Smartphones equipped with powerful embedded sensors (e.g. accelerometers, GPS, microphones, etc.) can be used to monitor multiple dimensions of human behaviors including physical, mental, and social behaviors of wellbeing. In particular, such enhanced capabilities in mobile devices enable people to better manage their health. Existing mobile health applications mostly rely on manual data entry which may not be sufficient for certain population e.g. seniors and students with emotional behavioral disorders (EBD). This project aims to build a multimodal sensing system which provides continuous and efficient monitoring of users? activities using mobile phones and wearable sensors. Our system analyzes and correlates different sensor streams to infer certain behaviors as well as possible environmental factors that may trigger such behaviors. Furthermore, our system provides non-intrusive peer assisted localization technique that allows caregivers to track the whereabouts of monitored users. We also develop efficient schemes to infer higher layer information e.g. activity levels of monitored individuals; social relationships among monitored users. Additionally, the communities extracted from a mobile phone enabled social network in our system not only enable mobile healthcare systems but can also be exploited for securing certain components of the system (e.g., coping with clone attacks). Our system allows users to be monitored for their mental, cognitive, and physical well-being and can potentially reduce the cost for special need education as a result of increasing the productivity of teachers and caregivers.
