Abstract

Sensor-based statistical models promise to support a variety of advances in human-computer interaction, but building applications that use them is currently difficult and potential advances go unexplored. We present Subtle, a toolkit that removes some of the obstacles to developing and deploying applications using sensor-based statistical models of human situations. Subtle provides an appropriate and extensible sensing library, continuous learning of personalized models, fully-automated high-level feature generation, and support for using learned models in deployed applications. By removing obstacles to developing and deploying sensor-based statistical models, Subtle makes it easier to explore the design space surrounding sensor-based statistical models of human situations. Subtle thus helps to move the focus of human-computer interaction research onto applications and datasets, instead of the difficulties of developing and deploying sensor-based statistical models. Author Keywords Toolkits, Subtle, sensor-based statistical models, machine

Citations