说明：  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.)