Video-Based Gait Assessment Using Machine Learning to Classify Age and Sex in Low-Resource Settings: Cross-Sectional Study Background: Gait assessment is an important tool for evaluating health risks in older adults but remains underused in low-resource settings. We explored the #feasibility of using a low-cost, simple walking protocol with smartphone video capture to extract health-related gait signals by classifying sex and age. Sex and age are fundamental biological factors linked to most health- and aging-related outcomes. Establishing baseline classification performance provides justification for future exploration of more complex health-related conditions using this protocol. Objective: This study aimed to assess whether pose parameters derived from smartphone-based gait videos can be used by machine learning models to classify age and sex. Methods: A cross-sectional study was conducted with 155 participants (Thailand: n=59, 38.1%; India: n=96, 61.9%). Participants performed a simple walking protocol while being recorded using smartphones. Pose estimation was conducted using the MediaPipe algorithm to extract 109 features related to joint distances, angles, and walking speed. For #feasibility assessment, we calculated the proportion of recordings for which pose estimation could be extracted. Elastic-net logistic regression and histogram-based gradient boosting classifiers were used for analysis. Model performance was evaluated using 5-fold cross-validation. Outcomes were sex (male vs female) and age group (aged

JMIR Formative Res: Video-Based Gait Assessment Using Machine Learning to Classify Age and Sex in Low-Resource Settings: Cross-Sectional Study #GaitAssessment #MachineLearning #HealthTech #Aging #LowResourceSettings

Redefining Macrosomia Risk Factors in Low-Resource Settings: A Cross-Sectional Study from an Ethiopian Tertiary Hospital
Koyra, H. C., Nadew, A. N. et al.
Paper
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#MacrosomiaRiskFactors #LowResourceSettings #EthiopianHealthStudy

At The Addis Clinic, we’re committed to leveraging these principles to expand access to equitable healthcare. How are you seeing AI/digital health transform low-resource settings? Share your thoughts below!

#DigitalHealth #AIforGood #GlobalHealth #LowResourceSettings #AddisClinic #WHO #AUBGHI

HIGH-Q Exchange Webinar Series 1

HIGH-Q Exchange Webinar Series #1: Ultrasound - Tues 29th April

Join the HIGH-Q Exchange's 1st #webinar on ultrasound innovations in cardiac & #maternalcare in #lowresourcesettings with @oxpop.bsky.social and Nuffield Dept of Women's & Reproductive Health

Register here: zoom.us/meeting/regi...