Briguglio, M., Latella, M., Borghi, S., Bizzozero, S., Imperiali, L., Wainwright, T. W., Vitale, J. A. and Banfi, G., 2025. Anthropometric Measurements from a 3D Photogrammetry-Based Digital Avatar: A Non-Experimental Cross-Sectional Study to Assess Reliability and Agreement. Applied Sciences, 15 (10), 5738.
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DOI: 10.3390/app15105738
Abstract
Photogrammetry captures and stitches multiple images together to generate a digital model of the human body, called an avatar, making it potentially useful in healthcare. Its validity for anthropometry remains to be established. We evaluated the reliability and agreement of measurements derived from a three-dimensional digital avatar generated by photogrammetry compared to manual collection. Fifty-three volunteers (34.02 ± 11.94 years of age, 64% female, 22.5 kg∙m<sup>−2</sup> body mass index) were recruited, and twenty-two body regions (neck, armpits, biceps, elbows, wrists, chest, breast, waist, belly, hip, thighs, knees, calves, ankles) were taken by an individual rater with a tape measure. Digital measurements were generated from photogrammetry. Participants’ intraclass correlation coefficients indicated strong consistency, with agreement of over 90% for limb regions such as biceps, elbows, wrists, thighs, knees, calves, and ankles, while chest and armpits showed lowest agreement (<60%). Random errors were low in limb regions, while trunk measurements showed highest errors (up to >1 cm) and variation. Bland–Altman analysis revealed wider limits of agreements and higher biases for chest (−2.44 cm), waist and belly (around −1.2 cm), and armpits (around −1.1 cm) compared to limbs. Our findings suggest that photogrammetry-based digital avatars can be a promising tool for anthropometric assessment, particularly for limbs, but may require refinement in trunk-related regions.
Item Type: | Article |
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ISSN: | 2076-3417 |
Uncontrolled Keywords: | body measures; anthropometry; physical examination; nutritional status; body composition; humanoid avatar; virtual reality; patient care |
Group: | Faculty of Health & Social Sciences |
ID Code: | 41111 |
Deposited By: | Symplectic RT2 |
Deposited On: | 13 Jun 2025 15:34 |
Last Modified: | 13 Jun 2025 15:34 |
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