Bhuyan, Md Arif-Ul-Anwar2022-12-022022-12-022022-08https://hdl.handle.net/11299/250025University of Minnesota M.S. thesis. 2022. Major: Design, Housing and Apparel. Advisors: Linsey Griffin, Elizabeth Bye. 1 computer file (PDF); 124 pages.There is a research gap in using an automatic dimension extraction process to extract a large set of dimensions and different types of measurements from 3D hand scans that are developed from the physical hand. In this thesis study, an algorithm was developed, tested, and evaluated to address that gap. Twenty participants from the Human Dimensioning Lab at the University of Minnesota-Twin Cities were used. Sixty-eight Landmarks were used to extract 121 measurements (50.4% linear, 38% surface arc, and 11.6% circumference) from these scans automatically and manually in the digital medium by a measurer. Paired t-tests, mean absolute difference (MAD), and percentage of differences (diff%) were used for statistical analysis. ISO standard for maximum allowable limit is used for MAD acceptance. The automatic processing successfully measured 99.25% of all dimensions. It extracted the dimensions that the manual measuring process couldn’t measure. The automatic method extracted 87% linear, 30.4% surface-arc, and 43% circumference dimensions accurately without statistical significance. For surface-NC measurements, it measured 14 dimensions within the acceptable MAD limit. More than 50% of the dimensions that were found statistically significant had a single or double participant with a very large value that impacted the MAD value. For 60% of statistically significant dimensions, the percentage was less than 5%. For circumference measurements, 8 measurements were statistically significant and three of them have a difference percentage within 3%. Linear dimensions had statistically significant differences due to the difference in dimensions extraction procedure for breadth and depth measure between the manual and automatic measuring systems. The cause of the difference in surface-arc and circumference measurements are seemed to be similar. Thumb movement, hand posture, and scan quality were the biggest barriers to measuring these dimensions accurately. Overall, the proposed technique was performed in an acceptable form under conditions for surface-NC and circumference measurements when it is working well for linear measures. A new method is needed to validate automatic processing from the manual measuring process when they both measure from a 3D scan. Both scanning protocols, landmarking, and software need to be developed together to improve the accuracy of automatic measurements. By making the automatic process adaptive to the scan, the automatic dimension extraction process could be a game changer in 3D hand anthropometry and product design.Keywords: automatic dimension extraction, 3D hand scan, digital anthropometry, validation of measuring process, algorithm, anthroscan, manual measurement, 3D scanningen3D hand scan3D scanningalgorithmautomatic dimension extractiondigital anthropometryvalidation of measuring processThesis or Dissertation