Sensor-based human activity recognition (HAR) mines activity patterns from the time-series sensory data. In realistic scenarios, variations across individuals, devices, environments, and time introduce significant distributional shifts for the same activities. Recent efforts attempt to solve this challenge by applying or adapting existing out-of-distribution (OOD) algorithms, but only in certain distribution shift scenarios (e.g., cross-device or cross-position), lacking comprehensive insights on the effectiveness of these algorithms. For instance, is OOD necessary to HAR? Which OOD algorithm performs the best? In this paper, we fill this gap by proposing HAROOD, a comprehensive benchmark for HAR in OOD settings. We define 4 OOD scenarios: cross-person, cross-position, cross-dataset, and cross-time, and build a testbed covering 6 datasets, 16 comparative methods (implemented with CNN-based and Transformer-based architectures), and two model selection protocols. Then, we conduct extensive experiments and present several findings for future research, e.g., no single method consistently outperforms others, highlighting substantial opportunity for advancement. Our codebase is highly modular and easy to extend for new datasets, algorithms, comparisons, and analysis, with the hope to facilitate the research in OOD-based HAR. Our implementation is released and can be found at https://github.com/AIFrontierLab/HAROOD.

翻译：传感器基人体活动识别（HAR）从时序传感数据中挖掘活动模式。在实际场景中，个体、设备、环境和时间的差异为同一活动引入了显著的分布偏移。近期研究尝试通过应用或适配现有分布外（OOD）算法来解决这一挑战，但仅针对特定分布偏移场景（如跨设备或跨位置），缺乏对这些算法有效性的全面洞察。例如，OOD对HAR是否必要？哪种OOD算法性能最佳？本文通过提出HAROOD填补了这一空白，这是一个面向OOD设置下HAR的综合基准。我们定义了4种OOD场景：跨人、跨位置、跨数据集和跨时间，并构建了一个覆盖6个数据集、16种对比方法（采用基于CNN和Transformer的架构实现）以及两种模型选择协议的测试平台。随后，我们进行了大量实验并提出了若干对未来研究具有启示的发现，例如：没有单一方法能持续优于其他方法，这凸显了该领域巨大的进步空间。我们的代码库高度模块化，易于扩展以支持新数据集、算法、比较和分析，旨在促进基于OOD的HAR研究。实现代码已开源，可通过https://github.com/AIFrontierLab/HAROOD获取。