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.

翻译：在实际应用中，可穿戴人体活动识别（WHAR）模型常因用户差异而在新用户上出现性能下降。无监督域适应（UDA）成为标注稀缺条件下跨用户WHAR的自然解决方案。现有UDA模型通常不加区分地对齐跨域样本，忽略了样本间的差异性。本文提出一种基于样本权重学习的无监督域适应模型（SWL-Adapt）用于跨用户WHAR。SWL-Adapt通过参数化网络，根据每个样本的分类损失和域判别损失计算样本权重。我们引入基于元优化的更新规则对该网络进行端到端学习，该规则由选定伪标签目标样本上的元分类损失引导。因此，该网络能够根据当前跨用户WHAR任务拟合权重函数，优于仅针对特定场景的现有样本差异化规则。在三个公开WHAR数据集上的大量实验表明，SWL-Adapt在跨用户WHAR任务上达到了最先进的性能，在准确率和宏F1分数上分别比最佳基线平均高出3.1%和5.3%。