Learning to detect user activity and availability from a variety of sensor data

Abstract

Using a networked infrastructure of easily available sensors and context-processing components, we are developing applications for the support of workplace interactions. Notions of activity and availability are learned from labeled sensor data based on a Bayesian approach. The higher-level information on the users is then automatically derived from low-level sensor information in order to facilitate informal ad hoc communications between peer workers in an office environment.