151 results about "Muscle force" patented technology

The invention relates to a multi-person motion capture real-time digital human driving method based on a skeleton-muscle interaction model, and belongs to the field of artificial intelligence and ubiquitous universe. According to the method, the key points of the human body are detected through YOLO-Pose, the positions of the shielding points are obtained through a double-stage dynamic shielding point compensation method, and the accuracy of the key points is guaranteed. And outputting the three-dimensional coordinates of the key points of the human body by adopting a three-dimensional key point depth extractor based on multi-dimensional exclusive-shared feature reasoning in combination with an MT ransPose model. A real person-skeleton model-digital person real-time mapping method is provided, three-dimensional key points are mapped to a standard human body skeleton model, and a digital person role and the skeleton model are bound through a weight mapping algorithm. Based on a skeleton-muscle interaction model, the digital person accurately reflects a real motion posture by introducing the mechanical action of a skeleton rigid body dynamic model and a muscle force line model, and the process of driving the digital person in real time through multi-person motion capture is achieved.

The invention belongs to the technical field of signal processing, and particularly relates to a lower limb motion intention recognition method and system based on electromyographic signals. The method comprises the following steps: S1, arranging electromyographic sensors on a hip key muscle group, a knee joint key muscle group and an ankle joint key muscle group in sequence, and collecting electromyographic signals; s2, performing time-frequency-space domain feature fusion and dimension reduction processing on the electromyographic signals acquired at the hip key muscle group and the knee joint key muscle group, and performing corresponding motion intention prediction by using a dynamic weight distribution method and a decoder based on an LSTM-attention mechanism; electromyographic signals collected at key muscle groups of ankle joints are processed through a muscle force distribution matrix, and meanwhile a time-varying weight adjustment strategy and impedance control are adopted for corresponding motion intention prediction; s3, according to the predicted motion intention, the system controls a direct-current brushless motor on rehabilitation training equipment, a fuzzy self-adaptive PID control method is adopted to regulate and control joint motion, and a walking posture is simulated.

The invention provides an intelligent wearable rehabilitation monitoring and evaluation device after anterior cruciate ligament injury, and relates to the technical field of exercise rehabilitation and intelligent medical monitoring, the device comprises a sensor acquisition module, a signal processing module, a compensation determination module, a difference determination module and an evaluation analysis module, a force-electricity coupling flexible sensor, an IMU and a pressure sensor, the method comprises the following steps: synchronously collecting a muscle force signal, a surface electromyogram signal, an attitude signal and a plantar pressure signal at the same position, performing dynamic filtering, confidence enhancement and personalized compensation processing, extracting a difference feature vector, inputting the difference feature vector into a muscle force prediction model, and outputting a quantitative evaluation result. The portable wearable long-term dynamic monitoring is realized, the core indexes of muscles, joints and gaits are covered in multiple dimensions, the assessment is accurate and real-time, the rehabilitation scheme can be optimized, the risk of re-injury is reduced, and the rehabilitation effect and compliance of a patient are improved.

The invention relates to a lower limb diagnosis training system, and belongs to the field of medical diagnosis and rehabilitation training. The system comprises a detection part used for collecting data information related to the lower limbs of a patient; and the control unit is used for analyzing and processing the data information so as to calculate and obtain a diagnosis result. The control unit can calculate a neural function index based on the electroneurographic signal parameters, calculate a joint activity index based on the joint activity, calculate a muscle strength index based on the muscle strength evaluation value, and further calculate an initial lower limb function comprehensive score; the initial lower limb function comprehensive score is used as an initial diagnosis result to output a control signal of a numerical range of a combination of traction forces in multiple directions, so that electromyographic signal parameters and blood flow parameters acquired by a detection part in real time in the training process of a patient can be used by a control unit for correcting the initial lower limb function comprehensive score; and obtaining a dynamic lower limb function comprehensive score for adjusting the specific numerical value of each traction force distribution.

