Inferring social contextual behavior from bluetooth traces

Authors:
Zhenyu Chen;Yiqiang Chen;Shuangquan Wang;Junfa Liu;Xingyu Gao;Andrew T. Campbell
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijng, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Dartmouth College, Hanover, NH, USA
Venue:
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Year:
2013

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Abstract

Context-aware computing is increasingly paid much attention, especially makes the people's social contextual behavior very crucial for user-centric dynamic behavior inference. At present, extensive work has focused on detecting specific places inferred by static radio signals like GPS, GSM and WiFi, and recognizing mobility modes inferred by embedded sensor components like accelerometer. This paper proposes a distinct feature based classification approach and context restraint based majority vote rule to infer social contextual behavior in dynamic surroundings. Experimental results indicate that our proposed method can achieve high accuracy for inferring social contextual behavior through the real-life Bluetooth traces.