Work

Activity Discovery and Activity Recognition: A New Partnership

Year: 2012

Type: article

Abstract: Activity recognition has received increasing attention from the machine learning community. Of particular interest is the ability to recognize activities in real time from 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. We demonstrate that activity discovery not only sheds light on behavioral patterns, but it can also boost the performance of recognition algorithms. We introduce this partnership between activity discovery and online activity recognition in the context of the CASAS smart home project and validate our approach using CASAS data sets.

Source: IEEE Transactions on Cybernetics

DOI: 10.1109/tsmcb.2012.2216873

Authors: Diane J. Cook, Narayanan C. Krishnan, Parisa Rashidi

Institutions: Washington State University, University of Florida

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Institute of Biomedical Imaging and Bioengineering

Awards: Smart Technologies for Health Assessment and Assistance, Smart environment technology for longitudinal behavior analysis and intervention, II-EN: Smart Environment Infrastructure for Resident and Environment Modeling