A Framework of Fine-Grained Mobile Sensing Data Collection and Behavior Analysis in an Energy-Configurable Way

In recent years, mobile sensing data are widely used for analyzing human's activities, usage patterns, emotions, health conditions and social relationships. In order to understand and analyze human's behaviors, several frameworks have been proposed to collect mobile sensing data. In this paper we extend previous works and design StarLog, which is a distributed and energy-configurable framework for both mobile data collecting and analyzing. It collects fine-grained sensing data of five categories, reflecting user's locations, activities, interactions with smart phone, social contacts and device setting habits. Data analyses are developed on both client side and server side to understand individual as well as crowd behaviors. Besides, StarLog proposes optional modes for collecting sensory data from GPS, accelerometer, gyroscope and magnetometer to make it configurable for battery concern. To achieve the data, we recruited 21 students and processed the data collection for 20 days. Up to 17 kinds of data sources were collected, including data sources like micro messages (Weixin and QQ) that have never been collected before. Analyses of these data leaded to interesting findings in user's patterns and social ties.