Integrating Eye Tracking and Inertial Sensing for Enhanced Freezing of Gait Detection in Parkinson's Disease

Freezing of gait (FOG), a disabling symptom in Parkinson's disease, presents a major challenge for wearable classification algorithms that struggle to distinguish freezes from voluntary stops. To address this ambiguity, we evaluated whether incorporating eye‐gaze kinematics could improve classification accuracy compared to using ankle‐mounted inertial measurement units (IMUs) alone. We analyzed data from 10 participants performing standardized walking tasks and compared two deep learning classifiers differing only in their inputs: an IMU‐only model (bilateral ankle accelerometer and gyroscope) and an IMU + Gaze model that improved macro‐averaged F1 from 0.657 (95% bootstrap CI: 0.461–0.756) to 0.757 (0.591–0.832; Δ = 0.099, bootstrap p = 0.016). Class level improvements were largest for standing (F1: 0.600 vs. 0.356; Δ = 0.244, p = 0.019, Holm‐corrected p = 0.056), driven by recall increasing from 36.4% to 81.8%, and standing windows misclassified as freezing reduced from 59.1% (13/22) to 13.6% (3/22). These findings show that gaze kinematics complement ankle kinematics for disambiguating voluntary stopping from FOG and potentially strengthen automated monitoring, clinician‐facing assessment, and patient‐facing assistive technologies.