A machine learning approach to identify hand actions from single-channel sEMG signals.

Surface Electromyographic (sEMG) signal is a prime source of information to activate prosthetic hand such that it is able to restore a few basic hand actions of amputee, making it suitable for rehabilitation. In this work, a non-invasive single channel sEMG amplifier is developed that captures sEMG signal for three typical hand actions from the lower elbow muscles of able bodied subjects and amputees. The recorded sEMG signal detrends and has frequencies other than active frequencies. The Empirical Mode Decomposition Detrending Fluctuation Analysis (EMD-DFA) is attempted to de-noise the sEMG signal. A feature vector is formed by extracting eight features in time domain, seven features each in spectral and wavelet domain. Prominent features are selected by Fuzzy Entropy Measure (FEM) to ease the computational complexity and reduce the recognition time of classification. Classification of different hand actions is attempted based on multi-class approach namely Partial Least Squares Discriminant Analysis (PLS-DA) to control the prosthetic hand. It is inferred that an accuracy of 89.72% & 84% is observed for the pointing action whereas the accuracy for closed fist is 81.2% & 79.54% while for spherical grasp it is 80.6% & 76% respectively for normal subjects and amputees. The performance of the classifier is compared with Linear Discriminant Analysis (LDA) and an improvement of 5% in mean accuracy is observed for both normal subjects and amputees. The mean accuracy for all the three different hand actions is significantly high (83.84% & 80.18%) when compared with LDA. The proposed work frame provides a fair mean accuracy in classifying the hand actions of amputees. This methodology thus appears to be useful in actuating the prosthetic hand.