A Data-Driven Iterative Design Optimization Approach for Smart Textile Installations Based on Physical Prototype Feedback

Abstract

This study proposes a case study-based approach to optimizing smart textile installation design by applying the Kano model to user feedback derived from physical prototypes. Taking an optical fiber art installation as the experimental subject, the research employs questionnaire surveys and Kano model analysis to categorize 17 design attributes and prioritize them using sensitivity coefficients. This process delineates a detailed pathway for design refinement, demonstrating the relationship between prototype feedback and design optimization. The results establish a “prototype-to-optimization” framework that mitigates user dissatisfaction risks in large-scale smart installations and offers practical strategies for enhancing the synergy between technology and design.