Dynamic Quantification of Activity Recognition Capabilities in Opportunistic Systems

Description

Opportunistic activity and context recognition systems draw from the characteristic to use sensing devices according to a recognition goal at runtime that just happen to be available instead of pre-defining them at the design time of the system. Whenever a user and/or application states a recognition goal at runtime to the system, the available sensing devices configure to an ensemble which is the best available set of sensors for a specified recognition goal. This paper presents an approach how machine learning technologies (classification, fusion and anomaly detection), that are integrated in a prototypical opportunistic activity and context recognition system (referred to as the OPPORTUNITY Framework) can be applied to define a metric value that quantifies the ensemble’s capabilities according to a recognition goal and evaluates the approach with respect to the requirements of an opportunistic system (e.g. ensemble configuration and reconfiguration at runtime).

Period	15 May 2011
Event title	Fourth Conference on Context Awareness for Proactive Systems: CAPS2011
Event type	Conference
Location	HungaryShow on map