Accuracy of Anthropometric Measurements by a Video-based 3D Modelling Technique

CHIU, Chuang-Yuan, THELWELL, Michael, GOODWILL, Simon and DUNN, Marcus (2020). Accuracy of Anthropometric Measurements by a Video-based 3D Modelling Technique. In: Computer Methods, Imaging and Visualization in Biomechanics and Biomedical Engineering: Selected Papers from the 16th International Symposium CMBBE and 4th Conference on Imaging and Visualization, August 14-16, 2019, New York City, USA. Springer.

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The use of anthropometric measurements, to understand an individual’s body shape and size, is an increasingly common approach in health assessment, product design, and biomechanical analysis. Non-contact, three-dimensional (3D) scanning, which can obtain individual human models, has been widely used as a tool for automatic anthropometric measurement. Recently, Alldieck et al. (2018) developed a video-based 3D modelling technique, enabling the generation of individualised human models for virtual reality purposes. As the technique is based on standard video images, hardware requirements are minimal, increasing the flexibility of the technique’s applications. The aim of this study was to develop an automated method for acquiring anthropometric measurements from models generated using a video-based 3D modelling technique and to determine the accuracy of the developed method. Each participant’s anthropometry was measured manually by accredited operators as the reference values. Sequential images for each participant were captured and used as input data to generate personal 3D models, using the video-based 3D modelling technique. Bespoke scripts were developed to obtain corresponding anthropometric data from generated 3D models. When comparing manual measurements and those extracted using the developed method, the accuracy of the developed method was shown to be a potential alternative approach of anthropometry using existing commercial solutions. However, further development, aimed at improving modelling accuracy and processing speed, is still warranted.

Item Type: Book Section
Additional Information: Series ISSN: 2212-9391
Identification Number:
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 11 Mar 2020 10:34
Last Modified: 01 Apr 2021 01:18

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