  In practice, Wearable Human Activity Recognition (WHAR) models usually face
performance degradation on the new user due to user variance. Unsupervised
domain adaptation (UDA) becomes the natural solution to cross-user WHAR under
annotation scarcity. Existing UDA models usually align samples across domains
without differentiation, which ignores the difference among samples. In this
paper, we propose an unsupervised domain adaptation model with sample weight
learning (SWL-Adapt) for cross-user WHAR. SWL-Adapt calculates sample weights
according to the classification loss and domain discrimination loss of each
sample with a parameterized network. We introduce the meta-optimization based
update rule to learn this network end-to-end, which is guided by
meta-classification loss on the selected pseudo-labeled target samples.
Therefore, this network can fit a weighting function according to the
cross-user WHAR task at hand, which is superior to existing sample
differentiation rules fixed for special scenarios. Extensive experiments on
three public WHAR datasets demonstrate that SWL-Adapt achieves the
state-of-the-art performance on the cross-user WHAR task, outperforming the
best baseline by an average of 3.1% and 5.3% in accuracy and macro F1 score,
respectively.
