Estimating Wrist Joint Angle Using Electromyography and Cepstral Coefficients

A method was developed for increasing the ac- curacy of the continuous wrist joint angle estimations for myoelectric prosthetic hands. The method considers cepstral coefficients, which efficiently represent the frequency spectrum characteristics of electromyography (EMG) signals as features. The root mean square myoelectric power (RMS) is conven- tionally used for myoelectric prosthetic control; however, the sensitivity of the RMS to angle changes is low, especially at the fine muscle contraction level. The proposed method incorporates low-order cepstral coefficients into the feature vector as well as the RMS myoelectric power. This approach increases the angle estimation accuracy through capturing the changes in the spectral shape related to the firing patterns of the motor units. The results of an angle estimation experiment involving wrist dorsiflexion and palmar flexion movements showed that adding a few cepstral coefficients substantially increased the estimation accuracy, particularly when estimating the small flexion angle range. The cepstral coefficient can be used to effectively estimate joint angles from EMG signals, contributing to the development of smoother and more intuitive myoelectric prosthetic control.