Developing and designing dynamic mobile applications that transform wearable data with machine learning and mathematical models.

Wearable analytics hold far more potential than sleep tracking or step counting. In recent years, a number of applications have emerged which leverage the massive quantities of data being amassed by wearables around the world, such as real-time mood detection, advanced COVID screening, and heart rate variability analysis. Yet packaging insights from research for success in the consumer market means prioritizing design and understandability, while also seamlessly managing the sometimes-unreliable stream of data from the device. In this presentation, I will discuss my own experiences building apps which interface with wearable data and process the data using mathematical modeling, as well as recent work extending to other wearable streams and environmental controls.