Lifelog is a data set composed of one or more media forms that record the same individual's daily activities. One of the main challenging issues is how to extract meaningful information from the huge and complex lifelog data which is continuously captured and accumulated from multiple sensors. This study is focused on the activity models and analysis techniques to process lifelog data in order: to find what events/states are interesting or important, to summarize the useful records in some structured and semantic ways for efficient retrievals and presentations of past life experiences, and to use these experiences to further improve the individual's quality of life. We propose an integrated technique to process the lifelog data using the correlations between different kinds of captured data from multiple sensors, instead of dealing with them separately. To use and test the proposed models and the analysis techniques, several prototype systems have been implemented and applied to some domain-specific lifelog data; such as in improving a group's collaborative efforts in revising a software, in managing kid's outdoor safety care, in providing a runner's workout assistance, and in structuring lifelog image generation, respectively.