Feature Extraction and Classification of Gestures from Myo-Electric Data Using a Neural Network Classifier

摘要

The information about intended hand gestures can be extracted by processing surface electromyography signals using non-invasive commercial off the shelf surface electromyography data acquisition devices. Surface electromyography signals have a great potential for use in multi-functional prosthetic controllers. The objective of this study is the implementation of a classifier that can be used to classify gestures from Myo-electric data obtained from the Myo-armband. This study describes in detail a method for data acquisition, feature extraction, and offline gesture classification using Artificial Neural Network. The performance is then compared with a Support Vector Machine Classifier. The proposed approach results in an accuracy greater than 94% for validation data set for classification of five distinct hand gestures. It could be concluded that this technique could be used in the human-machine interfaces with five distinct control signals including rest. A significant observation in this study was that a single artificial neural network taking inputs from all sensors simultaneously gives inferences with better accuracy compared to a system with a separate neural network for each sensor with a majority voting to decide the classification of the gesture.