Abstract : More and more mobile phones are equipped with multiple sensors today. This creates a new opportunity to analyze users' daily behaviors and evolve mobile phones into truly intelligent personal devices, which provide accurate context-adaptive and individualized services. This paper proposed a MAST (Movement, Action, and Situation over Time) model to explore along this direction and identified key technologies required. The sensing results gathered from some mobile phone sensors were presented to demonstrate the feasibility. To enable always sensing while reducing power consumption for mobile phones, an independent sensor subsystem and a phone-cloud collaboration model were proposed. This paper also listed typical usage models powered by mobile phone sensor based user behavior prediction.
https://hal.inria.fr/hal-01054992
Contributeur : Hal Ifip
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Soumis le : lundi 11 août 2014 - 08:44:43
Dernière modification le : vendredi 11 août 2017 - 17:44:31
Document(s) archivé(s) le : mercredi 26 novembre 2014 - 21:41:52
Jiqiang Song, Eugene Y. Tang, Leibo Liu. User Behavior Pattern Analysis and Prediction Based on Mobile Phone Sensors. Chen Ding; Zhiyuan Shao; Ran Zheng. IFIP International Conference on Network and Parallel Computing (NPC), Sep 2010, Zhengzhou, China. Springer, Lecture Notes in Computer Science, LNCS-6289, pp.177-189, 2010, Network and Parallel Computing. 〈10.1007/978-3-642-15672-4_16〉. 〈hal-01054992〉
