SVM-based Real-Time Classification of Prosthetic Fingers using Myo Armband-acquired Electromyography Data

In this work we applied real-time classification of prosthetic fingers movements using surface electromyography (sEMG) data. We employed support vector machine (SVM) for classification of fingers movements. SVM has some benefits over other classification techniques e.g. 1) it avoids overfitting, 2) handles nonlinear data efficiently and 3) it is stable. SVM is employed on Raspberry pi which is a low-cost, credit-card sized computer with high processing power. Moreover, it supports Python which makes it easy to build projects and it has multiple interfaces available. In this paper, our aim is to perform classification of prosthetic hand relative to human fingers. To assess the performance of our framework we tested it on ten healthy subjects. Our framework was able to achieve mean classification accuracy of 78%.