Real-Time Classification of Multi-Channel Forearm EMG to Recognize Hand Movements using Effective Feature Combination and LDA Classifier
Electromyography (EMG) signals acquired from surface of arms can be crucial in recognizing nature of hand gestures. The concept is used in current highly demanding fields such as controlling prosthetic limbs, diagnosing neuromuscular disorders, manipulation of robotic arm etc. The purpose of the work was to classify a set of hand motions from corresponding multi-channel surface EMG signals by developing MATLAB tools. The research focused on extracting multiple signal features and finding the appropriate combination of extracted intelligible features to get the best classification accuracy for the specific set of hand gestures. For dynamic and fast classification purpose, linear discriminant analysis (LDA) classifier was employed. Effect of feature dimensionality reduction on classification accuracy was also investigated via Principal Component Analysis (PCA) in this research. Finally, the research analyzed different electrode placements by comparing classification accuracy for each of the set of motions and proposed a simple and compact data acquisition instrumentation having less number of electrodes while maintaining high classification accuracy.
Bangladesh Journal of Medical Physics Vol.10 No.1 2017 25-39