# Study on Imagery Modeling of Electric Recliner Chair: Based on Combined GRA and Kansei Engineering
## 3 Materials and Methods
### Application Analysis of Product Design Assistant System Based on GRA and KE  
The dataset for the study was compiled from the official websites and furniture-sharing platforms of various domestic and international furniture brands. A total of 300 high-quality, clear images of electric recliner chairs in a three-quarters view were obtained as samples. To ensure the dataset's quality, a focused discussion and a pre-test were conducted by a panel of four design experts. This process aimed to eliminate poorly captured images and those showing high levels of similarity, given the substantial number of initial samples. Consequently, 121 samples of electric recliner chair shapes were selected for the study [61; 62]. The selected samples underwent preprocessing, which involved using Adobe Photoshop 2021 to standardize the images. Grayscale processing was applied, and any logos or brand identifiers were removed to eliminate potential biases caused by color and branding. Subsequently, a panel of fifteen experts, comprising eight furniture design instructors from a university, four furniture designers with more than a decade of experience, and three Ph.D. students specializing in design, were invited to participate in the study. These experts assessed the physical attributes of the electric recliner chairs in the sample set and categorized them into groups based on perceived similarities. We selected 6 representative electric recliner chairs (Figure 4). The perceptual vocabulary collection process collects user semantics, thereby refining and filtering the applicable semantics. The image vocabulary used in this study was obtained using the semantic differential method to explore users' real emotional images of the product. Through online data collection, a literature review, and interviews with users, 158 perceptual image words were obtained; the questionnaire is shown in Table 1. The selected perceptual words were matched with antonyms, and finally 18 sets of typical perceptual image semantic words were obtained that were broadly representative, as shown in Table 2[63]. Figure 4: Six designs of electric recliner chair. Based on the feedback from the user group and the expert group, a questionnaire survey was used to determine the perceptual imagery evaluation of the electric recliner. In this imagery evaluation experiment, questionnaires were randomly distributed to consumer groups of multiple age groups, and questionnaires were distributed to the test subjects (April 2023 of 6 to May 2023 of 13). The questionnaire were distributed through the Internet and user interviews. In addition, a total of 16 professionals engaged in furniture styling design were invited to help to determine the importance of key areas, including product styling designers from furniture companies and university teachers who studied furniture styling design, rating the importance of key areas of the power recliner using a 7-point Likert scale. Among them, invalid questionnaires were removed from the 98 questionnaires, and the remaining 82 questionnaires were screened out for data sorting and analysis. A total of 16 valid questionnaires were collected by the expert group, including 8 males and 8 females, as shown in Table 2. Results were obtained through factor analysis data, as shown in Table 3. The effective questionnaire proposed here was obtained by removing invalid questionnaires, such as those with short completion times and identical scores. We used the sample data statistic KMO = 0.656; the significance \(p\) value was less than 0.05, which satisfied the precondition of factor analysis [56]. Through the method of maximum variance, the composition matrix table after rotation showed that the shape of the chair represents a critical aspect of chair design due to the diverse shapes and designs. It is greatly affected by the two main components. The contribution value of component 1 to the variance was 39.217%, whereas component 2 had the highest contribution value of 34.156%.