Motion signal extraction method based on sEMG energy Gauss distribution characteristics.
- Author:
Ruihui LI
;
Zhijian FAN
;
Cuilian ZHAO
;
Linhui LUO
;
Kai WANG
- Publication Type:Journal Article
- MeSH:
Algorithms;
Electromyography;
Humans;
Motion;
Pattern Recognition, Automated;
Signal Processing, Computer-Assisted
- From:
Chinese Journal of Medical Instrumentation
2014;38(3):177-180
- CountryChina
- Language:Chinese
-
Abstract:
Motion segment and extraction from continuous signals is the premise of surface electromyography (sEMG) analysis. For the problem that sEMG energy threshold required repeated manual testing to set, this the paper established a this mathematical model of continuous actions based on Gaussian sEMG energy curve, in which the energy threshold was set according to the distribution of Gaussian signal section, and differentiated the action signals from no-action signals combined with energy comparison method. The experiment results showed the method can achieve continuous repetitive action segmentation, and compared with manual segmentation of the same signal, has a higher degree of coincidence.
