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.
翻译:在远程健康监测研究中,基于传感器活动学习的适应领域至关重要,然而,许多领域适应算法在目标领域差异性(现实中始终存在)的情况下,未能进行适应,多居民的存在严重妨碍其普遍性,在半监督和无形活动学习任务方面产生不令人满意的结果。我们建议采用一个新的深层次的基于自动编码的模型,在目标领域差异性存在的情况下,使半监督域适应工作得以进行,并如何将其纳入到任何同质深海适应结构中,以便进行跨域活动学习。 对18个不同差异和多居民使用从2个公开人类活动数据集(湿和周围智能之家)创建的8个不同领域的实验评价表明,在混合环境中,现有域适应技术(环境智能住宅和可磨损住宅的12.8 ⁇ 和8.9 ⁇ 改进)超越了现有域适应技术,供观察和无形活动学习。