The invention relates to the technical field of rehabilitation medical treatment, and discloses a muscle group stress analysis method and device, a terminal and a storage medium, and the method comprises the steps: respectively obtaining motion data collected by inertial measurement units arranged on the body of a user, and recognizing gait events based on the motion data corresponding to the inertial measurement units of the lower limbs, dividing the continuous motion data into a plurality of gait cycles according to the gait event; fusing the motion data acquired by each inertial measurement unit to obtain a three-dimensional posture of each preset segment of the body, and constructing a three-dimensional motion track of the spine area of the user in combination with a human kinematics chain model; processing the three-dimensional motion track and the known muscle group stress data by adopting a singular value decomposition embedding regression method to obtain a preliminary target muscle group stress curve of each gait cycle; and correcting the initial target muscle group stress curve based on a human body multi-rigid-body dynamic model in combination with a control cost function to obtain a final target muscle group stress curve.

The invention discloses a hip replacement postoperative gait dynamics prediction method based on discrete optimal control, and belongs to the field of clinical medical auxiliary prediction. The implementation method comprises the steps that kinematics data are collected through a preoperative gait motion capture experiment, muscle activation signals are collected through a surface myoelectricity system, and on this basis, a neuromuscular control strategy of a patient is obtained through a neural network; predicting the gait motion characteristics of the postoperative patient based on discrete mechanics and an optimal control method, and predicting the postoperative muscle activation degree by using the neuromuscular control strategy; by comparing the muscle activation degrees before and after the operation, the influence of the replacement operation on the muscle force change of the hip joint of the patient is investigated. According to the method, discrete optimal control and the neural network are combined, the learning characteristics of human nerve-muscle control are considered while the physical characteristics of the system are not deviated, personalized muscle control strategies better conforming to different patients can be found, and personalized prediction of postoperative gait dynamic characteristics is achieved.

The invention provides a patient rehabilitation tracking system, a rehabilitation training regulation and control method based on active perception and environment self-adaption, equipment and a medium, and the method comprises the steps: determining a cooperative motion state of a nerve-muscle-bone system according to a multi-modal physiological data flow of rehabilitation training tracking of a target patient; performing intention-muscle force cooperative identification based on the cooperative motion state to obtain motion intention and muscle force contribution distribution of the target patient; determining a dynamic risk map of the target patient during rehabilitation training; variable universe fuzzy reasoning is carried out on the motion intention, the muscle force contribution distribution and the dynamic risk map, and then a rehabilitation adjustment instruction matched with the rehabilitation state of the target patient is obtained; the rehabilitation progress of the target patient is continuously tracked, and the rehabilitation training robot is controlled to execute self-adaptive power-assisted compensation according to the rehabilitation adjustment instruction. By means of the scheme, motion intention decoding and muscle force contribution analysis can be conducted on rehabilitation training of the patient, and rehabilitation training regulation and control are conducted in combination with the dynamic environment risk.

The invention provides a muscular-bone rehabilitation posture correction method and system based on visual identification, and the method comprises the steps: firstly collecting the posture video data of the muscular-bone rehabilitation training of a rehabilitation object, and covering the joint movement, muscle contour change and body balance state change processes; analyzing the posture video data to generate joint movement tracks, muscle force application association and body balance state features, corresponding the joint movement tracks, the muscle force application association and the body balance state features to a preset muscle-bone rehabilitation stage target to establish a multi-dimensional adaptation relationship, and generating layered and staged correction guide information according to the multi-dimensional adaptation relationship, joint, muscle and balance adjustment layers are divided according to priorities, a specific adjustment mode is provided, new posture video data after adjustment is obtained, repeated operation is conducted, correction guide information is updated according to deviation changes, accurate and dynamic muscle-bone rehabilitation posture correction is achieved, and the rehabilitation effect and efficiency are improved.

A trunk-supporting exoskeleton for reducing muscle forces in a wearer's back during forward lumbar flexion comprises a supporting trunk, a first thigh link, a second thigh link, and an actuator, which includes an actuator first element and an actuator second element. When the wearer is in a forward-bent position, the actuator generates a first torque on the actuator first element and a second actuator torque on the actuator second element to generate extension torques between the first and second thigh links and the supporting trunk, thereby resisting bending motion of the supporting trunk in the forward-bent position. When the wearer is not in the forward-bent position, the actuator generates a substantially small first torque and second torque, resulting in small resistance to the movement of the thigh links relative to the supporting trunk during walking.

