Design Tool for Smartwatch Form Aesthetics Evaluation Based on Principal Component Regression

This study presents an aesthetic evaluation system for smartwatches based on Principal Component Regression (PCR). User reviews and product parameters were collected from platforms like Taobao and JD.com using web scraping techniques, and 38 attributes related to form aesthetics were extracted through literature review and interviews. Using card sorting, these attributes were reduced to six core evaluation attributes: style, color, material, dial, strap, and overall feel. A predictive model for form aesthetics evaluation was developed, along with a visualization system. The results show that design style and overall feel have the most significant impact on the aesthetic score, and the model effectively reflects the core aesthetic preferences of users. This system provides designers with a scientific tool from the user’s perspective and can be extended to optimize the design of other wearable devices. Future research will expand the sample size and introduce multidimensional evaluations to further enhance the system’s functionality.