Deep learning-based human activity recognition (HAR) methods have shown great promise in the applications of smart healthcare systems and wireless body sensor network (BSN). Despite their demonstrated performance in laboratory settings, the real-world implementation of such methods is still hindered by the cross-subject issue when adapting to new users. To solve this issue, we propose ActiveSelfHAR, a framework that combines active learning's benefit of sparsely acquiring data with actual labels and self- training's benefit of effectively utilizing unlabeled data to enable the deep model to adapt to the target domain, i.e., the new users. In this framework, the model trained in the last iteration or the source domain is first utilized to generate pseudo labels of the target-domain samples and construct a self-training set based on the confidence score. Second, we propose to use the spatio-temporal relationships among the samples in the non-self-training set to augment the core set selected by active learning. Finally, we combine the self-training set and the augmented core set to fine-tune the model. We demonstrate our method by comparing it with state-of-the-art methods on two IMU-based datasets and an EMG-based dataset. Our method presents similar HAR accuracies with the upper bound, i.e. fully supervised fine-tuning with less than 1\% labeled data of the target dataset and significantly improves data efficiency and time cost. Our work highlights the potential of implementing user-independent HAR methods into smart healthcare systems and BSN.

翻译：基于深度学习的人体活动识别（HAR）方法在智能医疗系统与无线体域网（BSN）应用中展现出显著潜力。尽管此类方法在实验室环境中表现优异，但在实际部署中仍面临跨个体问题，即难以适应新用户。为解决该问题，我们提出ActiveSelfHAR框架，该框架结合了主动学习稀疏获取真实标签数据的优势与自训练有效利用无标签数据的能力，使深度模型能够适应目标域（即新用户）。该框架首先利用上一轮迭代或源域训练的模型为目标域样本生成伪标签，并基于置信度分数构建自训练集；其次，我们提出利用非自训练集中样本的时空关系增强主动学习所选核心集；最后，结合自训练集与增强核心集对模型进行微调。我们在两个基于IMU的数据集与一个基于EMG的数据集上将所提方法与现有最优方法进行对比实验。结果表明，本方法在仅使用目标数据集不足1%标注数据的情况下，取得了与上界（即全监督微调）相近的HAR精度，同时显著提升了数据效率与时间成本。本研究揭示了将用户无关的HAR方法部署至智能医疗系统与BSN的可行性。