A comparative analysis of various EMG pattern recognition methods.

ABSTRACT

Identification of motions of the neck and shoulders using the electromyographic (EMG) signal was investigated in this study. Three discrimination methods, the Euclidean distance measure (EDM), the weighted distance measure (WDM) and the modified maximum likelihood method (MMLM), were used to compare the conventional autoregressive (AR) and cepstral coefficients with closely positioned (C-type) and separately located (S-type) electrode arrangements. Surface electrodes were bilaterally located on and between the sternocleidomastoid and the upper trapezius muscles. The EMG signals obtained during 20 repetitions of 10 motions were analyzed for each subject. Results from nine subjects showed that the mean recognition rate of the cepstral coefficients was at least 5% better than that of the AR coefficients for the S-type signals, while the improvement was less obvious for the C-type signals. The MMLM obtained the best discrimination results of the three discrimination methods. The S-type signals achieved higher recognition rates than the C-type signals in most cases. Among the various combinations of feature sets, classifiers and electrode arrangements proposed in this study, the combination of the cepstral coefficients and the MMLM with the S-type arrangement achieved the best discrimination efficiency. The proper choice of five of 10 motions raised the recognition rate to more than 97%.