A Novel Dynamic Hand Gesture and Movement Trajectory Recognition model for Non-Touch HRI Interface

Abstract

Efficient Human Robot Interaction (HRI) interface is very much demandable for controlling the semi-autonomous robots. Hand gesture recognition is an effective form of non-touch instruction. Thus, human hand gesture recognition is mostly used technique for HRI. However, in most research, some sensor devices or marker are incorporate with the hand or a large number of hand image and hand gesture sequence is stored and process for gesture recognition in machine learning techniques, which are costly and demand complex computation. From this point of view, an efficient dynamic hand gesture and movement trajectory recognition system is proposed in this paper, which can be used in real-time fashion for effective HRI interface. In the proposed dynamic gesture recognition system, hand images and skeleton information are extracted for Kinect sensor. Hands are segmented from the video frame using a skin color segmentation model from the region of interest (ROI) around the palm position of both hands. The hand open and close states are identified by calculating the position of palm and extreme position of Figure for activating the instruction recognition. The trajectory of segmented hands and the hands open state are considered for formulating the model of gesture with respect to the selected index points of body skeleton. Finally, several gesture models are derived to recognize the instruction during temporal gesture movement. For validating the proposed model, an experimental environment is setup in experimental lab. Ten volunteers are considered and tested the proposed system for six gesture instructions. According to the experiment, the proposed system shows 94.5% average recognition accuracy for dynamic motion instruction identification.