Hierarchical Markov model: Difference between revisions

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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 ̈hr, 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 ==

Hierarchical Hidden Markov Models (HHMMs) have been used in the context of eldercare aiming to identify behaviors such as eating dinner, watching television, and cooking <ref name="RecognitionOfHumanActivityThroughHierarchicalStochasticLearning" />. The goal is to learn the pattern of executing such activities normally, so that when an abnormal execution occurs (e.g., an elder person falls down while cooking) it can be automatically detected instantaneously. The appropriate action (e.g., provide audio/video cues to help the elderly or notify the appropriate caretaker that the elderly needs help) can then be taken before negative consequences for the elder person's health occur.

An HHMM is learned for each high-level activity (e.g., cooking) based on sequences of observations, where an observation is a person’s ___location in the room at a given time. The HHMMs are then used to classify new sequences.

== References ==