.png){kind=icon}
Return
Quantifying perceptual imagery: A Kansei engineering-based multiple regression model for design quantification
.png){kind=icon}
PRE
AI
DOI：10.1177/14727978251385155.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The rapid global urbanization has propelled Smart Street Lamps (SSLs) as essential smart city infrastructure, yet current designs rely on subjective aesthetic judgments, failing to systematically integrate user perceptual preferences. This study addresses this gap by developing a multiple regression model to quantify relationships between perceptual imagery dimensions and SSL design parameters. Using a mixed-method approach, it collects diverse SSL samples, establishes a perceptual lexicon, and employs cluster analysis, factor analysis, and partial least squares (PLS) regression to identify key perceptual factors and derive equations linking design attributes to perceptual keywords. The validated model bridges design elements with user perceptual experience, enabling data-driven optimization of SSL characteristics. By integrating Kansei engineering with technological innovation, this research enhances design rigor and provides a replicable methodology for smart city products, contributing to sustainable urban development.
Keywords:
smart street lamps
perceptual imagery
design quantification
multiple regression model
Kansei engineering
Journal
IF:
0.4
Papers:
184
Citations:
499
Organization
Cited Papers
ShareSaveShareSaveDesigning a Low-Cost System to Monitor the Structural Behavior of Street Lighting Poles in Smart Cities.png)
Sensors
OAAI
SensorsIF0
OAAIShareSaveProduct Design Award Prediction Modeling: Design Visual Aesthetic Quality Assessment via DCNNsIEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAIShareSaveShareSaveShareSaveShareSaveShareSave
View more