A deep learning based approach to recognize the gestures used for controlling smart wheelchair

Abstract

Classifying the gestures for various human intelligent machines to work is a typical task. A deep learning based approach for classifying the hand gestures that are used to control the wheel chair is proposed in this work. The actual attributes of a human psyche, for example, hand signals is analyzed in this work and analyzed the developments that add to the improvement of a robotized wheelchair framework. Due to the unavailability of proper hand gesture dataset for controlling the wheel chair, an unique 2D hand gesture data set is created with five subjects in different view orientations. Gestures are created individually using both the hands. A two stream CNN architecture was proposed and the training is initiated on the developed data set. The novelty of the proposed work is tested by designing various experiments. Recognition rates were considered to analyze the efficiency of the proposed methodology. This approach allows people to interact with machines through hand postures. Further, the created dataset is trained and tested on various existing deep learning architectures to validate the novelty of the proposed architecture. An average of 85% recognition rate is achieved with the proposed two-stream architecture.