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. In: Mihaela Ulieru and Valeriy Vyatkin, 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services MOBIQUITOUS 2015. International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Coimbra, Portugal, (e4.1-e4.10). 22 – 24 July 2015. doi:10.4108/eai.22-7-2015.2260164

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Author Wen, Jiahui
Loke, Seng W.
Indulska, Jadwiga
Zhong, Mingyang
Title of paper Sensor-based activity recognition with dynamically added context
Conference name International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
Conference location Coimbra, Portugal
Conference dates 22 – 24 July 2015
Proceedings title 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services MOBIQUITOUS 2015   Check publisher's open access policy
Journal name EAI Endorsed Transactions on Energy Web   Check publisher's open access policy
Place of Publication Ghent, Belgium
Publisher Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (I C S T)
Publication Year 2015
Sub-type Fully published paper
DOI 10.4108/eai.22-7-2015.2260164
Open Access Status File (Publisher version)
ISSN 2032-944X
Editor Mihaela Ulieru
Valeriy Vyatkin
Volume 15
Issue 7
Start page e4.1
End page e4.10
Total pages 10
Collection year 2016
Language eng
Abstract/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.
Keyword Activity recognition
Extra context
Activity adaptation
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Sun, 22 Nov 2015, 16:32:16 EST by Jiahui Wen on behalf of School of Information Technol and Elec Engineering