Study on exercise muscle fatigue based on sEMG and ECG data fusion and temporal convolutional network

Abstract

Background Muscle fatigue is a crucial indicator to determine whether training is in place and to protect trainers. Purpose To make full use of morphological information of surface EMG and ECG signals in the time domain, a new idea and method for the fatigue assessment of exercise muscles based on data fusion is proposed in this paper. Methods sEMG and ECG time series with the same length were obtained by signal preprocessing and sequence normalization, feature extraction of sequence tenses was realized by a deep learning network based on sequential convolution and signal fusion model of muscle fatigue evaluation was established by D-S evidence theory. Experiment Thirty volunteers were recruited and divided into three groups. ECG signals and sEMG signals at the biceps brachii of the right upper limb were monitored in a 20-minute exercise cycle. Results The prediction result of TCN based on time domain signal is better than the commonly used KNN and SVM recognition algorithm, and the recognition accuracy of relaxed, excessive and fatigue by D-S fusion was 89%, 86%, 88.5%. The accuracy was 0.9055, 0.9494 and 0.9269, respectively. The recall rates of the three conditions were 0.9303, 0.9570 and 0.9435. The F-score of the three conditions was 0.8911, 0.8764 and 0.8837, respectively. Conclusion Based on time series and time series convolutional network, sEMG and ECG fusion of motor muscle recognition method can better distinguish different state information and has certain practical value in the fields of muscle evaluation, clinical diagnosis, wearable devices and so on.

Research on fatigue characteristics of static muscle contraction based on surface electromyography

“…In recent years, surface electromyography analysis mainly focuses on the time domain, frequency domain and time-frequency domain. Mu et al [8] utilized a deep learning network founded on sequential convolution to extract features, incorporating the D-S evidence theory to assess muscle fatigue. Wang et al [9] established a mathematical model to assess muscle fatigue by monitoring and analyzing the time-frequency domain variations of EMG signals in subjects during mechanical operations.…”

Classifying static muscle fatigue: an surface electromyography signal analysis approach

“…Studies have effectively identified states of muscle fatigue from sEMG signals by constructing machine learning models such as Support Vector Machines (SVM) 8 and K-Nearest neighbors Algorithms (KNN) 9 . Moreover, deep learning techniques like Convolutional Neural Networks (CNN) 10 , Recurrent Neural Networks (RNN) 11 , and Long Short-Term Memory networks (LSTM) 12 have been utilized to automatically extract deep features from sEMG signals, thereby enhancing the accuracy of muscle fatigue classification. These studies highlight the tremendous potential of advanced algorithms in conducting in-depth analyses of sEMG signals for muscle fatigue assessment.…”

A Wireless, High-Quality, Soft and Portable Wrist-Worn System for sEMG Signal Detection

“…Meanwhile, research on the use of sEMG signals for gesture recognition [ 14 , 15 , 16 ] and physiological health status monitoring [ 17 , 18 , 19 , 20 ] has gained increasing attention in the fields of human–computer interaction and biomedical engineering. Prof. Sheng et al developed a real-time gesture recognition wristband that combined sEMG and inertial measurement unit sensors located at the wrist instead of at the forearm, which was more in line with human-wearing habits.…”