Modification and refinement of three-dimensional reconstruction to estimate body volume from a simulated single-camera image

CHIU, Chuang-Yuan, DUNN, Marcus, HELLER, Ben, CHURCHILL, Sarah and MADEN-WILKINSON, Tom (2022). Modification and refinement of three-dimensional reconstruction to estimate body volume from a simulated single-camera image. Obesity Science and Practice.

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Official URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/os...
Link to published version:: https://doi.org/10.1002/osp4.627
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    Abstract

    Objective Body volumes (BV) are used for calculating body composition to perform obesity assessments. Conventional BV estimation techniques, such as underwater weighing, can be difficult to apply. Advanced machine learning techniques enable multiple obesity-related body measurements to be obtained using a single-camera image; however, the accuracy of BV calculated using these techniques is unknown. This study aims to adapt and evaluate a machine learning technique, Synthetic Training for Real Accurate Pose and Shape (STRAPS), to estimate BV. Methods The machine learning technique, STRAPS, was applied to generate three-dimensional (3D) models from simulated two-dimensional (2D) images; these 3D models were then scaled with body stature and BV were estimated using regression models corrected for body mass. A commercial 3D scan dataset with a wide range of participants (n = 4,318) was used to compare reference and estimated BV data. Results The developed methods estimated body volume with small relative standard errors of estimation (< 7%) although performance varied when applied to different groups. The BV estimated for people with body mass index (BMI) < 30 kg/m2 (1.9% for male and 1.8% for female) were more accurate than for people with BMI ≥ 30 kg/m2 (6.9% for male and 2.4% for female). Conclusions The developed method can be used for females and males with BMI < 30 kg/m2 in BV estimation and could be used for obesity assessments at home or clinic settings.

    Item Type: Article
    Identification Number: https://doi.org/10.1002/osp4.627
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 10 Jun 2022 11:26
    Last Modified: 23 Jun 2022 08:52
    URI: http://shura.shu.ac.uk/id/eprint/30304

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