Enabling Heterogeneous Domain Adaptation in Multi-inhabitants Smart Home Activity Learning
This addresses the challenge of adapting activity learning models to real-world smart home environments with diverse users and sensor setups, though it appears incremental as it builds on existing domain adaptation architectures.
The paper tackled the problem of domain adaptation for sensor-based activity learning in smart homes with heterogeneous target domains and multiple inhabitants, proposing AEDA, which achieved up to 12.8% and 8.9% improvements over existing methods for ambient and wearable datasets.
Domain adaptation for sensor-based activity learning is of utmost importance in remote health monitoring research. However, many domain adaptation algorithms suffer with failure to operate adaptation in presence of target domain heterogeneity (which is always present in reality) and presence of multiple inhabitants dramatically hinders their generalizability producing unsatisfactory results for semi-supervised and unseen activity learning tasks. We propose \emph{AEDA}, a novel deep auto-encoder-based model to enable semi-supervised domain adaptation in the existence of target domain heterogeneity and how to incorporate it to empower heterogeneity to any homogeneous deep domain adaptation architecture for cross-domain activity learning. Experimental evaluation on 18 different heterogeneous and multi-inhabitants use-cases of 8 different domains created from 2 publicly available human activity datasets (wearable and ambient smart homes) shows that \emph{AEDA} outperforms (max. 12.8\% and 8.9\% improvements for ambient smart home and wearables) over existing domain adaptation techniques for both seen and unseen activity learning in a heterogeneous setting.