3D Shape-based Body Composition Prediction Model Using Machine Learning

A booming development of 3D body scan and modeling technologies has facilitated large-scale anthropometric data collections for biomedical research and applications. However, usages of the digitalized human body shape data are relatively limited due to a lack of corresponding medical data to establish correlations between body shapes and underlying health information, such as the Body Fat Percentage (BFP). We present a novel prediction model to estimate the BFP by analyzing 3D body shapes. We introduce the concept of "visual cue" by analyzing the second-order shape descriptors. We first establish our baseline regression model for feature selection of the zeroth-order shape descriptors. Then, we use the visual cue as a shape-prior to improve the baseline prediction. In our study, we take the Dual-energy X-ray Absorptiometry (DXA) BFP measure as the ground truth for model training and evaluation. DXA is considered the "gold standard" in body composition assessment. We compare our results with the clinical BFP estimation instrument-the BOD POD. The result shows that our prediction model, on the average, outperforms the BOD POD by 20.28% in prediction accuracy.