Intelligent assessment of pressure in women’s loungewear based on machine learning algorithms

Abstract

Since the dynamic and static scenarios of women’s loungewear involve multiple parts of bodies, it becomes a major factor in the assessment of comfort to measure dynamic pressure in loungewear. This study established a mathematical model for intelligent prediction of clothing pressure with 14 parameters based on fabric properties and shape size. Combining major influencing factors of clothing pressure, this model measures the clothing pressure exerted on the elbows, waist, buttocks, and knees in three scenes and seven postures, to study the predictive performance of support vector regression (SVR), backpropagation neural network (BPNN), and genetic algorithm (GA)-BPNN for dynamic pressure in women’s loungewear. According to the results, the accuracy of the three machine learning algorithms in the prediction of clothing pressure in loungewear, in descending order, is GA-BPNN, BPNN, and SVR. With complex influencing factors and limited sample sizes, the average relative errors of GA-BPNN for predicting the pressure on four body parts are 2.87%, 3.55%, 3.36%, and 4.35%, respectively, which can yield a science-based reference for the assessment of comfort in women’s loungewear.