Deep Generative Domain Adaptation with Temporal Attention for Cross-User Activity Recognition

arXiv:2403.17958v1 Announce Type: new
Abstract: In Human Activity Recognition (HAR), a predominant assumption is that the data utilized for training and evaluation purposes are drawn from the same distribution. It is also assumed that all data samples are independent and identically distributed ($\displaystyle i.i.d.$). Contrarily, practical implementations often challenge this notion, manifesting data distribution discrepancies, especially in scenarios such as cross-user HAR. Domain adaptation is the promising approach to address these challenges inherent in cross-user HAR tasks. However, a clear …

abstract arxiv attention challenge cs.ai cs.cv cs.hc cs.lg data distributed distribution domain domain adaptation evaluation generative human independent practical recognition samples temporal training type