摘要
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.
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.
基金
This study was supported by Natural Science Foundation of Tianjin （Grant Number 11JCZDJC16900）, Major State Basic Research Development Program of China （973 Program） （No. 2011CB711005 ）, National 863 plans projects （2009AA043001）, Ministry of Communications Research Project （2009-329-810-020 and 2009-353-312-190）, Shanghai Maritime University Research Project （20100130 and 20110019）, and Shanghai Science and Technology Committee Research Project （09DZ2250400）.
