Recognition of hand gestures with 3D, nonlinear arm movement

Abstract

Hand gesture is a useful modality of human interaction, In this paper, we propose an approach to recognition of space-time variable patterns of nonlinear arm movement and integration with other attributes to find the proper interpretation. At the encoding stage, we first extract the essential 2D trajectory from 3D arm movement by a plane fitting method. Pause information between the consecutive gestures is also modeled and integrated into the encoding. Codified information is then applied is a hidden Markov model (HMM) network which is responsible for segmentation and recognition of continuous arm movements. As a whole, three major attributes of hand gestures are processed in parallel and independently, followed by the inter-attribute communication for finding the proper interpretation. (C) 1997 Elsevier Science B.V.