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Sensor-based activity recognition with dynamically added context

Wen, Jiahui, Loke, Seng W, Indulska, Jadwiga and Zhong, Mingyang 2015, Sensor-based activity recognition with dynamically added context, EAI endorsed transactions on energy web, vol. 15, no. 7, pp. 1-10, doi: 10.4108/eai.22-7-2015.2260164.

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Title Sensor-based activity recognition with dynamically added context
Author(s) Wen, Jiahui
Loke, Seng WORCID iD for Loke, Seng W orcid.org/0000-0001-9568-5230
Indulska, Jadwiga
Zhong, Mingyang
Journal name EAI endorsed transactions on energy web
Volume number 15
Issue number 7
Article ID e4
Start page 1
End page 10
Total pages 10
Publisher European Alliance for Innovation
Place of publication [Ghent, Belgium]
Publication date 2015
ISSN 2032-944X
Keyword(s) activity recognition
extra context
activity adaptation
Summary 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.
Language eng
DOI 10.4108/eai.22-7-2015.2260164
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, J. Wen et al.
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30103544

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.