Human Activity Recognition (HAR) using mmWave radar provides a non-invasive alternative to traditional sensor-based methods but suffers from domain shift, where model performance declines in new users, positions, or environments. To address this, we propose mmADA, an Active Domain Adaptation (ADA) framework that efficiently adapts mmWave-based HAR models with minimal labeled data. mmADA enhances adaptation by introducing Renyi Entropy-based uncertainty estimation to identify and label the most informative target samples. Additionally, it leverages contrastive learning and pseudo-labeling to refine feature alignment using unlabeled data. Evaluations with a TI IWR1443BOOST radar across multiple users, positions, and environments show that mmADA achieves over 90% accuracy in various cross-domain settings. Comparisons with five baselines confirm its superior adaptation performance, while further tests on unseen users, environments, and two additional open-source datasets validate its robustness and generalization.

翻译：利用毫米波雷达进行人体活动识别（HAR）为传统基于传感器的方法提供了一种非侵入性替代方案，但面临域偏移问题，即模型在新用户、新位置或新环境中的性能会下降。为解决此问题，我们提出了mmADA，一种主动域自适应（ADA）框架，能够以最少的标注数据高效地适应基于毫米波的HAR模型。mmADA通过引入基于Renyi熵的不确定性估计来识别并标注最具信息量的目标样本，从而增强自适应效果。此外，该框架利用对比学习和伪标注技术，借助未标注数据优化特征对齐。采用TI IWR1443BOOST雷达在多个用户、位置及环境中进行的评估表明，mmADA在各种跨域场景下实现了超过90%的准确率。与五种基线方法的对比验证了其卓越的自适应性能，而在未见过的用户、环境以及两个额外开源数据集上的进一步测试，则证实了其鲁棒性和泛化能力。