Dining activity analysis using a hidden Markov model

作者: H.D. Wactlar , A. Bharucha , A.G. Hauptmann , Jiang Gao

DOI: 10.1109/ICPR.2004.305

关键词: Hidden Markov modelMaximum-entropy Markov modelArtificial intelligenceNursing homesForward algorithmMachine learningSubspace topologyPattern recognitionComputer scienceMotion (physics)Markov model

摘要: We describe an algorithm for dining activity analysis in a nursing home. Based on several features, including motion vectors and distance between moving regions the subspace of individual person, hidden Markov model is proposed to characterize different stages activities with certain temporal order. Using HMM model, we are able identify start (and ending) events high accuracy low false positive rate. This approach could be successful assisting caregivers assessments resident's levels over time.

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