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.
Data availability
The datasets used and/or analyzed during the current study are available at (https://github.com/mea-lab/GaitValidation.
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Funding
DLG received funding from Intelligent Clinical Care Center of the University of Florida as part of the 2024 AI2Heal Catalyst Grant Award, and the Norman Fixel Institute for Neurological Diseases as part of The Fixel-Eagles Pilot Grant Program and Fixel Institute Early Researcher Catalyst Award.
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S.L.: Writing, review & editing, Visualization, Formal analysis, Data acquisition, Data curation. A.W.: Software development, Methodology. S.C.: Writing—review & editing, Methodology, Data acquisition, Conceptualization. P.J.A.: Writing—review & editing, Methodology, Data acquisition, Conceptualization. D.L.G: Writing—review & editing, Writing—original draft, Visualization, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.
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The study was approved by the University of Florida’s Institutional Review Board, ethical approval number: IRB202302162. This study was conducted in accordance with the principles of the Declaration of Helsinki, all participants provided written informed consent prior to enrollment.
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Liu, S., Wong, A., Chen, S. et al. VisionMD-Gait: scalable clinical gait assessment from smartphone videos. Sci Rep (2026). https://doi.org/10.1038/s41598-025-34912-5
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DOI: https://doi.org/10.1038/s41598-025-34912-5