The invention relates to the field of upper limb joint rehabilitation training, in particular to an upper limb joint rehabilitation training method and system based on an exoskeleton mechanical arm, and the method comprises the steps: collecting sEMG signals of a target muscle group of a user in real time in a training process; calculating the root mean square of the amplitude of the sEMG signal in the current time window; if the root mean square is smaller than a muscle force insufficient threshold value, a training mode is switched to a full-assistance mode; if the root mean square is not smaller than the muscle force insufficient threshold value and smaller than the normal muscle force threshold value, the training mode is switched to a semi-assistance mode; if the root mean square is larger than or equal to the normal muscle force threshold value, the training mode is switched to an anti-resistance training mode. The rehabilitation training effect of the upper limb joints can be effectively improved.

The invention provides an elbow joint prosthesis installation position evaluation method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps that an elbow joint biomechanical model of an implanted prosthesis is constructed, and the model comprises a skeleton prosthesis model and a muscle model which are coupled, the skeleton prosthesis model comprises a humerus prosthesis equivalent mass block and an ulna prosthesis equivalent mass block; acquiring a plurality of pieces of to-be-evaluated position data of the prosthesis rotation axis, wherein each piece of to-be-evaluated position data is used for representing a to-be-evaluated position of the prosthesis rotation axis between the two prosthesis equivalent mass blocks; for each piece of to-be-evaluated position data, based on a prosthesis rotation axis corresponding to the to-be-evaluated position data, performing motion simulation calculation on the elbow joint biomechanical model implanted with the prosthesis by using a dynamics method to obtain muscle force data of reference muscles in the muscle model; and determining an evaluation result of each piece of to-be-evaluated position data according to the muscle force data corresponding to each piece of to-be-evaluated position data.

The invention discloses a pelvic floor assessment method and system combining pelvic floor myoelectricity and muscle force signals, and relates to the technical field of pelvic floor muscle assessment, and the method comprises the steps: synchronously collecting pelvic floor myoelectricity signals and pressure signals, and recording data of a pre-resting stage, a rapid contraction stage, a tension contraction stage, an endurance contraction stage and a post-resting stage; the pelvic floor electromyographic signals and the pressure signals are preprocessed, and statistical characteristics are extracted based on the preprocessed signals; constructing a pelvic floor evaluation model, wherein the pelvic floor evaluation model comprises a logistic regression model, a long-short term memory network based on a cross attention mechanism and a multi-layer perceptron classifier; and training the pelvic floor evaluation model by using an Adam optimizer and a cross entropy loss function, and optimizing parameters of the pelvic floor evaluation model through evaluation indexes. According to the pelvic floor evaluation model, the timing sequence features and the statistical features are combined, more comprehensive signal interpretation is achieved, the generalization ability of the pelvic floor evaluation model is improved, and the pelvic floor health condition can be recognized more accurately.

The invention relates to the technical field of disease rehabilitation assessment, and discloses a functional behavior-based assessment and rehabilitation system for lower limb artery occlusion, which is characterized in that gait data, muscle force data, blood flow change data and electromyographic signal data of a patient are acquired by arranging a data acquisition module, and a rehabilitation assessment report of the patient is generated by using a multi-modal fusion algorithm; and the artery occlusion degree, the lower limb blood circulation condition and the motion function state of the patient are comprehensively evaluated. On the basis, the system generates a personalized exercise scheme through a reinforcement learning algorithm, and dynamically adjusts the exercise scheme according to the real-time exercise data feedback of the patient, so as to ensure the effectiveness and safety of the rehabilitation scheme. Meanwhile, the system provides a guide feedback module, the patient is guided to complete the correct motion posture through voice, vision or vibration prompts, and secondary injury caused by improper motion posture is avoided. By dynamically optimizing the exercise scheme and guiding feedback in real time, personalized rehabilitation guidance of the patient is achieved, and the rehabilitation effect and exercise compliance are improved.

