Life Logging Practice for Human Behavior Modeling

With the fast progress of mobile computing, personal lifelogging gets easier taking a little effort for automation. However data-recalling services to help a user locate or remind the event in her lifelogs still lacks supports in major aspects due to the difficulty in creating a useful environment for users. This is because a collection of lifelog streams needs high-level behavior analysis techniques to create the meaningful summary of person's daily life. The challenging problem is that such knowledge depends on the types of data proprietarily created by the sensors where a complex knowledge on human activity patterns is not easy to apply for real-life event modeling and detection. This paper addresses many aspects of related lifelog research topics and technical challenges. We also studied the way to first handle disparate sensor data and showed the case study to put them on the table for lifelog data analysis and exploration.