Perpustakaan Digital ITB

Effectively recognizing activities in smart environments requires either matching sensors to semantic models or labeled training data from the target environment for machine learning. Combining knowledge-driven and data-driven approaches improves activity recognition (AR) by providing the benefits of each while also mitigating their challenges. In this paper we present the Semantic Cross-Environment Activity Recognition (SCEAR) system which is a novel method for creating semantic feature spaces and enables datadriven AR systems to transfer AR models across environments. We evaluate SCEAR using 22 datasets from real-world smart environments. Transferred model performance is compared to models trained in the target environment and shown to provide a 39% average per-class improvement.