基于多层神经网络的人类活动识别，智能家居领域的一项重大突破。-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.