Surrounding context and episode awareness using dynamic Bluetooth data

Authors:
Yiqiang Chen;Zhenyu Chen;Junfa Liu;Derek Hao Hu;Qiang Yang
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and Graduate University, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing, China;Hong Kong University of Science and Technology, Kowloon, Hong Kong;Hong Kong University of Science and Technology, Kowloon, Hong Kong
Venue:
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Year:
2012

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Abstract

Bluetooth information can efficiently capture characteristics of user-centric surrounding contexts, such as formal meeting or chatting with friends, shopping with friends or alone, etc. In this paper, we extract novel features from Bluetooth traces and use these features for recognizing contextual behavior as well as inferring continuous episode transition. Evaluation results show that extracted novel features are very effective, which enable the model to achieve an average of 87% accuracy for specific context classification and the ability of episode inference from real-life Bluetooth traces.