The invention relates to a gait monitoring algorithm which is used for a traditional four-foot walking aid, a six-axis gyroscope is installed in the middle of four feet of the walking aid, pressure sensor data is installed at the bottom of a handle and the bottoms of the four feet, and the gait monitoring algorithm comprises a sensor data acquisition module used for acquiring original sensor data related to gaits of a user, acquiring three-axis acceleration information; acquiring triaxial angular velocity information according to gyroscope sensor data; acquiring handle pressure and ground contact pressure information according to data of a pressure sensor; the gait feature extraction system is used for extracting gait features from original sensor data and comprises a gait feature extraction module used for extracting gait feature values from the original sensor data, the gait feature values comprise stride frequency, and the number of steps per second is calculated by analyzing periodic fluctuation of pressure or acceleration; and calculating the distance between the two steps through acceleration integration or the ground contact interval between the left foot and the right foot.

The invention provides a rehabilitation training feedback system and method. The method comprises the following steps: acquiring a surface electromyogram signal; a plurality of activation trend indexes are determined through the surface electromyogram signals, and then muscle force activation points of different muscle areas are extracted from the surface electromyogram signals according to all the activation trend indexes; performing collaborative analysis on the basis of a function collaborative relationship in combination with all muscle force activation points to obtain time sequence response tensors between all motion joints and corresponding muscle areas when motion responses are inconsistent; according to the joint angle information and the time sequence response tensor, constructing a functional coupling graph, and further performing kinematic chain simulation on a rehabilitation training process through the functional coupling graph to obtain a response consistency index; and performing feedback control on the rehabilitation training process of the patient according to the response consistency index in combination with a preset consistency threshold. By means of the scheme, feedback control can be conducted on rehabilitation training of the patient based on the space-time coupling relation between muscle group microstructure changes and mechanical conduction characteristics.

The invention relates to a data processing method and device, equipment and a medium, and the method comprises the steps: obtaining a plurality of closed movement tracks of the lower jaw of a user; acquiring a plurality of intersection points between each of a plurality of preset planes and the plurality of closed motion tracks; for each preset plane, determining a density highest point from the plurality of intersection points; and connecting the plurality of density highest points corresponding to the plurality of preset planes to obtain the muscle force closed channel. According to the technical scheme, the muscle force closed channel can be accurately obtained, the operation process is simple and convenient, and contact type measurement is not needed.

The invention provides an upper respiratory tract muscle group mechanical property measuring and evaluating method based on signal characteristics. Firstly, a force signal of an upper respiratory tract muscle group is collected through a sensor; after the signals are subjected to voltage amplification filtering and digital processing, key characteristic variables including the maximum muscle force, the muscle force stability, the total acting amount, the fatigue index and the overall testing time are generated. In order to improve the evaluation accuracy, a multi-level machine learning model is introduced, and the multi-level machine learning model comprises a basic TabTransform structure, a contrast learning framework and a meta-learning technology so as to realize deep analysis of a feature complex interaction relationship. In the model training process, through comparative learning of unlabeled data, the feature representation capability is enhanced; an MAML framework is adopted to quickly adapt to a small amount of labeled data, and a comprehensive target of disease grading and curative effect prediction is taken into account by utilizing a multi-task learning strategy. Finally, the performance index of the model on the verification set is remarkably improved, and efficient evaluation and rapid deployment can be achieved in practical application.

The invention provides a muscle force estimation method based on a long and short term memory network in combination with an attention mechanism, and aims to solve the limitation of a traditional muscle force estimation method.The method comprises the steps that firstly, multi-channel surface electromyographic signals and force data are collected through an electromyographic sensor and a force sensor, and wavelet denoising filtering processing is carried out; then, a channel attention mechanism is applied to allocate a weight for each channel, and key features are highlighted. And inputting the processed data into a long-short-term memory model, and capturing the dynamic change in the time sequence data by using the memory ability of the long-short-term memory model. In order to further optimize the model performance, residual connection and a self-attention mechanism are introduced, so that the model pays more attention to own information. Then, a mixed attention mechanism module is constructed, and the feature representation capability is enhanced by combining attention of channels and spatial dimensions; and finally, training results of a plurality of subjects are fused, and the generalization ability of the model is improved. According to the method, the prediction capability of the muscle force change trend is enhanced, and the method has excellent accuracy.

