Estimating Somatotype from a Single-camera 3D Body Scanning System

CHIU, Chuang-Yuan, CIEMS, Raimonds, THELWELL, Michael, BULLAS, Alice and CHOPPIN, Simon (2021). Estimating Somatotype from a Single-camera 3D Body Scanning System. European Journal of Sport Science.

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Official URL: https://www.tandfonline.com/doi/full/10.1080/17461...
Open Access URL: https://www.tandfonline.com/doi/pdf/10.1080/174613... (Published version)
Link to published version:: https://doi.org/10.1080/17461391.2021.1921041
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    Abstract

    Somatotype is an approach to quantify body physique (shape and body composition). Somatotyping by manual measurement (the anthropometric method) or visual rating (the photoscopic method) needs technical expertise to minimize intra- and inter-observer errors. This study aims to develop machine learning models which enable automatic estimation of Heath-Carter somatotypes using a single-camera 3D scanning system. Single-camera 3D scanning was used to obtain 3D imaging data and computer vision techniques to extract features of body shape. Machine learning models were developed to predict participants' somatotypes from the extracted shape features. These predicted somatotypes were compared against manual measurement procedures. Data were collected from 46 participants and used as the training/validation set for model developing, whilst data collected from 17 participants were used as the test set for model evaluation. Evaluation tests showed that the 3D scanning methods enable accurate (mean error < 0.5; intraclass correlation coefficients >0.8) and precise (test-retest root mean square error < 0.5; intraclass correlation coefficients >0.8) somatotype predictions. This study shows that the 3D scanning methods could be used as an alternative to traditional somatotyping approaches after the current models improve with the large datasets.

    Item Type: Article
    Uncontrolled Keywords: Sport Sciences; 0913 Mechanical Engineering; 1106 Human Movement and Sports Sciences
    Identification Number: https://doi.org/10.1080/17461391.2021.1921041
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 05 May 2021 16:25
    Last Modified: 03 Aug 2021 10:00
    URI: http://shura.shu.ac.uk/id/eprint/28612

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