EMG pattern recognition based on artificial intelligence techniques

Author

Park, Sang-Hui ; Lee, Seok-pil

Author_Institution

Dept. of Electr. Eng., Yonsei Univ., Seoul, South Korea

Volume

6

Issue

4

fYear

1998

fDate

12/1/1998 12:00:00 AM

Firstpage

400

Lastpage

405

Abstract

This paper presents an electromyographic (EMG) pattern recognition method to identify motion commands for the control of a prosthetic arm by evidence accumulation based on artificial intelligence with multiple parameters. The integral absolute value, variance, autoregressive (AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of EMG signals. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for EMG pattern recognition

Keywords

artificial intelligence; artificial limbs; electromyography; feature extraction; medical signal processing; pattern recognition; EMG pattern recognition method; adaptive cepstrum vector; artificial intelligence techniques; autoregressive model coefficients; evidence accumulation; fuzzy mapping function; integral absolute value; linear cepstrum coefficients; motion commands identification; multiple parameters; prosthetic arm control; variance; Artificial intelligence; Cepstrum; Data mining; Electromyography; Feature extraction; Motion control; Pattern recognition; Prosthetics; Signal processing; Vectors;

fLanguage

English

Journal_Title

Rehabilitation Engineering, IEEE Transactions on

Publisher

ieee

ISSN

1063-6528

Type

jour

DOI

10.1109/86.736154

Filename

736154