Toward Symmetry in Accessible Restrooms Design: Integrating KE, RST, and SVM for Optimized Emotional-Functional Alignment

Accessible restrooms must reconcile code-based functionality with the affective expectations of disabled users. This study develops an integrated Kansei Engineering (KE)–Rough Set Theory (RST)–Support Vector Machine (SVM) workflow that converts user emotions into verifiable design guidelines. Surveys and semi-structured interviews with 50 disabled participants produced nine Kansei words; factor analysis extracted three principal emotional factors—tidiness, utility and care—capturing 75.8% of total variance. The morphological decomposition of 60 restroom samples yielded 41 design attributes, from which RST attribute reduction isolated six critical features. An SVR model with a radial-basis kernel, trained on 90% of the data and validated on the remaining 10%, achieved R2 = 0.931 and RMSE = 0.085. The exhaustive prediction of 15,750 feasible design combinations pinpointed an optimal configuration; follow-up user testing confirmed the improvement in satisfaction (mean 5.1 on a seven-point scale). The KE–RST–SVM workflow thus offers a reproducible, data-driven path for harmonizing emotional and functional objectives in inclusive restroom design, and can be extended to other barrier-free facilities.