Real-Time Segmentation and Feature Extraction of Electromyography: Towards Control of a Prosthetic Hand

Abstract

Surface electromyographic (sEMG) signals can be used as inputs to control a myoelectric prosthetic hand. This requires the discrimination of sEMG associated with different hand movements. The signals for key unilateral hand movements such as wrist extension, wrist flexion, and power grip are similar, making the classification and control of these hand movements challenging. This preliminary study explores the viability of classifying the sEMG signals of these hand movements in real-time, in order to control a software simulation of a prosthetic hand. sEMG data was recorded from two bipolar electrodes for offline classifier training purposes. A novel segmentation technique was used to separate the muscle contraction and rest periods of the sEMG time-series data. A time-frequency algorithm was then applied for the first time to extract sEMG-based features from the segmented data. Features were used to train four support vector machines offline, in a one-versus-all architecture. The classification system was tested offline and in real-time. The system yielded accuracies of 89.39% and 84.93% for offline and real-time testing respectively. Real-time classification was done in 10.2 ms when processing 0.5 s of input sEMG data. This shows that the system is accurate and computationally efficient, even with limited electrodes. Thus, it can serve as a foundation for further work in the implemented segmentation and feature extraction methods, for an inexpensive alternative to commercial myoelectric prosthetic devices.