Extracting simultaneous and proportional neural control information for multiple-DOF prostheses from the surface electromyographic signal.
A novel signal processing algorithm for the surface electromyogram (EMG) is proposed to extract simultaneous and proportional control information for multiple DOFs. The algorithm is based on a generative model for the surface EMG. The model assumes that synergistic muscles share spinal neural drives, which correspond to the intended activations of different DOFs of natural movements and are embedded within the surface EMG. A DOF-wise nonnegative matrix factorization (NMF) is developed to estimate neural control information from the multichannel surface EMG. It is shown, both by simulation and experimental studies, that the proposed algorithm is able to extract the multidimensional control information simultaneously. A direct application of the proposed method would be providing simultaneous and proportional control of multifunction myoelectric prostheses.
Jiang
Ning
N
Department of Electrical and Computer Engineering, and Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B5A3, Canada. jiangning@hst.aau.dk
Englehart
Kevin B
KB
Parker
Philip A
PA
D000328:Adult; D000465:Algorithms; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D018482:Muscle, Skeletal / Q000502:physiology; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D019736:Prostheses and Implants; D016059:Range of Motion, Articular / Q000502:physiology; D011999:Recruitment, Neurophysiological / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D014955:Wrist Joint / Q000502:physiology