HIDDEN MARKOV MODELS FOR MODELING AND RECOGNIZING GESTURE UNDER VARIATION

Abstract

Conventional application of hidden Markov models to the task of recognizing human gesture may suffer from multiple sources of systematic variation in the sensor outputs. We present two frameworks based on hidden Markov models which are designed to model and recognize gestures that vary in systematic ways. In the first, the systematic variation is assumed to be communicative in nature, and the input gesture is assumed to belong to gesture family. The variation across the family is modeled explicitly by the parametric hidden Markov model (PHMM). In the second framework, variation in the signal is overcome by relying on online learning rather than conventional offline, batch learning.