Evaluation of Automated Anthropometrics Produced by Smartphone-Based Machine Learning: A Comparison with Traditional Anthropometric Assessments

Objective: The study evaluates the accuracy and reliability of smartphone-based automated anthropometric assessments compared to traditional tape measurements. It aims to determine whether mobile applications using machine learning can provide precise estimates of waist and hip circumference (WC, HC), waist-to-hip ratio (WHR), and waist-to-height ratio (W:HT) for health assessments, especially in populations with limited access to clinical care.

Implications: The study highlights the potential of mobile-based anthropometrics but also emphasizes the need for further refinements to ensure consistent accuracy across different populations and body types.

Austin J. Graybeal, Caleb F Brandner, Grant M. Tinsley (January 2023) “Evaluation of automated anthropometrics produced by smartphone-based machine learning: a comparison with traditional anthropometric assessments”National Library of Medicine – National Center for Biotechnical Information​