PDF(Pubmed)
Abstract：
UNASSIGNED: To evaluate the hypothesis that anthropometric dimensions derived from a person\'s manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient metabolic disease risk stratification in clinical settings.
UNASSIGNED: Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations. A new prospective sample of 84 adults had ground-truth measurements of 6 body circumferences, 7 volumes, and 7 surface areas with a 20-camera 3D reference scanner. 3D humanoid avatars were generated on these participants with manifold regression including age, weight, height, DXA %fat, and BIA impedances as potential predictor variables. Ground-truth and predicted avatar anthropometric dimensions were quantified with the same software.
UNASSIGNED: Following exploratory studies, one manifold prediction model was moved forward for presentation that included age, weight, height, and %fat as covariates. Predicted and ground-truth avatars had similar visual appearances; correlations between predicted and ground-truth anthropometric estimates were all high (R2s, 0.75-0.99; all p < 0.001) with non-significant mean differences except for arm circumferences (%D ~ 5%; p < 0.05). Concordance correlation coefficients ranged from 0.80-0.99 and small but significant bias (p < 0.05 - 0.01) was present with Bland-Altman plots in 13 of 20 total anthropometric measurements. The mean waist to hip circumference ratio predicted by manifold regression was non-significantly different from ground-truth scanner measurements.
UNASSIGNED: 3D avatars predicted from demographic, physical, and other accessible characteristics can produce body representations with accurate anthropometric dimensions without a 3D scanner. Combining manifold regression algorithms into established body composition methods such as DXA, BIA, and other accessible methods provides new research and clinical opportunities.
UNASSIGNED: Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations. A new prospective sample of 84 adults had ground-truth measurements of 6 body circumferences, 7 volumes, and 7 surface areas with a 20-camera 3D reference scanner. 3D humanoid avatars were generated on these participants with manifold regression including age, weight, height, DXA %fat, and BIA impedances as potential predictor variables. Ground-truth and predicted avatar anthropometric dimensions were quantified with the same software.
UNASSIGNED: Following exploratory studies, one manifold prediction model was moved forward for presentation that included age, weight, height, and %fat as covariates. Predicted and ground-truth avatars had similar visual appearances; correlations between predicted and ground-truth anthropometric estimates were all high (R2s, 0.75-0.99; all p < 0.001) with non-significant mean differences except for arm circumferences (%D ~ 5%; p < 0.05). Concordance correlation coefficients ranged from 0.80-0.99 and small but significant bias (p < 0.05 - 0.01) was present with Bland-Altman plots in 13 of 20 total anthropometric measurements. The mean waist to hip circumference ratio predicted by manifold regression was non-significantly different from ground-truth scanner measurements.
UNASSIGNED: 3D avatars predicted from demographic, physical, and other accessible characteristics can produce body representations with accurate anthropometric dimensions without a 3D scanner. Combining manifold regression algorithms into established body composition methods such as DXA, BIA, and other accessible methods provides new research and clinical opportunities.
摘要：
目的评估以下假设：从人的流形回归预测的三维（3D）人形化身得出的人体测量尺寸与实际周长相比是准确的，volume,以及使用地面实况3D光学成像方法获得的表面积测量。使用这种方法预测的化身，如果人体测量尺寸准确，可以用于多种目的，包括临床环境中的患者代谢疾病风险分层。方法对570名完成3D光学扫描的成年人进行流形回归3D化身预测方程，双能X射线吸收法（DXA），和生物阻抗分析(BIA)评估。一个由84名成年人组成的新的前瞻性样本对6个体围进行了真实测量，7卷，和7个具有20个摄像头的3D参考扫描仪的表面区域。在这些参与者身上生成了3D人形化身，包括年龄，体重,高度,DXA%脂肪，和BIA阻抗作为潜在的预测变量。使用相同的软件对地面实况和预测的化身人体测量尺寸进行量化。结果经过探索性研究，一个歧管预测模型被向前移动以用于呈现，包括年龄，体重,高度,和%脂肪作为协变量。预测和地面真实化身具有相似的视觉外观；预测和地面真实人体测量估计之间的相关性都很高(R2s，0.75-0.99；所有p<0.001），除手臂周长（％D〜5％；p<0.05）外，平均差异无统计学意义。一致相关系数在0.80-0.99之间，在20项人体测量中的13项，Bland-Altman地块存在很小但显着的偏差（p<0.05-0.01）。通过歧管回归预测的平均腰围与臀围比与地面实况扫描仪测量值无显着差异。结论3D化身从人口统计预测，物理，和其他可访问的特征可以在没有3D扫描仪的情况下产生具有精确人体测量尺寸的身体表示。将流形回归算法结合到既定的身体成分方法中，如DXA，BIA,和其他可获得的方法提供了新的研究和临床机会。
