FM-FoG: A Real-Time Foundation Model-based Wearable System for Freezing-of-Gait Mitigation
This addresses a critical issue for mid-to-late stage Parkinson's disease patients by providing a generalizable, energy-efficient solution for FoG mitigation, though it is incremental as it builds on existing foundation model and activity classification methods.
The paper tackles the problem of Freezing-of-Gait (FoG) detection in Parkinson's disease patients by introducing FM-FoG, a real-time wearable system that achieves a 98.5% F1-score on unseen patients without patient-specific training, while extending battery life by up to 72% and maintaining sub-20ms latency.
Freezing-of-Gait (FoG) affects over 50% of mid-to-late stage Parkinson's disease (PD) patients, significantly impairing patients' mobility independence and reducing quality of life. FoG is characterized by sudden episodes where walking cannot start or is interrupted, occurring exclusively during standing or walking, and never while sitting or lying down. Current FoG detection systems require extensive patient-specific training data and lack generalization, limiting clinical deployment. To address these issues, we introduce FM-FoG, a real-time foundation model-based wearable system achieving FoG detection in unseen patients without patient-specific training. Our approach combines self-supervised pretraining on diverse Inertial Measurement Unit (IMU) datasets with sensor context integration. Since FoG occurs only during ambulatory activities, a lightweight CNN-LSTM activity classifier selectively activates the foundation model only during walking or standing, avoiding unnecessary computation. Evaluated on the VCU FoG-IMU dataset with 23 PD patients, FM-FoG achieves a 98.5% F1-score when tested on previously unseen patients, substantially outperforming competitive baseline methods. Deployed on a Google Pixel 8a smartphone, the system extends battery life by up to 72% while maintaining sub-20ms intervention latency. The results indicate that our FM-FoG can enable practical, energy-efficient healthcare applications that generalize across patients without individual training requirements.