Parkinson’s Disease Detection Using Multi-Scale Frequency-Sharing Channel Attention Network With Smartwatch Movement Recordings

Diagnosing Parkinson’s Disease (PD) remains challenging due to its complex motor symptoms and the reliance on subjective clinical evaluations. To address this issue, this study proposes the Multi-Scale Frequency-Sharing Attention Network (MSF-CANet), an end-to-end framework designed to identify PD and healthy control subjects using smartwatch-based inertial sensor data. MSF-CANet integrates a multi-scale perception module to capture temporal features of tremors at different frequencies, a frequency-aware module to enhance PD-specific tremor signals within the 3–7 Hz range, and a shared channel attention mechanism to focus on key sensor channels while ensuring computational efficiency. The model was trained and evaluated on the PADS dataset using nested 5-fold cross-validation. The proposed method achieved an accuracy of 92.39% and an AUC of 0.9797, outperforming existing methods. The findings indicate that dual-hand data significantly improves detection performance compared to single-hand data, and dynamic tasks like ‘Drink from Glass’ and ‘cross and extend both arms’ achieved higher accuracy than static activities. These findings underscore the potential of MSF-CANet as a robust, non-invasive tool for real-time PD monitoring through wearable devices.