Research of Cycling Phase Identification Based on Multi-channels sEMG

Abstract

Objective: The objective of this study is to identify the cycling phase by using surface electromyography(sEMG). Methods: Eight professional cyclists participated in this study. Each subject performed a 30-second all-out cycling exercise. The braking torque imposed on cycling motion was 8% of each subject's weight. sEMG of Rectus Femoris(RF), Biceps Femoris(BF), Tibialis Anterior(TA) and Gastrocnemius Lateralis(GL) were recorded during the process. Wavelet packet transformation was performed to compute the energy for the frequency range 10-50Hz. On this basis, energies were selected as feature vectors and can be the inputs and cycling phase can be the output of Elman network. After trial-and-error optimization procedure, optimal ANN Model was developed. Results: The factor of cycling phase had a significant effect on the energy for the frequency range 10-50Hz. The prediction accuracy reached to 78.1%, implying a high-precision of the model output. Conclusions: The metergasis of RF, BF, TA, GL can be well reflected by the energy for the frequency range 10-50Hz. The predicted result of ANN was very similar with the measured value, indicating that the combination of 10-50Hz frequency band energy and Artificial Neural Network is feasible in identifying cycling phase using multi-channels sEMG. The main reason of the predicted error can be explained by the time delay between video and sEMG, and the low pass filtering effect of muscles tissue's changing during cycling exercise.