Recognition and Reproduction of Gestures using a Probabilistic Framework combining PCA, ICA and HMM

This paper explores the issue of recognizing, generalizing and reproducing arbitrary gestures. We aim at extracting a representation that encapsulates only the key aspects of the gesture and discards the variability intrinsic to each person's motion. We compare a decomposition into principal components (PCA) and independent components (ICA) as a first step of preprocessing in order to decorrelate and denoise the data, as well as to reduce the dimensionality of the dataset to make this one tractable. In a second stage of processing, we explore the use of a probabilistic encoding through continuous Hidden Markov Models (HMMs), as a way to encapsulate the sequential nature and intrinsic variability of the motions in stochastic finite state automata. Finally, the method is validated in a humanoid robot to reproduce a variety of gestures performed by a human demonstrator.

Published in:
Proceedings of the International Conference on Machine Learning (ICML), 105--112
Presented at:
22nd International Conference on Machine Learning, Bonn, Germany, August 2005

 Record created 2005-11-16, last modified 2018-03-17

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