## 3 Materials and Methods
### Eye-Tracking-Guided Acquisition of Product Styling Samples
#### 3.3.2 Operational Procedure of the Eye-Tracking Experiment
Specifically: Top view: Samples 3, 11, 1, 6, 9, 15, 20, 12, 8, 13; Front view: Samples 2, 14, 3, 15, 1, 7, 10, 18, 4, 16; Side view: Samples 4, 2, 8, 10, 14, 13, 9, 12, 19, 1; Bottom view: Samples 3, 2, 1, 20, 17, 5, 8, 18, 16, 12. Vectorization processing extracted industrial cleaning robot styling features [30], completing the repository construction (Figure 10). The mapping relationship between perceptual imagery semantics and stylistic design elements was established using the semantic differential method to quantify users' cognitive recognition of experimental samples and stylistic preferences. Design elements receiving higher perceptual preference ratings served as the basis for product affective styling development. Imagery scaling--a critical evaluation tool in product design research [31]--quantifies users' differential perception of stylistic attributes through semantic scales and adjective pairs. By screening antonymic adjective sets, an imagery scale matrix is constructed. Participants systematically report psychological perception intensity values induced by design styles, enabling collective cognitive variance analysis. This method helps designers understand users' psychological responses to industrial robot styling and their perceptual cognition levels, providing authentic affective references for design. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Number** & **Top** & **Front Face** & **Side Face** & **Bottom** \\ \hline Sample 1 & 0.8254 & 2.1559 & 1.2003 & 0.7275 \\ \hline Sample 2 & 0.7977 & 2.2688 & 1.2483 & 0.7345 \\ \hline Sample 3 & 0.8353 & 2.2142 & 1.1896 & 0.7483 \\ \hline Sample 4 & 0.7717 & 2.1084 & 1.2532 & 0.7131 \\ \hline Sample 5 & 0.8110 & 2.0853 & 1.1937 & 0.7213 \\ \hline Sample 6 & 0.8234 & 2.0357 & 1.1528 & 0.7046 \\ \hline Sample 7 & 0.7820 & 2.1464 & 1.1867 & 0.7043 \\ \hline Sample 8 & 0.8163 & 2.0324 & 1.2355 & 0.7201 \\ \hline Sample 9 & 0.8205 & 2.0675 & 1.2078 & 0.7140 \\ \hline Sample 10 & 0.8002 & 2.1324 & 1.2268 & 0.6917 \\ \hline Sample 11 & 0.8337 & 1.9943 & 1.1834 & 0.7113 \\ \hline Sample 12 & 0.8167 & 2.0067 & 1.2049 & 0.7157 \\ \hline Sample 13 & 0.8163 & 2.0435 & 1.2156 & 0.6949 \\ \hline Sample 14 & 0.8010 & 2.2498 & 1.2239 & 0.7099 \\ \hline Sample 15 & 0.8196 & 2.1692 & 1.1156 & 0.7096 \\ \hline Sample 16 & 0.8159 & 2.1043 & 1.1544 & 0.7172 \\ \hline Sample 17 & 0.7800 & 2.0704 & 1.1900 & 0.7219 \\ \hline Sample 18 & 0.7870 & 2.1171 & 1.1797 & 0.7195 \\ \hline Sample 19 & 0.7626 & 2.0055 & 1.2011 & 0.6805 \\ \hline Sample 20 & 0.8172 & 2.0443 & 1.1156 & 0.7251 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean fixation duration for all samples across participants. Figure 10: Design element sample library for industrial cleaning robots. Following acquisition of users' perceptual imagery evaluations for industrial cleaning robots, this study integrated Pearson correlation analysis to comprehensively assess participants' cognitive preference measurements for design element samples. Based on Extenics semantic analysis results from the user research phase, six imagery descriptors (dignified, modern, technological, agile, minimalist, elegant) were selected as measurement criteria for the imagery cognition experiment. These were semantically encoded as: A-dignified, B-modern, C-technological, D-agile, E-minimalist, F-elegant. To facilitate mapping between imagery semantics and design samples, participants evaluated both descriptor-specific attributes and overall preference for each sample. A standardized 9-point bipolar balanced scale quantified perceptual imagery cognition, with ratings ranging from \"strongly like\" (4) to \"*\"strongly dislike\" (\(-4\))*, ensuring measurement validity and data stability. Measurement scales incorporating these standards were developed using two visual stimulus boards, with industrial cleaning robot front-panel sample X-01 serving as the demonstration case (Figure 11). This experiment evaluated participants' perceptual imagery matching scores for industrial cleaning robot design samples.