Extraction of Foot Anthropometric Dimensions Using a Markerless 3D Scanner Based on Big Data Analysis in East Azerbaijan Province | ||
| مهندسی مکانیک مدرس | ||
| Article 4, Volume 25, Issue 3, 1403, Pages 155-161 PDF (611.43 K) | ||
| Document Type: پژوهشی اصیل | ||
| DOI: 10.48311/mme.2025.11469 | ||
| Authors | ||
| Mahmood Azghani* ; Elham Hazrati; Amin Partovi Fard; Abolfazl Gadimii | ||
| Sahand university of Technology | ||
| Abstract | ||
| Background Anthropometry, the scientific discipline concerning precise human body measurements, plays a pivotal role across various industries, particularly in medical applications where accurate data are essential for prosthetic and orthotic design. Conventional anthropometric data acquisition methods are often time-intensive and costly. This study establishes a comprehensive anthropometric database of Iranian ethnic groups utilizing three-dimensional scanning technology coupled with a Python-based algorithm for markerless foot measurement. Methods and Materials This cross-sectional study was conducted at the Iranian Foot Anthropometry Research Center (Sahand University of Technology, Faculty of Biomedical Engineering). The study population comprised 4,312 participants (2,527 males and 1,785 females), aged 6 to 76 years (mean 36.35 ± 14.76) from East Azerbaijan Province. A Python program has been developed to extract 35 anthropometric foot indices from three-dimensional scans without the need for physical markers. For validation purposes, 400 participants were randomly selected for two-dimensional foot scanning, with five foot-length indices and two foot-width indices extracted from both two-dimensional and three-dimensional scans. An independent samples t-test was performed using SPSS 26 to assess measurement reliability. Results Statistical analysis revealed that all indices demonstrated P-values exceeding 0.05, confirming the reliability of data extracted by the Python algorithm and establishing the methodological robustness of the three-dimensional scanning approach. Conclusion This study successfully validates the reliability of a Python-based algorithm for extracting anthropometric foot indices from three-dimensional scans, providing an efficient and accurate tool for foot measurement in clinical and research applications | ||
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