# Study on Imagery Modeling of Electric Recliner Chair: Based on Combined GRA and Kansei Engineering
###### Abstract
Emotional design encapsulates customers' psychological responses, intricately woven into the nuances of product design. Nevertheless, integrating these emotional prerequisites into product design remains a formidable challenge due to linguistic complexities. Kansei engineering (KE) has emerged as an effective tool for the translation of emotional needs into product design elements, employing various techniques, including genetic algorithms, neural networks, and fuzzy sets [13]. While prior research has predominantly focused on establishing the viability of employing neural network models within the realm of product design, there remains a significant void in the scholarly landscape concerning the convergence of consumer perception and neural network models in the context of product design. This study seeks to address this scholarly gap by elucidating the influence of integrating Kansei engineering and gray relational analysis (GRA) on the practice of product design. The primary objective of this research is to provide empirical evidence showcasing the effects of the aforementioned integration on the product design process [14]. At the core of the amalgamation of Kansei engineering and GRA is the establishment of a product design perception evaluation system. The analytical capabilities of GRA are harnessed by this system to evaluate the effectiveness of product designs. To exemplify the practical application of this evaluative approach, an electric recliner chair is considered as a case study. Through a comprehensive and rigorous analysis of this specific case, significant insights into the tangible real-world impact of integrating Kansei engineering and GRA in the field of product design can be gleaned [15]. Kansei engineering and perceptual engineering are at the forefront of product design, emphasizing the crucial role of user perception and emotion. By addressing the challenges of integrating emotional design into product development, these disciplines are reshaping the design landscape, ensuring that products seamlessly resonate with consumers' emotional needs and perceptions. The potential for these approaches to revolutionize product design is substantial, bridging the gap between human emotions and inanimate objects, ultimately enhancing the user experience [16]. The fusion of gray relational analysis (GRA) and KE provides a robust framework for product designs. Subsequently, GRA is utilized to identify the primary upper Kanseifactors and generate corresponding morphological analysis maps for the Kansei factors that exhibit the highest gray correlation. Subsequently, fuzzy quality function deployment is applied to create a mapping model that connects essential Kansei factors to product-specific forms, ultimately culminating in the derivation of an enhanced design solution for an electric recliner chair. These insights serve as guiding principles for the subsequent phases of the product design process (Figure 1). The procedural framework for the GRA fuzzy logic modeling in product image styling design is illustrated in Figure 1. Drawing upon GRA fuzzy theory and integrating it with the empirical protocol of Kansei engineering, we initially identify the most influential elements within the design morphology of the product. Subsequently, employing the Likert seven-point scale method, we evaluate the product images of experimental samples. The design elements exhibiting a pronounced influence are then designated as input fuzzy sets, while the evaluative values of the product imagery constitute the output fuzzy sets. Triangular fuzzy functions for both input and output linguistic variables are subsequently derived [17]. The Matlab platform is utilized to implement the fuzzy logic model, involving the construction of a fuzzy control data table and the formulation of fuzzy rules. The Center of Maximum (CoM) method is applied for the defuzzification of fuzzy numbers.