The invention discloses a muscle force estimation method based on surface electromyogram signals and adjustable input characteristics. The method comprises the steps that the surface electromyogram signals and force signals in a muscle contraction state are synchronously collected; the EMD-DWT method is adopted to preprocess the surface electromyogram signals; segmenting the surface electromyogram signals and the force signals by adopting a sliding window, extracting features of the surface electromyogram signals, extracting an average value of the force signals, and making sample labels; a Pearson's correlation coefficient method is adopted to calculate correlation coefficients of the extracted features and the force signals; sequentially adding features with high correlation coefficients to form an input vector of the model, constructing a muscle force estimation regression model for training and testing, and adjusting the number of input features according to errors of a training set and a testing set. According to the method, effective features can be accurately and rapidly extracted, the number of the features can be determined, and it is guaranteed that high muscle force estimation precision is achieved under the condition that the input dimension is low.

The invention discloses an upper limb exoskeleton on-demand auxiliary intelligent control method based on multi-index optimization and multi-mode, and the method employs an inner and outer ring force position mixed control structure based on an admittance model, an outer ring combines the three indexes of a task tracking error, patient muscle force and a rehabilitation training task period, the exercise ability of a patient is evaluated on the basis of a multi-index energy function, dynamic switching among a passive mode, an auxiliary mode and an anti-resistance mode of the upper limb exoskeleton co-fusion system is achieved, online self-adaptive adjustment of the auxiliary force level and the rehabilitation training track is achieved on the basis of fuzzy logic, and the method can adapt to rehabilitation patients with different exercise abilities. And the inner ring carries out trajectory tracking control on the ideal tracking trajectory position of the upper limb exoskeleton system obtained by the admittance model through a model-barrier-free Lyapunov control method based on quasi-time delay estimation. A simulation result verifies that the method can effectively improve the active participation degree of the patient in the rehabilitation training process, and shows good on-demand auxiliary ability and application prospects.

The invention relates to the technical field of health assessment, in particular to an orthopedic rehabilitation process real-time monitoring and assessment method, which comprises the following steps of: based on gait biomechanical parameters, identifying feature points, optimizing nodes in combination with body weight, stride frequency and stride change, extracting node correction parameter groups, analyzing muscle force change of knee joints and ankle joints, and evaluating the rehabilitation process of the orthopedics department. And combining the gait stability and the joint movement to obtain a correlation index proportion sequence. Parameters such as stride frequency, stride and body weight are combined with automatic recognition of feature points in a gait cycle, the time sequence sensitivity and individual adaptability of data are enhanced by dynamically correcting a feature point judgment standard, muscle force recovery and gait stability indexes are synchronously integrated and quantified in different stages, and the accuracy and the accuracy of gait recognition are improved. The linkage relation between the indexes can be adjusted according to real-time data, accurate tracking of a function recovery path is further supported, and the rehabilitation progress can be reflected by a dynamic structure of multi-dimensional parameters.

The invention relates to the technical field of orthopedic rehabilitation management, in particular to a self-adaptive orthopedic rehabilitation management system, which comprises a training module, an acquisition module, an evaluation module and an adjustment module, and is characterized in that the training module is used for assisting a user to execute training actions; the acquisition module is used for respectively acquiring the actuating muscle force, the actuating range and the breathing frequency of the user for executing the training action; the evaluation module is configured to generate an evaluation result of the training action process executed by the user on the basis of the difference between the action range and the standard action range, the difference between the action muscle force and the standard muscle force and the respiratory rate; the adjusting module is configured to correct the training level of the next training action based on the evaluation result, or output a rehabilitation prompt; through comprehensive analysis of the muscle force and the actuation range, the rehabilitation degree of the orthopedics department can be effectively evaluated, then the rehabilitation training intensity is accurately and adaptively adjusted, and the adaptability and timeliness of the rehabilitation management system for the orthopedics department are effectively improved.

