User Behavior Pattern Analysis and Prediction Based on Mobile Phone Sensors

被引：0
作者：
Song, Jiqiang [1 ]
Tang, Eugene Y. [1 ]
Liu, Leibo [2 ]
机构：
[1] Intel Labs China, Beijing, Peoples R China
[2] Tsinghua Univ, Beijing, Peoples R China
来源：
NETWORK AND PARALLEL COMPUTING | 2010年 / 6289卷
关键词：
Mobile computing; sensor; user behavior analysis; pattern prediction; MAST; context-adaptive; individualized;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页码：177 / +
页数：3
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