Activity Discovery and Activity Recognition: A New Partnership
- Resource Type
- Periodical
- Authors
- Cook, D. J.; Krishnan, N. C.; Rashidi, P.
- Source
- IEEE Transactions on Cybernetics IEEE Trans. Cybern. Cybernetics, IEEE Transactions on. 43(3):820-828 Jun, 2013
- Subject
- Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Robotics and Control Systems
General Topics for Engineers
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Hidden Markov models
Machine learning
Data models
Support vector machines
Pattern recognition
Smart homes
Machine learning algorithms
Activity recognition
out of vocabulary detection
sequence discovery
- Language
- ISSN
- 2168-2267
2168-2275
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.