Development of Algorithms for Detecting the Activities of Adults and Children From Wearable Sensors
Lead PI
Abstract
We are working on a variety of projects studying how to use mobile sensor data, especially from accelerometers, to detect physical activity (type, duration, and intensity), sedentary behavior, and sleep in adults and children. We are interested in extending this work to detect habits, and ultimately to develop a comprehensive probabilistic model of someone’s behaviors.
Collaborators
EveryFit, Inc. (QMedic), Stanford Medical School, Case Western Reserve University