用双腕加速度计数据高效筛查帕金森病，少标注也能高准确率。
Label-Efficient Bilateral Attention for Parkinson's Disease Screening from Wrist-Worn IMU Signals
- 设计时间交错编码器，融合双腕传感器信号提升诊断精度
- 仅用20%标签达91.6%准确率，接近全量标注性能
- 适合资源受限的边缘设备实时部署，单窗口仅需124.5毫秒
帕金森病（PD）是一种慢性神经退行性疾病，表现为震颤、运动迟缓、姿势不稳和步态冻结等运动症状。当前诊断依赖医生临床检查，耗时且主观性强。可穿戴惯性测量单元（IMU）传感器为PD检测提供了新路径。本文提出一种时间交错编码器，处理公开的PADS数据集中的双腕IMU信号，该数据集包含469名受试者，分为三组：帕金森病（PD）、健康对照（HC）和鉴别诊断（DD）。模型在HC-vs-PD和PD-vs-DD分类任务上分别达到93.2%/90.9%准确率（0.963/0.960 AUROC）；对鉴别诊断任务的敏感度较低（0.812 vs. 0.994），表明区分PD与类似病症更具挑战。采用对比学习（InfoNCE）进行自监督表征学习，仅使用20%标签即获得91.6%/89.5%准确率，较全标签上限仅低1.5个百分点，证明极少量标注即可实现近饱和性能。针对实际部署，模型在Raspberry Pi 4上每窗口处理仅需124.5毫秒。
原文摘要 · Abstract (English)
Parkinson's disease (PD) is a chronic neurodegenerative disorder. It shows multiple motor symptoms such as tremor, bradykinesia, postural instability, and freezing of gait (FoG). PD is currently diagnosed clinically through physical examination by health-care professionals, which can be time-consuming and highly subjective. Wearable IMU sensors have become a promising gateway for PD detection. We propose a time-interleaving encoder that processes bilateral wrist-worn IMU signals from the public PADS dataset, which consists of three groups, PD (Parkinson's Disease), HC (Healthy Control), and DD (Differential Diagnosis), across a total of 469 subjects. The encoder reaches 93.2%/90.9% accuracy (0.963/0.960 AUROC) on HC-vs-PD and PD-vs-DD; the lower sensitivity on the differential (0.812 vs. 0.994) confirms that separating PD from look-alike disorders is the harder clinical problem. Self-supervised representation learning with a contrastive InfoNCE objective attains 91.6%/89.5% accuracy using only 20% of the labels, within 1.5 points of the full-label ceiling, so near-saturation accuracy is reachable with minimal annotation. For real-time edge deployment we reach 124.5 ms per window on a Raspberry Pi 4.
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