发表机构
NeuroAI Fusion Labs（神经AI融合实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对帕金森病冻结步态检测中可穿戴设备不便与视觉方法精度不足的问题，提出监督跨模态蒸馏框架，利用IMU数据引导视觉模型，实现85.5%准确率且推理时仅需视觉输入。
AI 中文摘要
帕金森病（PD）中冻结步态（FoG）的客观评估主要依赖于可穿戴惯性测量单元（IMUs）。虽然IMUs提供最佳的动力学精度，但强制性的传感器附着限制了持续的临床部署。相反，不显眼的基于视觉的替代方案在转身任务中遭受显著的分类误差，其中几何自遮挡降低了确定性骨骼坐标，并掩盖了FoG检测所需的高频前兆。为解决这些物理观测限制，我们提出了一种监督跨模态子空间蒸馏框架。在优化过程中，来自IMU传感器的预训练动力学数据和上下文临床元数据作为引导者，指导可部署的视觉架构。通过结合关节速度和加速度导数，利用基于置信度的门控机制，视觉模型减轻了遮挡事件期间的一些跟踪误差。实证评估证实，这种潜在对齐将硬件传感器的预测保真度直接转移到视觉表示中，实现了85.5%的准确率和82.4%的平衡准确率。同时，在推理时仅保持视觉模型。
英文摘要
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.
Comments10 pages, 4 figures
