Title :
A new feature extraction method based on autoregressive power spectrum for improving sEMG classification
Author :
Jianwei Liu ; Jiayuan He ; Xinjun Sheng ; Dingguo Zhang ; Xiangyang Zhu
Author_Institution :
Sch. of Mech. Eng., Shanghai Jiao Tong Univ., Shanghai, China
Abstract :
The feature extraction is an important step to achieve multifunctional prosthetic control based on surface electromyography (sEMG) pattern recognition. In this study, we propose a new sEMG feature extraction method which is based on autoregressive power spectrum (ARPS). An experiment with a task containing thirteen motion classes was developed to examine the effectiveness of this method. The results show that the new feature, ARPS, has better performance comparing with other two frequently used features, the time domain set (TDS) and autoregressive coefficients (ARC). The ARPS obtains the highest separability index (SI)-a metric measuring the discriminative ability of the sEMG feature. And the average classification errors of ARPS, TDS and ARC are 5.00%, 8.43% and 6.39% respectively. This suggests that the ARPS is suitable for the sEMG pattern recognition.
Keywords :
autoregressive processes; electromyography; feature extraction; medical signal processing; signal classification; ARPS; autoregressive power spectrum; multifunctional prosthetic control; sEMG classification; sEMG feature extraction method; surface electromyography pattern recognition; Biomedical engineering; Electromyography; Feature extraction; Mathematical model; Pattern recognition; Prosthetics; Wrist;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Conference_Location :
Osaka
DOI :
10.1109/EMBC.2013.6610856