发表机构
Carl von Ossietzky Universität Oldenburg; University of Oldenburg（卡尔·冯·奥西茨基奥尔登堡大学; 奥尔登堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文评估四种开源加速度计基础模型与监督基线在19项运动相关任务的表现，发现其性能具任务依赖性，UniMTS表现最优，还提出活动轮廓推断是有前景的研究方向。
AI 中文摘要
针对大规模加速度计数据训练的基础模型（FMs）被提出作为健康监测的通用特征提取器，但关于其优势的系统性证据仍缺乏。我们首次对四种开源加速度计基础模型与监督基线模型开展全面评估，评估覆盖活动识别、日常活动、临床监测及生理推断领域的19项任务。研究发现任务依赖的性能结果：在人体动作识别（HAR）任务中，监督模型与基础模型表现相当，无一致优势；选定的基础模型在跌倒检测和压力检测任务中表现领先，且对传感器位置变化的鲁棒性最强。作为冻结特征提取器，基础模型在人口统计推断任务中表现最强，而所有模型的睡眠分期性能均接近随机水平。基础模型的内部表示在各层间表现出强相似性，凸显了未来改进基础模型的潜力。线性探测与冻结探测显示，UniMTS提供的表示最强，且是唯一在不微调的情况下超越监督基线模型的基础模型。概念发现分析表明，所有模型都能清晰捕捉高强度活动，但在久坐、复杂或模糊活动上存在困难。我们提供了基于场景的部署建议，此外，我们确定了基于基础模型的活动轮廓推断（超越固定类别分类）是一个有前景的研究方向。
英文摘要
Foundation models (FMs) trained on large-scale accelerometer data have been proposed as general-purpose feature extractors for health monitoring, but systematic evidence of their advantages is lacking. We present the first comprehensive evaluation of four open-source accelerometer FMs against supervised baselines covering 19 tasks across the domains of activity recognition including activities of daily living, clinical monitoring, and physiological inference. We find task-dependent performance results: supervised models remain competitive with FMs on human action recognition (HAR), with no consistent advantage for either, while selected FMs lead on fall and stress detection and are the most robust to sensor-placement variation. As frozen feature extractors, FMs are strongest for demographic inference, whereas sleep staging performance remains near chance level for all models. The internal FM representations show strong similarity across layers, highlighting potential for future FM improvements. Linear and frozen probing reveals that UniMTS provides the strongest representations and is the only FM that surpasses the supervised baselines without finetuning. Concept discovery analysis shows all models capture high-intensity activities clearly but struggle with sedentary, complex or ambiguous activities. We provide scenario-based deployment recommendations. Furthermore, we identify FM-derived activity profile inference-moving beyond fixed category classification-as a promising research direction.
Comments14 pages, 6 figures, 8 tables, code is available at https://github.com/AI4HealthUOL/movement-fm-benchmarking
