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.
['Jiang Gao', 'Alexander G. Hauptmann', 'Ashok J. Bharucha', 'Howard D. Wactlar']
Dining activity analysis using a hidden Markov model