This is done using a modification of the Apriori algorithm to take into account periodicity of patterns. Patterns are post-proccessed so that elements of the pattern that specify context are identified. These patterns are mapped to a heirarchical time structure, that goes from high to low granularity, and each time period has associated with it the patterns discovered in the previous step. The patterns are transformed into a markov chain, and the parameters of the chain are learned over time. A second component, called Pattern Adaptation Module (PAM) allows for monitoring of changes to these patterns. This can either be done by specifying specific patterns to look at (by the user) or automatically. In each case, a measure of the evidence of the pattern being frequent or periodic is maintained and tracked. The performance of the system is demonstrated in a smart home.
Macroscopic Human Behavior Interpretation Using Distributed Imager and Other Sensors
Relevance: Modeling of human behavior, Ambient sensing
Method: Probabilistic Context Free Grammars
Organization: Yale University
Published: October 2008, Proceedings of the IEEE
Summary: Probabilistic context free grammars are used to compose atomic activities into higher level activities. Model is temporally augmented to detect abnormal events
The system is based on a smart home equipped with Imote2 based vision sensors. The goal is to allow designers to specify grammars for behaviors. The 'programming' is therefore by domain experts, who can describe activities with high level scripts. The grammars are analogous to HMM models, however acc View More »