Abstract:
The feature extraction is an important step to achieve multifunctional prosthetic control based on surface electromyography (sEMG) pattern recognition. In this study, we ...Show MoreMetadata
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.
Published in: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Date of Conference: 03-07 July 2013
Date Added to IEEE Xplore: 26 September 2013
Electronic ISBN:978-1-4577-0216-7
ISSN Information:
PubMed ID: 24111043