Validation of Smartphone-Based Photogrammetric 3D Body Scanning for Automated Anthropometric Measurements Compared with a Commercial Depth-Sensor-Based Body Scanner

2026-08-13

AI Summary

This study addresses the lack of systematic validation regarding the accuracy and reliability of smartphone-based photogrammetry for full-body three-dimensional anthropometry. For the first time, it employs the PolyCam application to reconstruct 3D body models within a large-scale longitudinal cohort of pregnant individuals, integrating an automated pipeline for anatomical landmark identification and girth extraction. Using linear mixed-effects models, the method is rigorously compared against both Fit3D ProScanner scans and manual tape measurements across multiple dimensions. Results demonstrate a mean bias below 16 mm, intraclass correlation coefficients exceeding 0.8, Pearson correlation coefficients above 0.9, and phantom-based errors under 3.5 mm, collectively validating the approach as a feasible, low-cost, and high-precision tool for dynamic body shape monitoring with significant innovative potential.

Abstract

3D body scanning has become an important tool in healthcare applications because of its rapid and non-invasive nature. While smartphone-based photogrammetric reconstruction provide a low-cost and accessible alternative to commercial 3D body scanners, their performance for whole-body scanning remains insufficiently validated. Thus, we designed this study to comprehensively validate the photogrammetric 3D scanning application by evaluating automatically extracted whole-body measurements and longitudinal body-shape monitoring. We evaluated a representative application, PolyCam, against the commercial depth-sensor-based Fit3D ProScanner using 144 pregnant participants scanned longitudinally throughout pregnancy. We designed an automatic circumference extraction pipeline to get measurements at four anatomical landmarks from paired 3D scans. A linear mixed-effects model was used to evaluate scanner effects and longitudinal body-shape changes. Measurement consistency was assessed using repeated PolyCam scans and tape measurements on a rigid mannequin. PolyCam demonstrated strong agreement with Fit3D, with average biases below 16 mm, intraclass correlation coefficients above 0.8, and Pearson correlation coefficients above 0.9 across all landmarks. Both systems captured comparable longitudinal body-shape changes. Mannequin experiments showed mean biases below 3.5 mm and no significant differences from tape measurements. These findings support smartphone photogrammetry as a potential accessible alternative to commercial body scanners and applicable for longitudinal 3D body-shape assessment.

Problem

• smartphone photogrammetry
• 3D body scanning
• anthropometric validation
• longitudinal body-shape monitoring
• automated measurements

Innovation

• smartphone photogrammetry
• automated anthropometry
• 3D body scanning
• longitudinal shape monitoring
• circumference extraction pipeline