  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 gap in domain adaptation techniques is
the neglect of the temporal relation embedded within time series data during
the phase of aligning data distributions. Addressing this oversight, our
research presents the Deep Generative Domain Adaptation with Temporal Attention
(DGDATA) method. This novel method uniquely recognises and integrates temporal
relations during the domain adaptation process. By synergizing the capabilities
of generative models with the Temporal Relation Attention mechanism, our method
improves the classification performance in cross-user HAR. A comprehensive
evaluation has been conducted on three public sensor-based HAR datasets
targeting different scenarios and applications to demonstrate the efficacy of
the proposed DGDATA method.
