Intelligent evaluation model based on multi-dimensional dynamic measurement and comfort threshold of pressure for homewear

In response to the insufficient objective assessment of pressure comfort in homewear, this study employs an intelligent algorithm to develop a model for evaluating and predicting comfort based on dynamic and static pressure thresholds. The model analyzes the pressure exerted on elbows, waist, buttocks and knees during movement, and determines the comfort threshold of clothing pressure by combining subjective and objective methods. Then, the predictive potential of RBF neural network for dynamic pressures in homewear is explored. The findings indicate that the clothing pressure comfort thresholds for the four body parts were as follows: 1.125-1.355, 1.690-2.198, 2.450-2.805, and 5.325-6.55 kPa. Furthermore, the average prediction error of the RBF model for garment pressure across all four body parts was under 7%. This research provides a method for evaluating the pressure comfort of homewear based on a design scheme and offers framework for reverse optimization of the design.