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.
Citation:
Martin M?hlenbrock, Oliver Brdiczka, Dave Snowdon, Jean-Luc Meunier, "Learning to Detect User Activity and Availability from a Variety of Sensor Data," percom, pp.13, Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04), 2004