## 3 Materials and Methods
### Eye-Tracking-Guided Acquisition of Product Styling Samples
#### 3.3.2 Operational Procedure of the Eye-Tracking Experiment
To quantitatively assess visual attention distribution across industrial cleaning robot components, these regions were formally designated as areas of interest (AOIs) based on robotic morphology and aggregate eye-tracking data: Top AOI 1, Front Fascia AOI 2, Side Profile AOI 3, and Base AOI 4 (Figure 7). Figure 5: Some experimental scenes of the eye movement experiment. This study utilized the Velocity-Threshold Identification (I-VT) algorithm in Tobii Studio to detect fixations. The algorithm calculates the angular velocity between consecutive gaze samples and classifies segments with velocities below 30\({}^{\circ}\)/s as candidate fixations. These candidates were then filtered by a minimum duration threshold of 100 ms, retaining only segments lasting \(\geq\)100 ms as valid fixations and thereby excluding brief spurious fixations and noise. To more comprehensively assess participants' attention to different regions of the industrial cleaning robot, five eye-tracking metrics were employed: fixation count in areas of interest (AOI), total fixation duration in AOI, time to first entry into AOI, first fixation duration in AOI, and fixation sequence. These metrics capture various aspects of visual attention distribution and cognitive processing. Fixation count--the number of discrete fixations within an AOI--serves as an objective indicator of its importance, since, according to the eye-mind hypothesis, fixation count correlates positively with cognitive processing intensity [22]. Thus, more fixations imply greater interest and salience. Total fixation duration reflects the depth of information processing and cognitive load associated with an AOI [23]; longer durations indicate deeper processing or higher attractiveness. Time to first entry--the latency until a participant's gaze first lands on an AOI--is linked to visual search efficiency,  
Figure 6: Superposition of hot zones for industrial cleaning robots. Figure 7: Area of Interest for industrial cleaning robot AOI. with shorter latencies indicating stronger salience [24]. First fixation duration--the length of the initial fixation upon entering an AOI--reflects initial processing depth and interest, with longer durations suggesting more intense early engagement. Fixation sequence, based on theories of attentional allocation [25], reveals the priority of AOIs: regions fixated earlier in the sequence are considered higher priority. By integrating these five metrics, we obtain a multidimensional evaluation of participant attention to robot regions, encompassing both quantitative (count, duration) and qualitative (latency, initial engagement, sequence) dimensions, thereby providing a robust basis for subsequent design optimization. Following data collection, raw gaze data and heatmaps were exported from Tobii Studio (see Figures 8 and 9). Statistical analysis of eye-tracking metrics was conducted in SPSS (Version 29.0, IBM Corp., Armonk, NY, USA). For fixation count, total fixation duration, time to first entry, and first fixation duration, 20 participants' data were averaged to yield mean values per AOI. A repeated-measures ANOVA was then performed with AOI (top, front, side, bottom) as the within-subjects factor; Bonferroni correction was applied to adjust post hoc pairwise comparisons. Fixation sequence analysis further validated these findings: each AOI was assigned the sequence number corresponding to the order in which it was first fixated (e.g., AOI fixated first received label 1; subsequent first visits retained that label), and mean sequence labels across participants were compared to determine overall priority. Additionally, guided by previous research highlighting total fixation duration as a core measure of attention [26; 27], we examined differences in total fixation duration across AOIs for each design variant. For each AOI and each of the 30 participants, mean fixation duration was calculated to derive an overall attention score, and variants receiving higher mean values within each AOI were identified as the most attended features.