A novel spatiotemporal muscle activity imaging approach based on the Extended Kalman Filter.
A novel spatiotemporal muscle activity imaging (sMAI) approach has been developed using the Extended Kalman Filter (EKF) to reconstruct internal muscle activities from non-invasive multi-channel surface electromyogram (sEMG) recordings. A distributed bioelectric dipole source model is employed to describe the internal muscle activity space, and a linear relationship between the muscle activity space and the sEMG measurement space is then established. The EKF is employed to recursively solve the ill-posed inverse problem in the sMAI approach, in which the weighted minimum norm (WMN) method is utilized to calculate the initial state and a new nonlinear method is developed based on the propagating features of muscle activities to predict the recursive state. A series of computer simulations was conducted to test the performance of the proposed sMAI approach. Results show that the localization error rapidly decreases over 35% and the overlap ratio rapidly increases over 45% compared to the results achieved using the WMN method only. The present promising results demonstrate the feasibility of utilizing the proposed EKF-based sMAI approach to accurately reconstruct internal muscle activities from non-invasive sEMG recordings.
Department of Urology, University of Minnesota, Minneapolis, MN 55455, USA.
Jing
J
Wang
Yingchun
Y
Zhang
Xiangjun
X
Zhu
Ping
P
Zhou
Chenguang
C
Liu
William Z
WZ
Rymer
D003198:Computer Simulation; D004576:Electromyography; D005240:Feasibility Studies; D009132:Muscles / Q000502:physiology*