Parkinson's Disease Detection Using Multiscale Frequency-Sharing Channel Attention Network With Smartwatch Movement Recordings

Mingzhi Wang, Junxin Chen, Jiawei Du, Yongfei Wu, Jun Mou, David Camacho

Abstract: 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 multiscale 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 multiscale 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, noninvasive tool for real-time PD monitoring through wearable devices.

External IDs:dblp:journals/iotj/WangCDWMC25