Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors
Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease that significantly impacts mobility and quality of life. This paper presents FOGSense, a novel detection system using Gramian Angular Field transformations and federated deep learning to identify FOG episodes in real-world settings. Tested on the 'tdcsfog' dataset, FOGSense shows significant improvements in accuracy (10.4%) and F1-score (22.2%) over existing methods, while offering robust performance with missing data and personalized adaptation as symptoms evolve.
Apr 1, 2025
FOGSense
A novel Freezing of Gait detection system for Parkinson’s disease patients utilizing Gramian Angular Field transformations and federated deep learning with wearable sensors data. The system achieves 86.99% accuracy with multi-channel CNN processing of accelerometer data.
Apr 1, 2025