Parkinson’s Disease Detection via Bilateral Gait Camera Sensor Fusion Using CMSA-Net and Implementation on Portable Device

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 12; p. 3715
• Main Authors: Wang, Jinxuan, Huo, Hua, Liu, Wei, Zhao, Changwei, Kang, Shilu, Ma, Lan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.06.2025; MDPI
• Subjects: Aged; Algorithms; attention; Biomarkers; camera sensor; Cameras; Classification schemes; Data collection; Data processing; Datasets; Diseases; Female; Gait; Gait - physiology; Humans; Male; Mamba-2; Methods; Middle Aged; Parkinson Disease - diagnosis; Parkinson Disease - physiopathology; Parkinson's disease; Professionals; Sensors; video-based detection; Walking; attention; video-based detection; camera sensor; Mamba-2; Parkinson’s disease
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25123715

The annual increase in the incidence of Parkinson’s disease (PD) underscores the critical need for effective detection methods and devices. Gait video features based on camera sensors, as a crucial biomarker for PD, are well-suited for detection and show promise for the development of portable devices. Consequently, we developed a single-step segmentation method based on Savitzky–Golay (SG) filtering and a sliding window peak selection function, along with a Cross-Attention Fusion with Mamba-2 and Self-Attention Network (CMSA-Net). Additionally, we introduced a loss function based on Maximum Mean Discrepancy (MMD) to further enhance the fusion process. We evaluated our method on a dual-view gait video dataset that we collected in collaboration with a hospital, comprising 304 healthy control (HC) samples and 84 PD samples, achieving an accuracy of 89.10% and an F1-score of 81.11%, thereby attaining the best detection performance compared with other methods. Based on these methodologies, we designed a simple and user-friendly portable PD detection device. The device is equipped with various operating modes—including single-view, dual-view, and prior information correction—which enable it to adapt to diverse environments, such as residential and elder care settings, thereby demonstrating strong practical applicability.