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Domain adaptation for sensor-based activity learning is of utmost importancein remote health monitoring research. However, many domain adaptationalgorithms suffer with failure to operate adaptation in presence of targetdomain heterogeneity (which is always present in reality) and presence ofmultiple inhabitants dramatically hinders their generalizability producingunsatisfactory results for semi-supervised and unseen activity learning tasks.We propose \emph{AEDA}, a novel deep auto-encoder-based model to enablesemi-supervised domain adaptation in the existence of target domainheterogeneity and how to incorporate it to empower heterogeneity to anyhomogeneous deep domain adaptation architecture for cross-domain activitylearning. Experimental evaluation on 18 different heterogeneous andmulti-inhabitants use-cases of 8 different domains created from 2 publiclyavailable human activity datasets (wearable and ambient smart homes) shows that\emph{AEDA} outperforms (max. 12.8\% and 8.9\% improvements for ambient smarthome and wearables) over existing domain adaptation techniques for both seenand unseen activity learning in a heterogeneous setting.

Enabling Heterogeneous Domain Adaptation in Multi-inhabitants Smart Home Activity Learning