ArticleScientific reports2026
VisionMD-Gait: scalable clinical gait assessment from smartphone videos.
Article in Scientific reports, 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
VisionMD-Gait enables clinical-grade gait assessment using a single frontal-view smartphone video. This open-source platform integrates state-of-the-art monocular video analysis and 3D pose estimation to compute objective gait parameters without specialized hardware, technical expertise, or sharing data with cloud-based services. We validated VisionMD-Gait against a research-grade wearable system in 24 healthy adults and 10 individuals with vestibular dizziness. Video-derived measures showed strong agreement with wearable sensors across gait speed, cadence, step duration and other clinically relevant gait measurements, with mean absolute errors under 10%. Clinical comparisons revealed significant gait alterations in patients with dizziness, despite no clinically overt gait impairments. VisionMD-Gait’s ability to process data locally, preserving patient privacy, and function in standard clinical spaces underscores its scalability and transformative potential for gait screening, fall risk assessment, and monitoring. VisionMD-Gait represents a step towards the democratization of quantitative gait analysis for clinicians and researchers seeking accessible, cost-effective, mobile health solutions.
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