Anthropometry is a science concerning human body dimension measurements and proportions that is widely applied in multiple disciplines, particularly in apparel and “human-centered” product design. Designers and engineers generally use percentile anthropometric data that leads to significant errors in practice. Although adopting non-statistic body dimensions could resolve this issue, gaining detailed body measurements is a challenging task as neither manual measurement nor 3D scanning is efficient in a cost-effective manner. With the rapid development of artificial neural networks, predicting body sizes and shapes, instead of measuring them, has become a new trend. This work presents a unique Generalized Regression Neural Network architecture that is capable of accurately predicting 76 detailed body measurements from seven easily measured body features with high tolerance to input measurement errors. The proposed model outperforms the existing regression models and can be easily implemented in the design process.

人体测量学是一门关于人体尺寸测量和比例的科学，广泛应用于多个学科，特别是在服装和“以人为本”的产品设计中。设计师和工程师通常使用百分位人体测量数据，这在实践中会导致重大错误。虽然采用非统计身体尺寸可以解决这个问题，但获得详细的身体测量是一项具有挑战性的任务，因为手动测量和 3D 扫描都无法以经济高效的方式进行。随着人工神经网络的快速发展，预测身体大小和形状而不是测量它们已成为一种新趋势。这项工作提出了一种独特的广义回归神经网络架构，能够从七个易于测量的身体特征中准确预测 76 个详细的身体测量结果，并且对输入测量误差具有很高的容忍度。所提出的模型优于现有的回归模型，并且可以在设计过程中轻松实现。