  In human activity recognition (HAR), the assumption that training and testing
data are independent and identically distributed (i.i.d.) often fails,
particularly in cross-user scenarios where data distributions vary
significantly. This discrepancy highlights the limitations of conventional
domain adaptation methods in HAR, which typically overlook the inherent
temporal relations in time-series data. To bridge this gap, our study
introduces a Conditional Variational Autoencoder with Universal Sequence
Mapping (CVAE-USM) approach, which addresses the unique challenges of
time-series domain adaptation in HAR by relaxing the i.i.d. assumption and
leveraging temporal relations to align data distributions effectively across
different users. This method combines the strengths of Variational Autoencoder
(VAE) and Universal Sequence Mapping (USM) to capture and utilize common
temporal patterns between users for improved activity recognition. Our results,
evaluated on two public HAR datasets (OPPT and PAMAP2), demonstrate that
CVAE-USM outperforms existing state-of-the-art methods, offering a more
accurate and generalizable solution for cross-user activity recognition.
