这篇论文拆解了HAR模型在真实场景中表现不佳的原因，系统测试了4种偏移和28种方法，结论对做可穿戴设备或传感器AI的人很有参考价值。
该论文系统评估了人类活动识别（HAR）中4种分布偏移：设备类型、传感器位置、采样率和用户行为。研究发现多样性偏移主导所有偏移类型，表明不同域间存在独特特征。论文引入了统一的HAR分布偏移基准，并全面评估了28种域泛化方法。结果显示现有域泛化算法在实现模型泛化上仅微弱优于经验风险最小化基线。这是首个针对传感器HAR中特定分布偏移的域泛化和适应系统性探索，并提供了开源基准平台和数据集。
Assessing Distribution Shift in Human Activity Recognition for Domain Generalization
While the field of Human Activity Recognition (HAR) continues to draw interest from researchers and advance in important ways, some key challenges remain. One of the most difficult aspects of building HAR models that show good performance in real-world settings is dealing with data diversity from device and sensor heterogeneity, and contextual changes that are intrinsic to real-world applications. While data diversity in HAR has been well-acknowledged in the literature, there remains a gap in understanding the effect of various types of distribution shifts on HAR models and the domain generalization problem that arises. Towards that end, this paper systematically evaluates 4 different types of distribution shifts, including variations in device type, sensor placement, sampling rate, and user behavior. Quantifying their effects, we illustrate that diversity shifts predominantly define all types of shifts, indicating the existence of unique features that are not shared across different domains. We then introduce a uniform HAR-based distribution shift benchmarks and conduct a comprehensive evaluation of up to 28 domain generalization methods. Our analysis exposes the limitations of current domain generalization algorithms in achieving model generalizability, marginally outperforming the empirical risk minimization baseline. This work represents the first systematic exploration of domain generalization and adaptation concerning specific distribution shifts in sensor-based HAR, offering an open-source benchmark platform and datasets to spur further research.