Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors

Shovito Barua Soumma; S M Raihanul Alam; Rudmila Rahman; Umme Niraj Mahi; Abdullah Mamun; Sayyed Mostafa Mostafavi; Hassan Ghasemzadeh

arXiv · 2411.11764

Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors

Shovito Barua Soumma·S M Raihanul Alam·Rudmila Rahman·Umme Niraj Mahi·Abdullah Mamun·Sayyed Mostafa Mostafavi·Hassan Ghasemzadeh

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Abstract

Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease that impairs mobility and safety by increasing the risk of falls. An effective FOG detection system must be accurate, real-time, and deployable in free-living environments to enable timely interventions. However, existing detection methods face challenges due to (1) intra- and inter-patient variability, (2) subject-specific training, (3) using multiple sensors in FOG dominant locations (e.g., ankles) leading to high failure points, (4) centralized, non-adaptive learning frameworks that sacrifice patient privacy and prevent collaborative model refinement across populations and disease progression, and (5) most systems are tested in controlled settings, limiting their real-world applicability for continuous in-home monitoring. Addressing these gaps, we present FOGSense, a real-world deployable FOG detection system designed for uncontrolled, free-living conditions using only a single sensor. FOGSense uses Gramian Angular Field (GAF) transformations and privacy-preserving federated deep learning to capture temporal and spatial gait patterns missed by traditional methods with a low false positive rate. We evaluated our system using a public Parkinson's dataset collected in a free-living environment. FOGSense improves accuracy by 10.4% over a single-axis accelerometer, reduces failure points compared to multi-sensor systems, and demonstrates robustness to missing values. The federated architecture allows personalized model adaptation and efficient smartphone synchronization during off-peak hours, making it effective for long-term monitoring as symptoms evolve. Overall, FOGSense achieved a 22.2% improvement in F1-score and a 74.53% reduction in false positive rate compared to state-of-the-art methods, along with enhanced sensitivity for FOG episode detection.

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BibTeX RIS

Shovito Barua Soumma, S M Raihanul Alam, Rudmila Rahman, Umme Niraj Mahi, Abdullah Mamun, Sayyed Mostafa Mostafavi, Hassan Ghasemzadeh. 2025-05-02. Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors. https://arxiv.org/abs/2411.11764

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