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Automated mobile systems for multidimensional well-being sensing and feedback

In recent years, we have seen a prolific rise of mobile and wearable sensing in healthcare and fitness. Although the data generated is incredibly useful, state-of-the-art feedback technologies are often limited to either providing an overall status or serving large volume of multi-dimensional sensor data with little processing. My research falls into filling this gap. I work on developing systems that use sensors to understand different dimensions of well being, and subsequently devise interventions through personalized and actionable suggestions. Using simple machine learning techniques, my systems automatically mine user behaviors that influence specific well-being dimensions. Then utilizing decision theory and behavioral psychology theory, my systems create personalized actionable suggestions that are related to existing user's behaviors. In this proposal, I describe how I realize such automated systems for sensing and providing feedback.