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Image from <ref name="RecognitionOfHumanActivityThroughHierarchicalStochasticLearning" />]]
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.▼
Abstract Hidden Markov Models have been used to recognize human behavior based on the human's position in a building as recorded by video cameras. For instance, if the person is close to a computer, the behavior recognition method can infer that the person is using the computer, or if the person is moving in a hallway towards the north exit of the building, the method infers that the person intends to exit the building through the north exit.
▲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.
== References ==
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