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.

本论文提出了一种新的基于样本权重学习的无监督领域自适应方法，称为SWL-Adapt，用于解决新用户的穿戴式人体活动识别性能下降的问题，并在三个公共数据集上进行了广泛的实验，结果显示SWL-Adapt在跨用户的WHAR任务中表现优异，平均准确度和宏F1分数分别达到了3.1％和5.3％的最佳基线以上。

SWL-Adapt：基于样本权重学习的无监督领域自适应模型，用于跨用户可穿戴人体活动识别