Table 3 Root-mean-squared errors (RMSE) for predicted percent fat (PFAT) and visceral fat (VFAT) of all current 3D-optical body composition prediction literature on Shape Up! Adults compared to the 3DAE-GPR prediction of the d = 301 and d = 4284 models using the most accurate feature layer identified in Fig. 1

From: 3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology

Paper

N test meshes

PFAT RMSE (%)

VFAT RMSE (kg)

Ng et al. (2019) Anthro only

M: 177

F: 230

M: 4.03

F: 3.99

M: 0.15

F: 0.14

Ng et al.13

M: 177

F: 230

M: 3.55

F: 3.88

M: 0.14

F: 0.13

Tian et al.16

M: 31

F: 39

M: 3.90

F: 3.29

M: 0.15

F: 0.17

Wong et al.27

M: 159

F: 202

M: 2.73

F: 3.46

M: 0.13

F: 0.13

Tian et al.14

M: 182

F: 248

M: 3.24

F: 4.22

M: 0.12

F: 0.14

PCA-GPR M391/F45714

M: 182

F: 248

M: 2.79

F: 3.09

M: 0.13

F: 0.12

PCA-GPR 4284

M: 181

F: 239

M: 2.68

F: 2.85

M: 0.11

F: 0.11

3DAE-GPR 4284

M: 181

F: 239

M: 2.50

F: 2.81

M: 0.11

F: 0.11