The invention relates to the technical field of medical instruments, in particular to a magnetic control pulse type pelvic floor nerve muscle stimulator and a parameter optimization system thereof. Comprising a host, a dynamic layered pulse waveform generation technology module, a multi-target reinforcement learning parameter optimization module, a multi-modal biofeedback fusion and VR enhancement module, and a flexible wearable stimulation array and ultrasonic targeting module. The system has the advantages that real-time data and deep learning are achieved, and personalized precise treatment is achieved, in the actual use process, the dynamic layered pulse waveform generation technology module predicts the requirements of muscles in the next stage through a deep learning model on the basis of pelvic floor electromyographic signals, pressure data and infrared thermal imaging which are collected in real time, and the treatment accuracy is improved. Dynamically generating a composite pulse waveform; the multi-objective reinforcement learning parameter optimization module constructs a'state-action-reward 'reinforcement learning framework, and takes patient treatment feedback, muscle force improvement, pain score and treatment compliance as reward signals.

The invention discloses a muscle force calculation method and system driven by multi-source data in the technical field of biomechanics, and the method comprises the steps: employing a motion capture system to collect first training data when a subject carries out target motion, and synchronously collecting second training data corresponding to the first training data, the first training data is kinematics data of the subject, and the second training data is dynamics data of the subject; and performing noise reduction processing on the first training data and the second training data to obtain first processing data and second processing data. According to the method, the target model is constructed by collecting the kinematics data and the dynamics data of the subject, rapid calculation of the muscle strength in the movement process of the user is achieved through the target model, the calculation speed of the muscle strength can be effectively increased, the dynamics modeling and data iteration steps are reduced, computing power resource consumption of computer equipment can be reduced, and the user experience is improved. The reasoning calculation time of the target data is effectively shortened, and timely feedback of the target data is facilitated.

The LNTPI is a surgical instrument used to cut through fibrotic and edematous tissue to define the natural tissue plane of dissection. After defining the natural tissue plane, it is designed to move or carefully insinuate tissues along these natural planes without cutting the tissue, so that targeted organs or parts thereof could be viewed and reached during laparoscopic procedures. The following features make the LNTPI unique compared to standard blunt dissectors used in laparoscopy: 1. The tip of the LNTPI is coarse and not smooth. The tip has diagonal, spiral ridges from the sides which are carried onto the tip converging as a single spiral at the center of the tip, creating a pattern resembling that of a screw; 2. The ridges of the LNTPI are comprised of asymmetrical triangular ridges with an acute angle at the apex that may be leaning along with or against the rotation of the spiral; 3. The tip of the LNTPI is made of tungsten carbide metal instead of stainless steel; 4. Dissection can be done in the usual up, down, left, and right motions as standard dissectors; 5. The side of the LNTPI can be used to "peel" tissue by using the ridges on its side combined with a twisting movement. This maneuver cannot be performed using standard dissectors; and 6. The tip of the LNTPI can perform circumferential dissection when used with a twisting movement causing it to engage tissue like a "drill" along the natural tissue plane. The ridges that converge into a single spiral at the tip of the invention appear like a screw and with the proper twisting movement have a similar effect on tissue. Because of the texture, design, and material used for the tip of the LNTPI, it has the following advantages over standard dissectors: The LNTPI can engage tissues more easily even if the tissues are slippery due to the presence of blood or fluids. The LNTPI can be used to engage tissues through a twisting motion which motion allows the tissues to be engaged in a specific manner that defines natural tissue dissection planes. The LNTPI can insinuate / "peel" tissue by using the side spiral ridges in combination with a twisting movement. The LNTPI can perform circumferential dissection by using the tip and the convergence of the spiral ridges at the center of the tip in combination with a twisting movement. The transfer of lateral shear force to the tissue at various angles (45, 90, and 180 degrees) when using the LNTPI is significantly higher whether the motion is by a sweeping motion or twisting of the instrument, allowing it to insinuate better than standard instruments, especially in a wet field. Hand movements needed in insinuating tissue using the LNTPI are different from the usual hand movements when using standard dissection instruments. The hand movements in using LNTPI require less muscular effort and repetitiveness, thus preventing muscle fatigue and repetitive use injury among surgeons. The instrument is reusable, as compared to dissectors that have fabric at the tip, which must be disposed of and replaced once the fabric tip fully absorbs the blood or other fluid in the wet tissue surface.

