Predicting human tactile smoothness/roughness perception from multidimensional mechanical properties of synthetic fibers using machine learning

Accurately predicting human perception of tactile roughness remains challenging because previous models often used limited mechanical properties, small sample sizes, and insufficient validation methods. To address these limitations, we developed a predictive model integrating multidimensional mechanical properties and subjective evaluations of tactile perception, using 50 commercially available synthetic fiber samples, including polyester, spandex, nylon, and their blends. Twelve mechanical properties were measured across four categories: geometric roughness, frictional force, hardness, and tensile strength. Tactile perception of smoothness/roughness was evaluated by 37 participants using a 5-point scale, with lower values indicating smoother textures and higher values indicating rougher textures. Correlation analysis identified kinetic friction coefficient (KF, ρ = -0.67), arithmetic mean roughness (Ra, ρ = 0.44), mean width of profile elements (RSm, ρ = 0.42), maximum load (ML, ρ = -0.41), and root mean square slope (Rdq, ρ = 0.31) as key predictors. Among six regression models, Gaussian process regression showed the highest predictive accuracy (cross-validated R² = 0.71). Comparisons between non-cross-validated and cross-validated results revealed substantial performance drops in cross-validation, underscoring the risk of performance overestimation without rigorous validation. The proposed framework provides a robust, generalizable approach applicable to broader tactile dimensions, benefiting material evaluation, product development, and haptic technologies.