Utilising Temporal Information in Behaviour Recognition
نویسندگان
چکیده
The correct recognition of behaviours based on sensor observations in a smart home is a challenging problem; the sensor observations themselves can be noisy, and the pattern activity seen for a behaviour is rarely identical for different occurrences of the behaviour. For this reason, probabilistic methods such as Hidden Markov Models are preferred over symbolic reasoning approaches. However, these models do not deal well with interleaved behaviours, nor do they allow small variations in behaviour to be detected as abnormal, although this might be useful for the smart home, since changes in ingrained habit could be early signs of ill-
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