Markerless Motion Capture Reveals Movement Abnormalities in Isolated REM Sleep Behavior Disorder
Markerless Motion Capture Reveals Movement Abnormalities in Isolated REM Sleep Behavior Disorder
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Publication status: preprint
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Original abstract
Objective: and scalable approaches for detecting subtle motor impairment in isolated REM sleep behavior disorder (iRBD), a prodromal stage of Parkinson’s disease, remain limited. We investigated whether markerless motion capture from single RGB-camera videos can identify gait abnormalities in people living with iRBD and provide interpretable digital biomarkers. We retrospectively analyzed 93 standardized walking videos from three clinical sites. Human pose estimation extracted 12 body markers and 14 kinematic time series. Thirty-five machine learning approaches classified healthy controls (HC) and people with iRBD. The Movement Disorder Society Unified Parkinson’s Disease Rating Scale Part 3 (MDS-UPDRS III) served as the clinical baseline. The best-performing model (tsfresh+XGBoost) achieved an AUROC of 0.739, significantly outperforming the MDS-UPDRS III sum score when trained on data from all three sites. Harmonized multi-site training improved performance. SHAP identified hip-related temporal features as key contributors, which differed between groups and showed stronger associations with regional dopaminergic deficits than clinical scores. Single-camera gait analysis may provide scalable digital biomarkers for low-cost screening and monitoring of prodromal PD. Plain Language Summary People with isolated REM sleep behavior disorder (iRBD) often show subtle changes in their walking that can be difficult to detect during routine clinical examinations. In this study, we used artificial intelligence to analyze videos of a simple walking task without requiring wearable sensors or body markers. The computer-based analysis distinguished people with iRBD from healthy individuals more accurately than standard clinician-rated motor scores. While further validation in larger studies is needed, these findings suggest that video-based movement analysis could become a useful objective tool to support the assessment of people at increased risk of Parkinson’s disease and related disorders.