Research On Detection Method Of Exercise Muscle State Based On Bioimpedance

Muscle is an important biological tissue that supports the movement of all parts of the human body.When the body bears a large load,its structure will change accordingly,thus showing different degrees of functional state.In daily life,muscle fatigue has become a common disease,which has attracted the attention of experts and scholars in rehabilitation medicine,sports mechanics and ergonomics.Muscle fatigue is an important sports feature of muscle tissue.Usually,muscle fatigue is reversible.If it is not treated in time,it may lead to permanent injury and seriously affect people's work,exercise and daily life.Therefore,based on bioelectrical impedance measurement technology,this paper proposes a method to characterize the degree of muscle fatigue by the changes of biological impedance and capacity of fluid inside and outside muscle cells,and uses the integrated algorithm based on machine learning to predict and classify the state of moving muscle.Firstly,according to the theory of intracellular and extracellular fluid and the physiological basis of muscle fatigue,the biological impedance signals of muscle fatigue process under high-frequency excitation and low-frequency excitation are collected respectively.All signals are preprocessed by one-dimensional Gaussian filter to extract the average impedance value and complete the data collection.According to the time sequence signal characteristics after preprocessing,the qualitative relationship between intracellular and extracellular fluid impedance and capacity and muscle fatigue is given.Secondly,using the human body subsection impedance measurement model,the calculation formulas of intracellular and extracellular fluid impedance of arm muscle are given.The characteristic values of muscle impedance signals under high-frequency excitation and low-frequency excitation after Gaussian denoising were extracted,and the average impedance values of intracellular and extracellular fluid before and after muscle fatigue were calculated and analyzed.The volume of intracellular and extracellular fluid before and after muscle fatigue was calculated and qualitatively analyzed by using the improved moissl equation.The experimental results showed that after muscle fatigue,compared with the resting state,the impedance value of extracellular fluid decreased and the capacity increased;The impedance value of intracellular fluid increased and the capacity decreased.The sum of intracellular and extracellular fluid,that is,the total fluid,decreased slightly.Finally,an integrated algorithm of muscle fatigue state classification based on machine learning is proposed,and four base classifiers are selected:KNN,DT,SVM and DNN.According to the impedance signal measured in the experiment,the eigenvalues are extracted,and the dual frequency fusion data set is established.The muscle is classified into three states under static motor isometric contraction,namely S₁non fatigue state(resting state),S₂fatigue transition state(moderate fatigue)and S₃deep fatigue state(extremely fatigue but not exhausted state).The model is trained by 50%cross validation to improve the generalization ability of the model.Through static exercise fatigue experiments and data analysis,the results show that the algorithm in this paper has a high classification accuracy,which proves that the classification model can predict the muscle movement state in local fatigue.