3D hand gesture tracking and recognition for controlling an intelligent wheelchair

Abstract

Hand gesture recognition is a user-friendly and intuitive means for human machine interaction. This paper proposes a novel 3D hand gesture recognition method for controlling an intelligent wheelchair based on both colour and depth information. Image depth information of human palm is obtained by a 3D Kinect vision sensor and then its position is obtained through the hand analysis module in OpenNI. The improved Centroid Distance Function is used to extract 3D hand trajectory features, while hidden Markov model HMM is applied to train samples and recognise hand gesture trajectories. Finally, the recognition results are converted into control commands through an ad hoc network and sent to an intelligent wheelchair for its motion control. Experiment results show that the proposed method has good invariance to lighting changes, hand rotation and scaling conditions and is very robust to background interference.