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

arXiv: https://arxiv.org/abs/2411.11764 · HTML: https://mlsanity.com/paper/2411.11764/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [Convergence of Edge Computing and Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/1907.08349/) (2019)
- [Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables](https://mlsanity.com/paper/1604.08880/) (2016)
- [Imaging Time-Series to Improve Classification and Imputation](https://mlsanity.com/paper/1506.00327/) (2015)
- [AI Benchmark: Running Deep Neural Networks on Android Smartphones](https://mlsanity.com/paper/1810.01109/) (2018)
- [Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://mlsanity.com/paper/1911.05642/) (2019)
- [WAFFLE: Weighted Averaging for Personalized Federated Learning](https://mlsanity.com/paper/2110.06978/) (2021)
- [Self-Supervised Learning and Opportunistic Inference for Continuous Monitoring of Freezing of Gait in Parkinson's Disease](https://mlsanity.com/paper/2410.21326/) (2024)
- [ECG classification using Deep CNN and Gramian Angular Field](https://mlsanity.com/paper/2308.02395/) (2023)
- [Freezing of Gait Prediction From Accelerometer Data Using a Simple 1D-Convolutional Neural Network -- 8th Place Solution for Kaggle's Parkinson's Freezing of Gait Prediction Competition](https://mlsanity.com/paper/2307.03475/) (2023)

## Built on by (most cited first, 4 of 4)

- [Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals](https://mlsanity.com/paper/2504.06987/) (2025)
- [Evaluating YOLO Architectures: Implications for Real-Time Vehicle Detection in Urban Environments of Bangladesh](https://mlsanity.com/paper/2509.05652/) (2025)
- [Label-Efficient Bilateral Attention for Parkinson's Disease Screening from Wrist-Worn IMU Signals](https://mlsanity.com/paper/2604.18372/) (2026)
- [Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability](https://mlsanity.com/paper/2608.02238/) (2026)

## Abstract (arXiv)

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.