A Wavelet-Based Method to Predict Muscle Forces From Surface Electromyography Signals in Weightlifting

Abstract

The purpose of this study was to develop a wavelet-based method to predict muscle forces from surface electromyography (EMG) signals in vivo. The weightlifting motor task was implemented as the case study. EMG signals of biceps brachii, triceps brachii and deltoid muscles were recorded when the subject carried out a standard weightlifting motor task. The wavelet-based algorithm was used to process raw EMG signals and extract features which could be input to the Hill-type muscle force models to predict muscle forces. At the same time, the musculoskeletal model of subject's weightlifting motor task was built and simulated using the Computed Muscle Control (CMC) method via a motion capture experiment. The results of CMC were compared with the muscle force predictions by the proposed method. The correlation coefficient between two results was 0.99 ( p<0.01). However, the proposed method was easier and more efficiency than the CMC method. It has potential to be used clinically to predict muscle forces in vivo.