Thesis
Activity recognition in complex smart environment settings
Washington State University
Master of Science (MS), Washington State University
2009
Handle:
https://hdl.handle.net/2376/103192
Abstract
Smart environments rely on artificial intelligence techniques to make sense of the sensor data and to use the information for recognizing and tracking activities. However, many of the techniques that have been developed are designed for simplified situations. In this thesis we investigate more complex situations like recognizing activities when they are interweaved in realistic scenarios and when the space is inhabited by multiple resident performing tasks concurrently. This technology is beneficial for monitoring the health of smart environment residents and for correlating activities with parameters such as energy usage. We describe our approach to sequential, interleaved and concurrent (multi-resident) activity recognition and evaluate various probabilistic techniques for activity recognition. In addition to demonstrating that these activities can be recognized by sensors in physical environments using Markov and Hidden Markov models, we also show variants of these models that help in improving the recognition accuracy. We validate our algorithm on real sensor data collected in the CASAS smart apartment testbed.
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Details
- Title
- Activity recognition in complex smart environment settings
- Creators
- Geetika Singla
- Contributors
- Diane J. Cook (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; Pullman, Wash. :
- Identifiers
- 99900524876701842
- Language
- English
- Resource Type
- Thesis