WiFOG: Integrating deep learning and hybrid feature selection for accurate freezing of gait detection

• Published in: Alexandria engineering journal Vol. 86; pp. 481 - 493
• Main Authors: Habib, Zeeshan, Mughal, Muhammad Ali, Khan, Muhammad Attique, Shabaz, Mohammad
• Format: Journal Article
• Language: English
• Published: Elsevier 01.01.2024
• Subjects: Contactless; Freezing of Gait; Parkinson’s Disease
• ISSN: 1110-0168
• DOI: 10.1016/j.aej.2023.11.075

This study investigates the feasibility of utilizing non-invasive WiFi sensing using the 4.8 GHz operating frequency band of the 5 G spectrum, which is suitable for Internet of Things applications. We propose WiFOG: a WiFi CSI system for detecting FOG in PD leveraging deep learning and wireless channel characteristics collected by wireless devices such as a radio frequency signal generator, a network interface card, and dipole antennas. The raw data for several activities, including sitting, slow-walking, fast-walking, voluntary stopping, and FOG episodes, is collected. Regress feature engineering is performed in which discrete wavelet transforms is used for signal denoising and Hilbert-Huang transforms for feature extraction. Further, we propose hybrid feature selection techniques based on whale optimization, recursive feature elimination, and select form models for dimensionality reduction. Moreover, we propose a deep-gated recurrent network (DGRU) for activity classification and FOG detection and compared the results with the state-of-the-art approaches in the existing work. The results show our proposed scheme surpasses existing FOG detection with a total improvement of approximately 4% in accuracy and a 29% reduction in training time.