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User Behavior Pattern Analysis and Prediction Based on Mobile Phone Sensors

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.
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https://hal.inria.fr/hal-01054992
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Jiqiang Song, Eugene Y. Tang, Leibo Liu. User Behavior Pattern Analysis and Prediction Based on Mobile Phone Sensors. IFIP International Conference on Network and Parallel Computing (NPC), Sep 2010, Zhengzhou, China. pp.177-189, ⟨10.1007/978-3-642-15672-4_16⟩. ⟨hal-01054992⟩

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