Single channel surface EMG control of advanced prosthetic hands: A simple, low cost and efficient approach

Abstract

Surface EMGs have been the primary sources for control of prosthetic hands due to their comfort and naturalness. The recent advances in the development of the prosthetic hands with many degrees of freedom and many actuators, requires many EMG channels to take the full advantage of the complex prosthetic terminals. Some EMG wearable devices were developed lately, that are able to detect several gestures. However, the main drawbacks of these systems are the cost, the size and the system complexity. In this paper, we suggest a simple, fast and low-cost system which can recognize up to 4 gestures with a single channel surface EMG signal. Gestures include hand closing, hand opening, wrist flexion and double wrist flexion. These gestures can be used to control a prosthetic terminal based on predefined grasp postures. We show that by using a high-dimensional feature space, together with a support vector machine algorithm, it is possible to classify these four gestures. Overall, the system showed satisfactory results in terms of classification accuracy, real time gesture recognition, and tolerance to hand movements through integration of a lock gesture. Calibration took only 30 seconds and session independence was demonstrated by high classification accuracy on different test sessions without repeating the calibration. As a case study we use this system to control a previously developed soft prosthetic hand. This is particularly interesting because we show that a simple hardware that has only a single channel EMG, can afford the control of a multi-DOF prosthetic hands. In addition, such system may be used as a general purpose Human Machine Interface for gaming,for controlling multimedia devices, or to control robots.