  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.
