EMG pattern recognition based on artificial intelligence techniques.
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.
Park
S H
SH
Department of Electrical Engineering, Yonsei University, Seoul, Korea.
Lee
S P
SP
D001132:Arm; D001185:Artificial Intelligence; D001186:Artificial Limbs; D004576:Electromyography; D017143:Fuzzy Logic; D006801:Humans; D010363:Pattern Recognition, Automated