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Title of Journal: Pers Ubiquit Comput

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Abbravation: Personal and Ubiquitous Computing

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Springer-Verlag

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10.1007/bf01433773

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1617-4917

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Human interaction discovery in smartphone proximit

Authors: Trinh Minh Tri Do Daniel GaticaPerez
Publish Date: 2011/12/14
Volume: 17, Issue: 3, Pages: 413-431
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Abstract

Since humans are fundamentally social beings and interact frequently with others in their daily life understanding social context is of primary importance in building contextaware applications In this paper using smartphone Bluetooth as a proximity sensor to create social networks we present a probabilistic approach to mine human interaction types in real life Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related The results show that the model can automatically discover a variety of social contexts We objectively validated our model by studying its predictive and retrieval performanceBased on “GroupUs Smartphone Proximity Data and Human Interaction Type Mining” by Trinh Minh Tri Do and Daniel GaticaPerez which appeared in the Proceedings of the International Symposium on Wearable Computers San Francisco California June 2011 ©2011 IEEE


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