Hand gesture movement classification based on dynamically structured neural network

As one of the complex biomedical signals, electromyography represents the electrical activation of the muscle providing useful information about gesture movements. A dynamically structured neural network is proposed to be used as an efficient tool to classify such movement patterns through some EMG signals generated from hand/arm movements with unknown complexity in real-time. The dynamically structured neural network is trained to establish input/output mapping while learning its optimal structure. The proposed architecture's performance is compared to various size statically structured neural network configurations. Over two folds of improvements are observed in terms of accuracy and computation time when four common hand gestures (flexion, extension, fist and rest) tested both in the training and validation data sets. Additionally, the robust performance was obtained by reducing an optimal number of EMG channels. After completing the extensive tests, the present algorithm showed the same excellent classification performance of EMG signals obtained from subjects even including those not trained before. It was concluded that the suggested algorithm offers a powerful classification solutions for hand gesture movements in real-time applications and promises to be a viable methodology for future expansions.