用少量标注数据实现帕金森患者步态冻结实时检测
Wearable-Based Real-time Freezing of Gait Detection in Parkinson's Disease Using Self-Supervised Learning
- 通过自监督学习与差分窗口技术缓解数据不平衡问题
- 精度提升7.25%，准确率提高4.4%，仅需40%标签样本
- 仅在运动时激活模型，推理耗时减少67%，适合居家持续监测
LIFT-PD是一种创新的自监督学习框架，用于基于单个三轴加速度计实时检测帕金森病（PD）患者的步态冻结（FoG）。该方法通过差分窗口技术（DHWT）缓解训练中的数据不平衡问题，显著降低对大规模标注数据的依赖。同时，采用机会式推理模块，在活动期间才激活模型，有效降低能耗。在公开数据集上的大量测试表明，相较于监督模型，LIFT-PD在精度上提升7.25%，准确率提高4.4%，仅使用40%的标注样本，并将推理时间减少67%。这些成果使LIFT-PD成为一种高效、节能的居家连续监测解决方案。
原文摘要 · Abstract (English)
LIFT-PD is an innovative self-supervised learning framework developed for real-time detection of Freezing of Gait (FoG) in Parkinson's Disease (PD) patients, using a single triaxial accelerometer. It minimizes the reliance on large labeled datasets by applying a Differential Hopping Windowing Technique (DHWT) to address imbalanced data during training. Additionally, an Opportunistic Inference Module is used to reduce energy consumption by activating the model only during active movement periods. Extensive testing on publicly available datasets showed that LIFT-PD improved precision by 7.25% and accuracy by 4.4% compared to supervised models, while using 40% fewer labeled samples and reducing inference time by 67%. These findings make LIFT-PD a highly practical and energy-efficient solution for continuous, in-home monitoring of PD patients.
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