Learning to Detect User Activity and Availability from a Variety of Sensor Data

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. 1. Opportunistic meetings In an office environment, unplanned interactions play an important role in the exchange of information as well as for the social cohesion of a group, their importance should not be neglected, as they are often instrumental to the organization [10]. In contrast to simply spontaneous encounters, opportunistic meetings occur where one person has a reason to talk with another but has not arranged to meet in advance and thus either seeks them out or waits until an encounter occurs by chance to raise the issue [9]. However, there is always the danger that the encounter might not take place (or not in a timely manner), leaving the issue unaddressed. In this paper we consider how such meetings, referred to here as IFFI’s (for ‘informal faceto-face interactions’), might be facilitated. In the first place by alleviating the burden of finding an appropriate time and place to meet while taking into account each person’s constraints and activities. Facilitating IFFI’s differs from making a rendezvous, since it enables a deep interleaving with current ongoing activities so as to support the setting-up of an interaction in near real-time. Secondly, these interactions should be supported in situ and in an adhoc manner, as they cannot be logistically pre