| Researches on the reorganization of human motor function and neuroscience prove that robot-assisted rehabilitation training can achieve the remodeling of motor and nerve functions.Studying accurate quantitative evaluation methods of motor function and muscle fatigue algorithms can reduce the subjectivity of rehabilitation physicians in evaluating rehabilitation effects,reduce the risk of auxiliary rehabilitation training,and ensure that patients can carry out safe and effective rehabilitation training to achieve reorganization of sports functions.Based on the human upper limb movement mechanism,this paper analyzes the source of power for movement,uses muscle force as a quantitative evaluation index of motor function,studies the relationship between sEMG,exercise data and muscle force,and constructs an elbow joint that combines sEMG,joint angle and expected muscle force.Muscle force prediction model provides accurate muscle force parameters for real-time monitoring of motor function reorganization status,and the accuracy and reliability of the prediction model are verified by error analysis.At the same time,to study the anti-interference ability of five muscle fatigue algorithms and to distinguish the degree of fatigue,the aim is to provide a more effective muscle fatigue evaluation algorithm for elder patients with hemiplegia to carry out long-term repetitive rehabilitation training.The research content and results of this article are as follows:This article analyzes the influencing factors of muscle structure and muscle force,and studies the relationship between sEMG and muscle activation,and the relationship between joint angle and muscle contraction The advantages and disadvantages of the muscle force prediction method based on biomechanics and sEMG are systematically discussed.The construction and solution method of the muscle force prediction model combining sEMG and joint angle is explained.By comparing with traditional muscle force prediction method,the advantages and correction methods of this model are analyzed.Aiming at the needs of building the musculoskeletal model of elbow joint,through in-depth analysis of the physiological structure parameters of upper limb,the OpenS im musculoskeletal simulation modeling method was systematically studied.The personalized musculoskeletal model of elbow joint was built with the help of Arm.26.At the same time,the reverse dynamics analysis of the elbow joint is carried out to solve the simulated muscle force of the motion The experimental results can be used to provide the expected muscle force required by the prediction model.In order to further explore the accuracy of the model,a prediction model of elbow joint muscle force was constructed by combining sEMG,joint Angle and expected muscle force.sEMG and motion data of elbow flexion and extension were collected synchronously,and model parameters such as muscle activation degree,joint Angle and expected muscle force were calculated.Muscle force was optimized and predicted with the help of the constructed model.The results of error analysis show that the model can greatly reduce the prediction error and provide accurate muscle force index for real-time motion function evaluation of elbow joint.In order to provide a real-time high quality muscle fatigue detection algorithm for elbow joint assisted rehabilitation training,for example,calculate the five indicators of muscle fatigue algorithm,linear regression analysis of various index carried out inspection and K-S,in order to determine the regression equation of coefficient(R2)on behalf of its anti-jamming,two index K-S inspection of the maximum vertical distance(Lmax)represents its ability to distinguish between the level of fatigue.The results showed that the spectrum distance(SMR)haves strong anti-interference ability and the ability to distinguish fatigue degree under different fatigue states,which provided more effective real-time muscle fatigue detection algorithm to carry out auxiliary rehabilitation training for patients with impaired elbow joint motor function.Finally,this paper summarizes the whole paper,points out the shortcomings of the research work in this paper,and looks forward to the research direction of elbow joint muscle force prediction and muscle fatigue algorithm. |
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