ArticleJournal of physical therapy science2026
Continuous video-based postural tracking for Parkinson's disease: a pose estimation approach with landmark calibration.
Article in Journal of physical therapy science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
[Purpose] We aimed to develop and validate an automated, continuous, video-based postural tracking system using pose estimation in people with Parkinson's disease. [Participants and Methods] This cross-sectional validation study included 14 people with Parkinson's disease with postural abnormalities. Participants performed a 2 min standing endurance test while being recorded using iPads. We developed Continuous Video AutoPosture for Parkinson's disease, an automated system with optional operator-guided initial-frame landmark calibration integrating MediaPipe pose estimation and DeepLab semantic segmentation to track postural angles at 1 s intervals. We measured the lumbar anterior trunk flexion, thoracic anterior trunk flexion, and lateral trunk flexion and manually calculated ground-truth postural angles from 140 sampled frames using NeuroPostureApp for validation. An optional initial-frame landmark calibration feature was implemented. [Results] Following calibration, the system demonstrated excellent reliability for lumbar (intraclass correlation coefficient=0.98) and thoracic (intraclass correlation coefficient=0.96) anterior trunk flexion and good reliability for lateral trunk flexion (intraclass correlation coefficient=0.80). Continuous tracking captured gradual postural deterioration during the standing task. [Conclusion] The system demonstrated excellent-to-good reliability and enables continuous postural tracking with superior temporal resolution compared with conventional assessments, advancing clinical evaluation and research on postural abnormalities in people with Parkinson's disease.
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