Dual-objective prediction of garment pressure and body contouring efficacy in shapewear: a hybrid GA-BP framework for sizing systems

Abstract

Accurate shapewear sizing is essential to maximize wearer comfort and achieve effective body-contouring outcomes, yet remains challenging due to the complex, nonlinear relationship between anthropometric data, garment pressure distribution, and subjective comfort perception. This study presents a novel application of a hybrid intelligent model that integrates backpropagation (BP) neural networks with genetic algorithm (GA) optimization to enhance the precision of size recommendations. The GA-BP framework specifically tackles the limitations of conventional sizing methods by simultaneously optimizing initial weights, activation thresholds, and network topology, effectively escaping local minima and enhancing generalization for dual-objective prediction of garment pressure and contouring efficacy. Experimental results demonstrate superior predictive performance, achieving coefficients of determination (R²) of 0.9896 (training) and 0.9854 (testing), significantly outperforming standard BP networks (0.837). Crucially, the model predicts key ergonomic indicators (e.g., localized pressure values) correlated with comfort, validated through controlled user trials. To bridge research and practice, an intelligent decision support system (DSS) was developed, featuring a C#-based GUI and MATLAB backend. This system processes anthropometric data to provide personalized sizing recommendations through an intuitive user interface. The findings confirm the effectiveness of GA-BP optimization in garment sizing and propose a user-centric decision support framework that harmonizes garment pressure with ergonomic comfort in shapewear selection.