Abstract: Wearable human activity recognition (HAR) plays an important role in intelligent health monitoring and behavioral analysis. However, realworld applications often encounter challenges such as limited user diversity and insufficient labeled data, which constrain the model’s generalization capability. To enhance crossuser adaptability while preserving data privacy, this study proposes a privacyfriendly human activity recognition model based on multistrategy data augmentation. The proposed framework integrates source pretrained networks with targetdomain data to achieve domain adaptation without requiring access to the source data. In the source domain, multiaxis sensor signals are transformed into activity images, and spatialtemporal representations are extracted using a GCNTransformer backbone for pretraining. In the target domain, multiple data augmentation strategies are employed to generate diverse views of activity samples. Furthermore, threelevel consistency constraints, applied to intermediate features, logits outputs, and pseudolabel confidence, enhance the model’s domain adaptability and robustness. Experimental results demonstrate that the proposed model achieves superior accuracy and generalization across users, providing a promising solution for personalized and privacypreserving applications in wearable computing.

Key words: sourcefree domain adaptation; data privacy protection; data augmentation; human activity recognition; ensemble learning

摘要： 可穿戴人体活动识别在实际应用中常因覆盖群体有限、标注数据不足等问题而导致模型泛化能力不足.为提升跨用户适应性，已有研究提出多源域适应方法，但此类方法需要直接访问多个源域数据，容易引发隐私泄露风险.针对上述问题，提出一种基于多策略数据增强的人体活动识别模型.该模型结合源域预训练模型与目标域数据，在无需访问源数据的前提下，实现了兼顾隐私保护与识别性能的域适应.具体而言，源域将多轴传感信号转化为活动图像，并通过GCNTransformer主干网络提取空间与时序特征完成活动识别预训练；目标域则采用多种数据增强策略生成多视角样本，在中间特征、Logits输出与伪标签置信度3层施加一致性约束，从而提升源模型的域适应能力与鲁棒性.实验结果表明，该模型在跨用户识别任务中显著提高了准确率与泛化性能，为可穿戴设备的个性化识别与隐私保护应用提供了有效支持.

关键词: 无源域适应；数据隐私保护；数据增强；人体活动识别；集成学习