Abstract
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 this https URL.
Abstract (translated)
基于传感器的人体活动识别（HAR）从时间序列的传感数据中挖掘行为模式。在现实场景中，个体、设备、环境和时间的变化会引入同一活动中显著的分布变化。近期的努力尝试通过应用或调整现有的出站分布（OOD）算法来解决这一挑战，但仅限于某些分布变化情景（如跨设备或跨位置），缺乏对这些算法有效性全面的理解。例如，对于HAR来说OOD是否必要？哪种OOD算法表现最佳？ 在本文中，我们填补了这一空白，提出了HAROOD，一个针对OOD设置的综合基准测试平台。我们定义了4种OOD场景：跨个人、跨位置、跨数据集和跨时间，并构建了一个包含6个数据集、16种对比方法（采用CNN基和Transformer基架构）、两个模型选择协议的实验环境。然后，我们进行了广泛的实验并提出了多项对未来研究有价值的发现，例如没有单一的方法能持续优于其他方法，这突显了在这个领域中巨大的进步空间。 我们的代码库高度模块化且易于扩展新的数据集、算法、对比以及分析内容，旨在促进基于OOD的人体活动识别的研究。我们的实现已经发布，并可在以下链接找到：[此URL]。
URL
https://arxiv.org/abs/2512.10807
