FM-FoG: A Real-Time Foundation Model-based Wearable System for Freezing-of-Gait Mitigation

Freezing of Gait (FoG) is one of the most debilitating symptoms of Parkinson's disease (PD), affecting over half of patients and severely impairing mobility and independence. Effective management requires both real-time FoG detection for reactive intervention and pre-FoG detection for proactive prevention before freezing fully manifests. However, existing methods rely on massive labeled data and require patient-specific calibration, restricting scalability and clinical deployment. We propose FM-FoG, a domain-specific foundation model designed for real-time FoG management without requiring individual patient-specific training. FM-FoG uses body-worn IMU sensors for gait monitoring, wearable vibrotactile actuators for delivering intervention cues, and a smartphone for real-time model inference. FM-FoG is pretrained in a self-supervised manner on approximately 73 hours of unlabeled IMU recordings from 170 subjects across diverse datasets, enabling generalizable motion representations that support two downstream tasks: real-time FoG detection and predictive pre-FoG detection. However, directly applying foundation models to wearable sensing presents two real-world challenges: (i) heterogeneous sensor configurations, and (ii) energy constraints in continuous operation. To address the first challenge, FM-FoG incorporates sensor-context embeddings that enable adaptive alignment across heterogeneous sensor locations. To address the second challenge, FM-FoG adopts an event-triggered activation strategy to limit inference to ambulatory periods and reduce unnecessary computation. Deployed on a Google Pixel 8a smartphone and evaluated under cross-patient protocols on our VCU FoG-IMU dataset of 23 PD patients with clinically induced FoG, FM-FoG achieves 98.5% F1 for FoG detection and > 85% F1 for 1-3s pre-FoG detection on completely unseen test subjects, and 96.9% and 93.8% respectively under leave-one-subject-out cross-validation, exceeding state-of-the-art models by up to 15%, while extending battery life by 43% at clinically representative activity levels with sub-20 ms latency, enabling practical, energy-efficient, and patient-independent FoG monitoring.