The invention discloses an auxiliary rehabilitation method for a stroke patient and related equipment. The method comprises the following steps: detecting physical sign information of a hand of a patient, wherein the physical sign information comprises joint motion range information, muscle force information and muscular tension information; matching training music for the patient based on the physical sign information of the hand of the patient; a training instruction is generated according to the rhythm of the training music so as to assist rehabilitation, and the training instruction is used for indicating the hand movement content. The problems that in an existing rehabilitation device combining music and gloves, a single instruction or the combination of single music rhythm and movement is commonly used, synchronous stimulation of an audio-visual channel is lacked, and the oneness and limitation of the training process are ignored can be solved.

The invention relates to the field of medicine, and particularly provides a method for treating patellofemoral joint pain syndrome (PFPS) through combination of neuromuscular electrical stimulation (NMES) and exercise training. According to the method, NMES is adopted to stimulate quadriceps femoris muscles, and isometric and isotonic training and core stability training are combined, so that the stability and muscle force of knee joints are improved, pain of patients is relieved, and dysfunction is improved. The invention provides a non-invasive treatment means, has high clinical application value, and is suitable for sports injury rehabilitation and orthopedic rehabilitation treatment.

The utility model belongs to the technical field of medical supplies, and particularly relates to an electric anorectum training device which comprises a first mounting shell, a mounting inner bin is embedded in the first mounting shell, a heating pipe and a refrigerating pipe are fixedly mounted in the mounting inner bin through a connecting frame, and a heat conducting plate is fixedly connected to the inner wall of the first mounting shell. A second mounting shell is arranged at the bottom of the first mounting shell, connecting seats are mounted at the two ends of the second mounting shell, and the top of the second mounting shell is fixedly connected with the bottom of the first mounting shell through one of the connecting seats; on one hand, cold and hot stimulation can be performed on the anal sphincter of a patient to facilitate the recovery of the defecation function of the patient, and on the other hand, the muscle force and muscular tension of the anal sphincter of the patient can be monitored in real time to facilitate medical staff to more accurately master data of the affected part of the patient, so that the medical staff can accurately know the data of the affected part of the patient. And medical staff can be helped to make a relatively efficient treatment plan.

This invention relates to the interdisciplinary field of image recognition and intelligent medical technology, specifically disclosing a method for orthopedic rehabilitation movement recognition and assessment based on image recognition. This method acquires patient motion images using a non-invasive multi-view visual sensor, extracts the three-dimensional coordinates of key skeletal points, and constructs a parametric biomechanical digital twin model integrating joint range of motion, muscle force lines, and ligament tension mechanisms. The key point data is input into a graph neural network for posture recognition, and the digital twin model is simultaneously invoked for physical feasibility verification and posture correction. Finally, the spatiotemporal trajectory similarity is calculated using a standard rehabilitation movement template, generating a comprehensive assessment result including movement completion, safety level, and injury risk. This invention, through the above technical solution, improves the robustness, physiological rationality, and clinical safety of movement recognition.

The invention discloses a digitization and flexible pressure sensor combined thigh prosthetic socket optimization manufacturing method. Performing digital reconstruction on the thigh stump physical model; performing space calibration and size calibration processing on the reconstructed stump structural plane digital model; carrying out sparse processing on the stump structural plane digital model after space calibration and size calibration, and editing and optimizing the stump structural plane digital model; splicing the sparse point cloud digital model and the digital point cloud model of the hat brim and the seat support to form a thigh artificial limb digital model; a flexible pressure sensor is arranged for pressure analysis and adjustment of the thickness of a local segment, a prosthetic socket digital model with double-sided sparse point clouds formed on the outer layer surface is generated, and finally conversion is performed for 3D printing. According to the invention, the flexible sensing unit corresponds to the point cloud unit on the digital skin, real-time change evaluation of muscle stress under static and dynamic conditions is carried out, the physiological change of a patient can be rapidly adapted during re-examination, and a more personalized and timely rehabilitation solution is provided.