17th International Conference on Pattern Recognition (ICPR'04) - Volume 2
Dining Activity Analysis Using a Hidden Markov Model
Cambridge UK
August 23-August 26
ISBN: 0-7695-2128-2
Jiang Gao, Carnegie Mellon University, Pittsburgh, PA
Ashok Bharucha, University of Pittsburgh Medical Center, Pittsburgh, PA
We describe an algorithm for dining activity analysis in a nursing home. Based on several features, including motion vectors and distance between moving regions in the subspace of an individual person, a hidden Markov model is proposed to characterize different stages in dining activities with certain temporal order. Using HMM model, we are able to identify the start (and ending) of individual dining events with high accuracy and low false positive rate. This approach could be successful in assisting caregivers in assessments of resident's activity levels over time.
Citation:
Jiang Gao, Alexander G. Hauptmann, Ashok Bharucha, Howard D. Wactlar, "Dining Activity Analysis Using a Hidden Markov Model," icpr, vol. 2, pp.915-918, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 2, 2004