Recognizing hand-raising gestures using HMM

Abstract

Automatic attention-seeking gesture recognition is an enabling element of synchronous distance learning. Recognizing attention seeking gestures is complicated by the temporal nature of the signal that must be recognized and by the similarity between attention seeking gestures and non-attention seeking gestures. Here we describe two approaches to the recognition problem that utilize HMMs to learn the class of attention seeking gestures. An explicit approach that encodes the temporal nature of the gestures within the HMM, and an implicit approach that augments the input token sequence with temporal markers are presented. Experimental results demonstrate that the explicit approach is more accurate.