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This paper is in the following e-collection/theme issue:

Development and Evaluation of Research Methods, Instruments and Tools (1347) mHealth for Data Collection and Research (1742) mHealth for Screening (467) Machine Learning (3217) Digital Biomarkers and Digital Phenotyping (452) Kinesiology (50)

Published on in Vol 10 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/76755, first published .
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Video-Based Gait Assessment Using Machine Learning to Classify Age and Sex in Low-Resource Settings: Cross-Sectional Study

Video-Based Gait Assessment Using Machine Learning to Classify Age and Sex in Low-Resource Settings: Cross-Sectional Study

Authors of this article:

Chanchanok Aramrat1, 2 Author Orcid Image ;   Poppy Alice Carson Mallinson3 Author Orcid Image ;   Papangkorn Inkaew4 Author Orcid Image ;   Pusit Seepheung1 Author Orcid Image ;   Nutchar Wiwatkunupakarn1, 2 Author Orcid Image ;   Nida Buawangpong1, 2 Author Orcid Image ;   Nick Birk3, 5 Author Orcid Image ;   Judith Lieber6 Author Orcid Image ;   Santhi Bhogadi7 Author Orcid Image ;   Hemant Mahajan8 Author Orcid Image ;   Santosh Kumar Banjara8 Author Orcid Image ;   Bharati Kulkarni8 Author Orcid Image ;   Sanjay Kinra3 Author Orcid Image ;   Chaisiri Angkurawaranon1, 2 Author Orcid Image
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Journals

  1. Foster K, Hong H, Kapur N, Mariano E. The role of emerging technology in expanding regional anesthesia education and patient access. Current Opinion in Anaesthesiology 2026;39(5):624 View