发表机构
KU Leuven; Medical School Hamburg; Delft University of Technology（鲁汶大学; 汉堡医学院; 代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对居家PD患者的FOG检测，对比IMU-TCN等模型，发现IMU-TCN性能最优，预训练第一视角视频特征可补充可穿戴传感器的临床动作理解上下文。
AI 中文摘要
理解日常生活中的动作需要运动学之外的上下文信息，因为日常生活活动（ADLs）期间相似的惯性模式可能反映出有意停止、物体交互或病理性运动损伤。第一视角视觉提供了与任务相关的上下文，可帮助区分这些情况。我们通过帕金森病（PD）患者的步态冻结（FOG）检测来研究这一挑战，FOG是一种受ADLs期间上下文因素强烈影响的症状。使用从13名居家PD参与者处收集的同步第一视角视频、可穿戴惯性测量单元（IMUs）以及专家标注的FOG标签，我们在留一受试者评估设置下，评估了预训练第一视角视频和时间序列基础模型的冻结表示，以及从头训练的基于IMU的时间卷积网络（TCN）。基于IMU的TCN实现了最强的事件检测性能，F1值达42.3，AUROC达83.0，而V-JEPA2第一视角视频特征的F1值为32.6，AUROC为77.2。尽管仅第一视角视频的表现未优于基于IMU的传感，但它表现出高于随机水平的区分能力，定性分析表明，第一视角视觉可能捕获与IMUs无关的FOG相关信息。这些结果共同支持使用预训练第一视角视频表示，为基于可穿戴传感器的居家日常生活临床动作理解添加上下文信息。
英文摘要
Understanding motion in daily living requires context beyond kinematics, because similar inertial patterns during activities of daily living (ADLs) can reflect intentional stopping, object interaction, or pathological movement impairment. Egocentric vision provides task-related context that may help disambiguate these cases. We investigate this challenge through freezing of gait (FOG) detection in Parkinson's disease (PD), a symptom strongly influenced by contextual factors during ADLs. Using synchronized egocentric video, wearable IMUs, and expert-annotated FOG labels collected from 13 PD participants in their homes, we evaluate frozen representations from pretrained ego-video and time-series foundation models, alongside an IMU-based TCN trained from scratch, under leave-one-subject-out evaluation. The IMU-based TCN achieved the strongest event-detection performance, reaching 42.3 F1 and 83.0 AUROC, compared with 32.6 F1 and 77.2 AUROC for V-JEPA2 ego-video features. Although ego-video alone did not outperform IMU-based sensing, it showed above-chance discrimination, and qualitative analyses suggest that egocentric vision may capture FOG-relevant information independent of IMUs. Together, these results support the use of pretrained ego-video representations to add contextual information to wearable-sensor-based clinical motion understanding in daily living.
CommentsAccepted to ECCV Workshop 2026 (Human Motion Challenges in Real-World and Clinical Settings)
