CRADLE: A Clinically Robust, Anatomy-Aware Post-Processing Framework for Infant GMA Landmark Tracking in 2D Videos

Accurate pose estimation is central to automated infant General Movements Assessment during the fidgety period, when subtle limb movements, particularly at distal joints inform neurodevelopmental risks. Robust 2D pose tracking from handheld videos remains challenging in real-world settings, where occlusion, rapid motions, and visually ambiguous smaller joints frequently compromise anatomical accuracy. We present CRADLE, a clinically motivated, anatomy-aware post-processing pipeline designed to refine infant 2D movement trajectories across 24-anatomocal landmarks detected by our DeepLabCut-trained model. CRADLE integrates segment-length constraints, velocity-based anomaly detection, anatomically constrained interpolation, and Kalman filtering to correct both large localization failures and subtle persistent joint misplacements without relying primarily on confidence scores. Evaluations against conventional Confidence-Thresholding using Mean Absolute Error (MAE), {Delta}MAE, average Percentage of Correct Keypoints, and net keypoint correction rate showed consistently reduced or preserved error while maintaining accurate trajectories, with the strongest gains achieved at clinically important distal joints. Mean improvements reached up to 5 pixels for some smaller distal landmarks, large-magnitude corrections occurred more often than with Confidence-Thresholding, and well-localised joints remained largely unaffected. Positive net correction rates across metacarpophalangeal and metatarsophalangeal distal-landmarks further confirmed a favourable correction-degradation balance. By improving pose trajectory quality, CRADLE enhances the reliability of downstream movement analysis.