Sensor-Based Activity Recognition with Dynamically Added Context
Journal Title: EAI Endorsed Transactions on Energy Web - Year 2015, Vol 2, Issue 7
Abstract
An activity recognition system essentially processes raw sensor data and maps them into latent activity classes. Most of the previous systems are built with supervised learning techniques and pre-defined data sources, and result in static models. However, in realistic and dynamic environments, original data sources may fail and new data sources become available, a robust activity recognition system should be able to perform evolution automatically with dynamic sensor availability in dynamic environments. In this paper, we propose methods that automatically incorporate dynamically available data sources to adapt and refine the recognition system at run-time. The system is built upon ensemble classifiers which can automatically choose the features with the most discriminative power. Extensive experimental results with publicly available datasets demonstrate the effectiveness of our methods.
Authors and Affiliations
Jiahui Wen, Seng Loke, Jadwiga Indulska, Mingyang Zhong
Keywords
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- EP ID EP45141
- DOI http://dx.doi.org/10.4108/eai.22-7-2015.2260164
- Views 225
- Downloads 0
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