HMM-Based Gesture Recognition for Robot Control

Abstract

In this paper, we present a gesture recognition system for an interaction between a human being and a robot. To recognize human gesture, we use a hidden Markov model (HMM) which takes a continuous stream as an input and can automatically segments and recognizes human gestures. The proposed system is composed of three modules: a pose extractor, a gesture recognizer, and a robot controller. The pose extractor replaces an input frame by a pose symbol. In this system, a pose represents the position of user’s face and hands. Thereafter the gesture recognizer recognizes a gesture using a HMM, which performs both segmentation and recognition of the human gesture simultaneously [6]. Finally, the robot controller handles the robot as transforming the recognized gesture into robot commands. To assess the validity of the proposed system, we used the proposed recognition system as an interface to control robots, RCB-1 robot. The experimental results verify the feasibility and validity of the proposed system.