论文标题
在多居民中实现异质域的适应性智能家庭活动学习
Enabling Heterogeneous Domain Adaptation in Multi-inhabitants Smart Home Activity Learning
论文作者
论文摘要
在远程健康监测研究中，基于传感器的活动学习的领域适应性至关重要。然而，许多领域适应算法在存在目标域异质性（始终存在于现实中）的情况下未能进行适应性，并且多个居民的存在极大地阻碍了他们的普遍性，从而产生了难以令人满意的成果，从而无法令人满意地进行半胜诉和看不见的活动学习任务。我们提出了一种新型的基于自动编码器的新型模型\ emph {AEDA}，以实现目标域异质性的存在，以及如何将其纳入任何同质深层域适应架构，以促进异质性的异质性，以实现目标域异质性的适应性。 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 环境。
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.
