Freezing of gait, a disabling symptom in Parkinson’s disease, presents a major challenge for wearable classification algorithms that struggle to distinguish pathological 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 fuses IMU channels with 3D fixation‑velocity from the headset eye tracker. With subject‑independent five‑fold cross‑validation, the IMU+Gaze model improved macro‑averaged performance – precision 0.758 vs 0.675, recall 0.867 vs 0.701, and F1 0.801 vs 0.683 – relative to IMU‑only. Gains were largest for intentional standing (F1 0.679 vs 0.390), reducing the proportion of standing windows misclassified as freezing from 63.6% (14/22) to 9.1% (2/22). 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.