PGCLN：一种基于物理原理的个性化最优坐姿预测方法，利用压力分布

To bridge the gap between physiological data and engineering implementation, this study addressed the limitation of indirect mapping of sitting posture by establishing a direct predictive link between physiological information from pressure distribution and optimal sitting posture. A novel predicting framework named Physics-Guided and Contrastive Learning Network (PGCLN) was proposed to predict sitting posture using 12 pressure distribution features and seven anthropometric data, and designed to overcome small-sample and high-dimensional regression problems. Crucially, Contact Pressure Distrib