UC Irvine

The real-time detection of personal life events has great potential to provide human-friendly services. Using detected life events in the fields of health care, smart homes, elderly surveillance, and smart car, etc. will provide more relational information and/or services to a user. However, automatically detecting a life event from human behaviors and their surrounding contexts is a challenging problem. I believe that the combination of Formal Concept Analysis (FCA) and a context-aware mobile computing system can help a path toward automated life event detection. The advancement of the smartphone and its embedded sensors will enable this scenario. I propose a generic context-aware system based on Formal Concept Analysis, which covers both front-end and back-end processing, to detect human life events. The main contributions of this system are: 1) it provides a framework for real-time personal monitoring; 2) it integrates, processes and stores personalized latent features; and 3) it combines heterogeneous data streams into one life event. The experimental validation, which I implemented on an Android platform and server, demonstrates that a general life event model can be applied to each individual and that concept data analysis can be substituted for statistical data analyses in life event detection. I believe that my findings can lead to new event detection approach, which is not just confined to specific environments, such as Social Life Networking, video streams, or artificially organized sentient locations, but can be opened for all environments in the real world by using sensors and context factors.