Activity recognition has received increasing attention from the machine learning community. Of particular interest is the ability to recognize activities in real time streaming data, but this presents a number of challenges not faced by traditional offline approaches. Among these challenges is handling the large amount of data that does not belong to a predefined class. In this paper, we describe a method by which activity discovery can be used to identify behavioral patterns in observational data. Discovering patterns in the data that does not belong to a predefined class aids in understanding this data and segmenting it into learnable classes.

activity recognition based on DRNN\cairodata
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activity recognition based on DRNN

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