude of surface electromyography signals. S2-3. Optimize the model parameters using the least squares method to ensure that the error between the simulated joint contact force and the measured data from the pressure sensor is ≤8%.

4. The method for rehabilitation of knee joint sports injuries according to claim 1, characterized in that: The phased rules of the initial rehabilitation program in S3 include 0-7 days as the acute inflammation phase, 2-4 weeks as the fiber repair phase, and 5-12 weeks as the functional recovery phase, wherein: During the acute inflammatory phase: focus on static joint stabilization training, with each training session lasting ≤10 minutes, twice a day, and the intensity of electromyographic activity controlled at 20%-25% of the maximum voluntary contraction. During the fiber repair period: increase progressive resistance training, with resistance increasing by 5%-8% each week, and keep joint range of motion within a pain-free range; Functional recovery period: Introduce balance and coordination training, gradually increasing the single-leg standing time from 10 seconds to 60 seconds.

5. A method for rehabilitation of knee joint sports injuries according to claim 1, characterized in that: The real-time motion correction prompts in S4 include: Auditory cues: Different frequencies of cues are emitted through wearable bone conduction headphones. For every 3° increase in the deviation angle, the frequency of the cues increases by 50Hz. Tactile feedback: The protective gear generates graded vibrations through a built-in vibration module. The varus and valgus deviations of the knee joint correspond to the medial and lateral vibrations. When the deviation exceeds 10°, the vibration intensity increases to 500Hz.

6. A knee joint sports injury rehabilitation system, used to implement the knee joint sports injury rehabilitation method as described in claims 1-5, characterized in that, include: Multimodal sensing module: used to collect knee joint kinematics, dynamics data and physiological parameters, including flexible pressure sensor, IMU, surface electromyography sensor, temperature sensor and blood oxygen sensor; Data processing module: Connected to the multimodal sensing module, it filters and fuses the collected data to build and update personalized biomechanical models; The program generation module generates phased rehabilitation training programs based on the analysis results of biomechanical models and the rehabilitation database. Motion correction module: Receives motion data in real time and compares it with standard parameters, and outputs correction prompts through auditory and tactile modules; Feedback adjustment module: Receives rehabilitation assessment data and user feedback, and adjusts the training parameters of the rehabilitation program through a preset algorithm.

7. A knee joint sports injury rehabilitation system according to claim 6, characterized in that: The multimodal sensing module is integrated into the wearable knee brace, which features a segmented design and adjustable tightness via Velcro. Sensor data is transmitted via Bluetooth Low Energy, and the battery life is ≥12 hours. The data processing module includes a local processing unit and a cloud analysis unit. The local processing unit uses an ARM Cortex-M4 processor to calculate joint range of motion and muscle activation in real time. The cloud analysis unit uses a random forest algorithm to optimize the prediction accuracy of the biomechanical model based on historical training data.

8. A knee joint sports injury rehabilitation system according to claim 6, characterized in that: The standard parameter library of the motion correction module contains three-dimensional motion parameters for 30+ common rehabilitation movements. The angle error threshold for each movement can be dynamically adjusted according to the user's rehabilitation stage. The evaluation indicators of the feedback adjustment module include objective indicators and subjective indicators. When the improvement rate of objective indicators is <8% or the subjective indicators deteriorate in two consecutive evaluations, the program adjustment is automatically triggered. The adjustment range is ±15% of the training intensity or 20%-30% of the training movements are replaced.

9. A knee joint sports injury rehabilitation system according to claim 8, characterized in that: The objective indicators include the improvement rate of joint mobility, muscle strength symmetry, and pressure distribution uniformity, while the subjective indicators include pain score and fatigue score.

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