Research on exercise fatigue estimation method of Pilates rehabilitation based on ECG and sEMG feature fusion

Abstract

Purpose Surface electromyography (sEMG) is vulnerable to environmental interference, low recognition rate and poor stability. Electrocardiogram (ECG) signals with rich information were introduced into sEMG to improve the recognition rate of fatigue assessment in the process of rehabilitation. Methods Twenty subjects performed 150 min of Pilates rehabilitation exercise. Twenty subjects performed 150 min of Pilates rehabilitation exercise. ECG and sEMG signals were collected at the same time. Aftering necessary preprocessing, the classification model of improved particle swarm optimization support vector machine base on sEMG and ECG data fusion was established to identify three different fatigue states (Relaxed, Transition, Tired). The model effects of different classification algorithms (BPNN, KNN, LDA) and different fused data types were compared. Results IPSO-SVM had obvious advantages in the classification effect of sEMG and ECG signals, the average recognition rate was 87.83%. The recognition rates of sEMG and ECG fusion feature classification models were 94.25%, 92.25%, 94.25%. The recognition accuracy and model performance was significantly improved. Conclusion The sEMG and ECG signal after feature fusion form a complementary mechanism. At the same time, IPOS-SVM can accurately detect the fatigue state in the process of Pilates rehabilitation. On the same model, the recognition effect of fusion of sEMG and ECG(Relaxed: 98.75%, Transition:92.25%, Tired:94.25%) is better than that of only using sEMG signal or ECGsignal. This study establishes technical support for establishing relevant man–machine devices and improving the safety of Pilates rehabilitation.

A New Wrist–Forearm Rehabilitation Protocol Integrating Human Biomechanics and SVM-Based Machine Learning for Muscle Fatigue Estimation

“…Although muscle fatigue estimation during rehabilitation training was already studied by several recent works [48][49][50], the integration of fatigue results in the main controller being challenged. Indeed, in most cases when muscle fatigue is sensed, the defined rehabilitation protocol consists of manually stopping or at least changing the setup of the rehabilitation exercise in order to protect the patient.…”

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

“…The results of ECG and sEMG model based on D-S evidence theory show that TCN has better classification performance than KNN and SVM, and the classification accuracy, recall rate and F-score are improved, and the lowest recognition accuracy of individuals is also higher than those of the two methods. The average precision of TCN (90.90%) and the average accuracy (88.0%) of TCN was also higher than that of previous studies [ 25 ] This provides a new idea for the integration of ECG and sEMG. Future studies will explore the effects of improved TCN or receptive field and model depth on recognition accuracy.…”

Simulation of muscle activity in sports training based on optical coherence tomography technology

“…By performing NMF decomposition on the feature matrices of A-type ultrasound signals and surface electromyography signals, the literature obtained the fusion results of the two signal features [16]. The literature has conducted feature fusion analysis on the activity patterns of core muscle groups [17]. By analyzing the fused features, they can understand the activity patterns of the core muscle group, as well as the relationship between muscle thickness and surface electromyographic signals.…”