To enhance user interaction experience in furniture customization, this study optimizes an Internet of Things (IoT)-driven Artificial Intelligence (AI)-assisted design system. First, the study analyzes human-computer interaction theories in IoT environments. Second, a personalized furniture design model based on a Generative Adversarial Network (GAN) is constructed. This enhances the AI-assisted design system's ability to generate diverse design solutions while avoiding the limitations of traditional systems. Compared to other deep learning architectures (e.g., encoder-decoder networks), GAN excels in generating realistic and creative furniture design solutions. Finally, virtual reality (VR) technology is integrated to enable real-time interaction between users and customized furniture. The Kano model is used to evaluate the interactive features of the furniture. The results show that in the proposed interactive furniture customization system, female users prioritize comfort, convenient control functions, and safety. They also expect a smooth and intuitive interaction experience. Male users focus more on convenient control functions, visualization features, and safety, with Proportion of Attractive Quality (PA) scores of 60.80%, 56.32%, and 73.18%, respectively. Younger users significantly value visualization features and convenient control functions while also emphasizing safety. Middle-aged and elderly users prioritize operational functionality and comfort, with relatively lower demand for social and entertainment features. In terms of income levels, low-income users mainly focus on comfort, operational functionality, and safety, with PA values of 60.12%, 66.21%, and 72.35%, respectively. Middle-income users show higher demand for visualization features, with a PA value of 55.21%. High-income users emphasize safety and comfort more. The designed system effectively highlights the preferences of users across different genders, age groups, and income levels, enabling flexible design adjustments based on user characteristics. This method better meets the personalized needs of diverse users while addressing the limitations of traditional AI-assisted design systems in generating diverse solutions. It provides new insights for smart furniture design, enhancing adaptability and flexibility, and promoting technological innovation and interdisciplinary integration. This study holds significant academic value and practical application prospects.
Xiaohong Jiang (Thu,) studied this question.