Comparative Study of Motion Recognition with Temporal Modelling of Electromyography for Thumb and Index Finger Movements aiming for Wearable Robotic Finger Exercises

Abstract

The project aims to perform pattern recognition of thumb and index finger gestures from the Electromyography (EMG) recordings acquired by a recently introduced External Wearable device. On the basis of the selected time domain features as reviewed based on classification performance, machine learning techniques, such as K-nearest neighbour (KNN), Support Vector Machine (SVM), Discriminant Analysis etc. are compared to choose a suitable model for recognition of same and different finger movements. The recognition model obtained for a set of six hand-finger gestures shows an accuracy of 80-86% in KNN model for two Different movements of Thumb and index finger and about S2-SS% in SVM model for two same movements of index finger and thumb using single myo armband. The trained model obtained from single myo armband was also tested with data from double myo armbands. As a result, the accuracy obtained was in a range of 66-82% for various gestures. The post-analysis results are promising and competent evidence for available literature and for developing user-friendly medical devices. The purpose of analyzing the following gestures using Myo armband is to implement a suitable model for creating an intuitive human-machine interface like robotic Hand exoskeleton for rehabilitation purposes.