Bio-Inspired Model for Gestures Recognition through Vision-based Movement Primitives

This article addresses the issue of gesture recognition using movement primitives to obtain a bio-model that, in a close future, call be used in the robot programming through the imitation learning paradigm. Those movement primitives are extracted from consecutive images caught by a standard web cam. For robot programming by imitation, gesture recognition was identified as first phase, which requires three main aspects to be taken into consideration. These are the instantaneous movement representation, the temporal integration of related information, and the classification strategy. These three aspects are going to be developed in this article and in contrast to other works in this field; the movement extraction and its codification are inspired in the macaco's brain motion processing. The obtained model was applied then to the recognition of four different hand gestures performed by different people. The success percentage using different standard classification strategies varied between 91.42% and 97.14%. Copyright (c) 2008 CEA.