Validation and Reliability of Sizestream 3D scanner using Regression modeling

Abstract

3D scanning has evolved as one of the most advanced and accurate technology to measure humans and products. Quick and reliable results achieved by 3D scanning over manual measurements, make it the most preferred tool for measurement. 3D scanning has been extensively used in various national sizing surveys worldwide. The 3D scan results are compared to the manual measurements to establish the accuracy of the scanner. This research paper describes a novel and alternative approach to check the reliability and validity of the measures derived from 3D Body Scanner in comparison to measures provided by the manual measures.The 3-D Body scanning was done by Sizestream 3D Body Scanner - SS14. The manual body measurements were taken by experienced experts using an anthropometer, stadiometer, and certified flexible non-stretchable steel tape. In total 133 subjects (68 male and 65 female subjects) covering 102 body dimensions were taken manually and were used while comparing 3D scan measurements to establish the validity and reliability of the scanner. The procedure adopted for validation and reliability check for the 3D scanner was as prescribed in the ISO 20685(2005) and ISO 20685(2018). It was observed that the Sizestream - SS14 scanners used were highly consistent in measuring the subjects, though a systematic error was reported in the process failing some of the measurements in terms of accuracy levels (as per ISO 8559 and ISO 20685(2005)) achieved against manual measurements used as the gold standards. Subsequently, a novel approach based on regression modeling was applied to establish scanning accuracy.This research paper describes the validation and reliability procedure as per ISO protocols. It also discusses the regression-based statistical procedure adopted to confirm the desired measurement accuracy of the scanners within the permissible error limits of ISO 20685and ISO 8559.

Keywords: 3D Body Scanning, Validation, and Reliability, Scanner Consistency, Intra-class Correlation (ICC), Regression Modeling

DOI: 10.54941/ahfe1001552