# Research on the Parametric Design and Application of Ceramic Modeling Based on Python
## 3 Parametric Design Practice  
### Determination of Parametric Design Factors  
With the increasing demand for artistic value and personalization in ceramic products, the selection and combination of modeling elements become crucial for enhancing the user experience. To systematically study the impact of shaping elements on the user experience, this research employs the principles of human factors engineering and the ErgoLAB human-machine environment synchronization platform V3.0. Focusing on core elements such as shape, volume, material, mouthpiece, handle, and bottom design [13], advanced measurement techniques and data analysis methods are utilized to identify key parameters for parametric design. This paper adopted the ErgoLAB human-machine environment synchronization platform V3.0 for experimental design and data collection. The platform can synchronously collect multi-modal data from participants, including physiological signals (such as electroencephalography, electrocardiography, heart rate variability, etc. ), eye movement data, etc., providing strong support for a comprehensive analysis of participants' behaviors and reactions when observing and using ceramic products. To ensure the reliability and representativeness of the experimental results, we carefully recruited 50 representative participants through a combination of stratified sampling and quota sampling methods. Participants were recruited via online platforms (e.g., social media and academic forums) and offline channels (e.g., university campuses and community centers) to ensure diversity in recruitment sources. Eligibility criteria included age (18-65 years), gender (balanced male-to-female ratio), occupation (covering students, professionals, and artisans), and educational background (from high school to postgraduate levels). Each participant received monetary compensation to incentivize engagement and ensure data completeness. To ensure the reliability and representativeness of the experimental results, we carefully balanced the sample distribution and addressed potential data skewness and outliers through several measures. We employed stratified sampling, dividing the population into subgroups based on age, gender, and occupation to ensure proportional representation from each subgroup. Specific quotas were allocated for underrepresented groups, such as older adults and artisans, to prevent the overrepresentation of dominant demographics. During data preprocessing, we applied statistical methods like Z-score analysis to detect and exclude extreme values caused by technical errors or non-compliance. Additionally, we weighted the data from underrepresented groups post-collection to align with the target population distribution. This comprehensive approach ensured that the participants, who had diverse backgrounds in terms of age, gender, occupation, and education, could fully reflect the needs and preferences of various user groups. During the experiment, we showed the participants samples of ceramic products with various design elements and set observation times. Through the ErgoLAB human-machine environment synchronization platform V3.0, this study synchronously collected the following multimodal data (see Figure 2, which shows a photo of an actual experimental scene): Firstly, physiological signals were recorded, including electroencephalogram (EEG) signals to capture cognitive processing activities, electrocardiogram (ECG) signals to analyze heart rate variability (HRV) and reflect emotional fluctuations, and galvanic skin response (GSR) signals to characterize emotional arousal and stress responses. Secondly, eye movement data were gathered, covering gaze heatmaps, gaze trajectories, time to first fixation, and total fixation duration. Lastly, behavioral data were collected, such as task completion time, preference selection records, and interactive behaviors during operation simulations. To analyze the physiological parameters collected during the experiment, we employed specific methods for each type of data.