Sporadic State Estimation for General Activity Inference
نویسندگان
چکیده
We present a probabilistic behavior recognition model that can be used to segment continuous data and distinguish between dozens of different activities. The model uses a natural semantic specification of partiallyordered activities. In order to provide robust and general activity descriptions certain parameters of the underlying probabilistic model are deliberately left unspecified. However, we show how inference on a dynamic Bayesian network of the model can be efficiently supported by suspending complete state estimation until queried. We present results on recognizing the performance of activities of daily living (such as cooking and cleaning) in a real home, where data is gathered using wearable RFID tag readers to detect the physical manipulation of household objects.
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