PERVASIVEHEALTH

Computational sensing, machine learning, and user interface systems for preventive medicine and personal, behavioral informatics; persuasive user interfaces for motivating behavior change; sensor-enabled mobile health technologies; context-aware ecological momentary assessment; experimental ubiquitous computing; living laboratories; perceptually-based interactive environments; pattern recognition and dynamic scene understanding; artificial intelligence; health technology and policy.

I am exploring the development and evaluation of personal, behavioral health informatics - how sensor data acquired throughout everyday life from miniature mobile and in-home sensors might be used to improve wellness via novel human-computer interfaces. This research involves merging ideas from the computer science subfields of pattern recognition, machine learning, computational sensing, and artificial intelligence with ideas from behavioral science, behavioral medicine, social psychology, and preventive medicine. I am particularly interested in how algorithms that recognize everyday activities can drive the development of interactive preventive health tools that could ultimately be applied at the population scale in a cost-effect manner. Within computer science, this requires developing new user-driven activity detection algorithms that use statistical integration of contextual information when large activity training sets are not available. Within preventive medicine, this requires building and deploying pilot systems and demonstrating that the technology does have a real-world impact on health outcomes. As part of this work, my research group has worked to create new tools that can be used to both measure and motivate behavior change taking advantage of emerging sensor-based technologies.