Hierarchical Markov model: Difference between revisions

Revision as of 00:25, 17 December 2011

Revision as of 00:36, 17 December 2011

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=== Abstract Hidden Markov Model ===

An Abstract Hidden Markov Model (AHMM) <ref name="AHMM">H. H. Bui, S. Venkatesh, and G. West [http://dl.acm.org/citation.cfm?id=1622824 Policy recognition in the abstract hidden markov model]. Journal of Artificial Intelligence Research, vol. 17, p. 451–499, 2002.</ref> is an extension of a [[Hierarchical hidden Markov model|Hierarchical Hidden Markov Model]] that allows the choice of how a high-level activity (policy) will be decomposed into a sequence of lower-level activities (policies) to be dependent on the current state of the environment. Thus, simple types of context-sensitive behaviors can be captured by an AHMM <ref name="RecognitionOfHumanActivityThroughHierarchicalStochasticLearning"> S. Lu ̈hrLuhr, H. H. Bui, S. Venkatesh, and G. A. W. West [http://dl.acm.org/citation.cfm?id=826390 Recognition of Human Activity through Hierarchical Stochastic Learning]. PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. </ref>.

== Applications to Human Behavior Recognition ==

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Once the HHMMs for the kitchen and living room behaviors have been learned, they can be used to classify new sequences of observations. The experiments with recognizing simplistic kitchen and living room behaviors in <ref name="RecognitionOfHumanActivityThroughHierarchicalStochasticLearning" /> report high classification accuracy--83% for the cooking sequences and 80% for the living room sequences.

The HHMMs are then used to classify new sequences.

== References ==