用视觉模型模拟可穿戴设备，精准检测帕金森患者步态冻结。
Supervised Cross-Modal Feature Alignment for Zero-Wearable Freezing of Gait Detection in Parkinsonism
- 通过监督跨模态对齐，让摄像头模型学习穿戴传感器的运动特征。
- 在无传感器情况下实现85.5%准确率，平衡准确率达82.4%。
- 适合需要无感监测的临床场景，尤其适用于转圈动作检测。
帕金森病患者的步态冻结（FoG）客观评估主要依赖可穿戴惯性测量单元（IMUs），但其强制佩戴限制了连续临床应用。相比之下，非侵入式视觉方法在原地转身任务中因身体自遮挡导致骨骼坐标失真，难以捕捉高频率预警信号，分类误差显著。为突破观测物理限制，本文提出一种监督跨模态子空间蒸馏框架。训练阶段，利用预训练的IMU运动数据和临床元信息作为指导信号，引导可部署的视觉模型学习。通过引入关节速度与加速度导数，并结合置信度门控机制，有效缓解遮挡期间的跟踪误差。实证表明，该隐式对齐将硬件传感器的预测保真度直接迁移至视觉表征，最终在无传感器条件下实现85.5%准确率与82.4%平衡准确率，推理阶段仅需视觉输入。
原文摘要 · Abstract (English)
Objective assessment of Freezing of Gait (FoG) in Parkinson's disease (PD) relies predominantly on wearable Inertial Measurement Units (IMUs). While IMUs provide optimal kinematic precision, mandatory sensor attachment restricts continuous clinical deployment. Conversely, unobtrusive vision-based alternatives suffer substantial classification errors during turning-in-place tasks, where geometric self-occlusion degrades deterministic skeletal coordinates and obscures the high-frequency precursors required for FoG detection. To resolve these physical observation limits, we propose a supervised cross-modal subspace distillation framework. During optimisation, pre-trained kinematic data from IMU sensors and contextual clinical metadata act as oracles to guide a deployable visual architecture. By incorporating joint velocity and acceleration derivatives, utilising a confidence-based gating mechanism, the visual model mitigates some of the tracking errors during occlusion events. Empirical evaluations confirm this latent alignment transfers the predictive fidelity of hardware sensors directly into the visual representation, yielding $85.5\%$ accuracy, and $82.4\%$ balanced accuracy. All the while maintaining a vision only model at inference.
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