Artificial intelligence-based precise prediction of anthropometric data for female garment pattern-making

Abstract

Anthropometric data form the cornerstone of garment pattern-making. This article introduces an artificial intelligence-driven approach, employing a back-propagation artificial neural network (BP-ANN), to predict the anthropometric data essential for crafting patterns for women’s upper tops. The model adeptly processes minimal critical data from women’s upper bodies, yielding projected dimensions that are arduous to manually measure yet crucial for tailoring body-fitting tops. Utilising a three-dimensional body scanner for accurate anthropometric data collection from 196 women in Sichuan Province, China, our study compares the BP-ANN model with a Linear Regression (LR) model. Results demonstrate superior predictive accuracy for BP-ANN. Notably, the BP-ANN model excels in efficiency and accuracy, particularly in challenging anthropometric parameters. The findings underscore the transformative potential of AI-based models in optimizing garment production processes, offering a precise alternative to traditional methods. This research contributes valuable insights for the integration of AI technology in advancing pattern-making practices.