Classification of multifunction surface EMG using advanced AR model representations

Author

Knox, R. ; Brooks, D.H.

Author_Institution

CDSP Center, Northeastern Univ., Boston, MA, USA

fYear

1994

fDate

17-18 Mar 1994

Firstpage

96

Lastpage

98

Abstract

The potential use of new features derived from autoregressive coefficients for upper limb EMG pattern recognition has been discussed (Knox et al., 15^th Ann. Int. Conf. of the IEEE EMBS, San Diego, CA, USA, Oct. 1993). These new features (reflection coefficients, cepstral coefficients, and logarithmic area ratios) are used heavily in speech processing. Results from a nonparametric linear classifier are presented

Keywords

bioelectric potentials; medical signal processing; muscle; physiological models; stochastic processes; time series; advanced autoregressive model representations; autoregressive coefficients; cepstral coefficient; electromyography; logarithmic area ratio; multifunction surface EMG classification; nonparametric linear classifier; reflection coefficient; upper limb EMG pattern recognition; Cepstral analysis; Electromyography; Filtering; Muscles; Pattern recognition; Predictive models; Prosthetics; Reflection; Speech processing; Surface treatment;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioengineering Conference, 1994., Proceedings of the 1994 20th Annual Northeast

Conference_Location

Springfield, MA

Print_ISBN

0-7803-1930-3

Type

conf

DOI

10.1109/NEBC.1994.305164

Filename

305164