Parkinson's Disease Detection via Self-Supervised Dual-Channel Cross-Attention on Bilateral Wrist-Worn IMU Signals

• URL: http://arxiv.org/abs/2604.18372v1
• Date: Mon, 20 Apr 2026 15:00:44 GMT
• Title: Parkinson's Disease Detection via Self-Supervised Dual-Channel Cross-Attention on Bilateral Wrist-Worn IMU Signals
• Abstract summary: We propose a self-supervised cross-attention encoder that processes bilateral wrist-worn IMU signals from a public dataset called PADSNCE.<n>We have achieved a mean accuracy of 93.12% for HC vs. PD classification and 87.04% for PD vs. DD classification.<n>Results emphasize the clinical challenge of distinguishing Parkinson's from other neurodegenerative diseases.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parkinson's disease (PD) is a chronic neurodegenerative disease. It shows multiple motor symptoms such as tremor, bradykinesia, postural instability, freezing of gait (FoG). PD is currently diagnosed clinically through physical exam by health-care professionals, which can be time consuming and highly subjective. Wearable IMU sensors has become a promising gateway for passive monitoring of PD patients. We propose a self-supervised cross-attention encoder that processes bilateral wrist-worn IMU signals from a public dataset called PADS, consisting of three groups, PD (Parkinson Disease), HC (Healthy Control) and DD (Differential Diagnosis) of a total of 469 subjects. We have achieved a mean accuracy of 93.12% for HC vs. PD classification and 87.04% for PD vs. DD classification. The results emphasize the clinical challenge of distinguishing Parkinson's from other neurodegenerative diseases. Self-supervised representation learning using contrastive infoNCE loss gained an accuracy of 93.56% for HC vs. PD and 92.50% for PD vs. DD using only 20% of labelled data. This demonstrates the effectiveness of our method in transfer learning for clinical use with minimal labels. The real-time applicability was tested by deploying the optimized model with a mean inference time of 48.32 ms per window on a Raspberry Pi CPU.

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