Label-Efficient Bilateral Attention for Parkinson's Disease Screening from Wrist-Worn IMU Signals

Abstract

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.