Towards Context Consistency in a Rule-Based Activity Recognition Architecture

Accurate human activity recognition (AR) is crucial for intelligent pervasive environments, e.g., energy-saving buildings. In order to gain precise and fine-grained AR results, a system must overcome partial observability of the environment and noisy, imprecise, and corrupted sensor data. In this work, we propose a rule-based AR architecture that effectively handles multiple-user, multiple-area situations, recognizing real-time office activities. The proposed solution is based on an ontological approach, using low-cost, binary, wireless sensors. We employ context consistency diagrams (CCD) as a key component for fault correction. A CCD is a data structure that provides a mechanism for probabilistic reasoning about the current situation and determines the most probable current situation in the presence of inconsistencies, conflicts, and ambiguities in sensor readings. The implementation of the system and its evaluation in a living lab environment show that the CCD corrects up to 46.8% of sensor data faults, improving overall recognition accuracy by up to 11.1%, thus achieving reliable recognition results from unreliable sensor data.