Understanding Human Behavior through Sensory Data and Location Based Services

Abstract

Human behavior mining is an important task in the ubiquitous computing era. Delivering relevant services or information based on the users' activity context could bring great benefit to the users. The latest advances in smart devices (e.g., smartphones, smartwatches) unlock the possibility of understanding users' context continuously with a less obtrusive approach. However, unlike motion activities (e.g., sitting, standing, or walking), human activities (e.g., having a meeting, driving to the office, having lunch) require more sensory data which captures users' context at the moment and are more complex to be identified. Therefore, various information from multiple sources collaborates to model the users' behavior and pattern. In addition, due to space and energy limitation of smart devices, users' behavior model should be minified. To address this issue, we proposed a new preprocessing methods based on the selection of values in the data. Finally, even though human activity is highly personalized based on the users' preferences but we could not neglect the importance of social influence from close friends. Based on the mobility data generated, one could identify the social relationship between two people. All in all, this work is a collaboration work with Dr. Kuo-Wei Hsu(1) and Prof. Wen-Chih Peng(2).