发表机构
University of Maryland; Boston University; University of Michigan（马里兰大学; 波士顿大学; 密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用老年人HAR数据集MyMove，发现年轻人基准上的模型改进难以迁移至老年人，而基于UK Biobank预训练的自监督特征可显著缩小性能差距，表明需关注人群多样性与个性化适应。
AI 中文摘要
基于腕戴式加速度计的人类活动识别（HAR）越来越多地用于健康和行为追踪。然而，大多数可穿戴HAR模型是在以年轻人为主的数据集上开发和评估的，这使得基准测试的进展是否能跨年龄组推广尚不明确。在本研究中，我们利用MyMove——我们精心标注的、自由生活的老年人HAR数据集（平均年龄71岁）——在留一受试者交叉验证和跨数据集迁移两种设置下评估深度学习架构和训练方案。我们发现，在年轻人基准上的改进无法同等地迁移到从老年人收集的数据上，导致性能差距持续存在且往往扩大。然而，更丰富的表征，特别是基于年龄多样化的UK Biobank数据集预训练的冻结自监督特征，显著提升了在老年人数据上的性能，并持续缩小性能差距，尽管对年轻人性能有适度代价，但差距仍然存在。这些发现表明，仅凭基准提升和架构扩展无法完整反映可穿戴HAR的进展，更广泛的进步可能需要更好地捕捉人群多样性的表征，以及对个体运动模式和日常习惯的个性化适应。
英文摘要
Human activity recognition (HAR) from wrist-worn accelerometers is increasingly used for health and behavioral tracking. Yet, most wearable HAR models are developed and evaluated on datasets dominated by younger adults, leaving it unclear whether benchmark progress generalizes across age groups. In this work, we leverage MyMove, our carefully annotated, free-living older-adult HAR dataset (mean age 71), to evaluate deep-learning architectures and training regimes under both leave-one-subject-out and cross-dataset transfer. We find that improvements on younger-adult benchmarks fail to transfer equally to data collected from older adults, resulting in a persistent and often widening performance gap. However, richer representations, particularly frozen self-supervised features pretrained on the age-diverse UK Biobank dataset, substantially improve performance on data from older adults and consistently narrow the performance gap, at modest cost to younger-adult performance, though disparities remain. These findings suggest that benchmark gains and architectural scaling alone provide an incomplete picture of progress in wearable HAR, and broader advances may require representations that better capture population diversity, alongside personalized adaptation to individual movement patterns and routines.
Comments8 pages, 6 figures, to be published in Proceedings of the 2026 ACM International Symposium on Wearable Computers (ISWC '26)
