Enabling Dynamic Sensor Configuration and Cooperation in Opportunistic Activity Recognition Systems

Abstract

Opportunistic activity recognition as research discipline is characterized by the fact that human activities (and more generally the context) shall be recognized with sensors that are initially unknown to the system. In contrast to “traditional” applications—where sensors, their modalities, locations, and working characteristics have to be defined at design time—opportunistic systems do not rely on an initially defined and fixed sensing infrastructure. Sensors have to be utilized upon their spontaneous availability and activity recognition capabilities and dynamic sensor ensembles have to be configured at runtime with respect to maximized recognition accuracy and minimized energy consumption. This requirement contains two research challenges that this paper tackles: (i) estimating the accuracy of an ensemble without being able to compare the output in the form of recognized activity classes to a (labeled) ground truth and (ii) optimizing the accuracy/energy trade-off by applying exact and heuristic methods adapted for cooperative sensor ensembles.