PLS-based approach for Kansei analysis

A residential garden contributes to mental health in modern life. Gardening is a common recreational activity. From the view of the Kansei engineering, designing the garden is a quite difficult subject. Since garden components such as stones and trees are widely diversified, then number of possible design elements becomes quite large. Meanwhile, evaluation samples that can be used for Kansei Evaluation are limited. Relations between Kansei word evaluation and design elements had been analyzed with Quantification Theory type I, which is a variation of a multiple regression model. Since QT1 is based on the least square method, number of evaluation samples should be larger than the number of design elements. Thus, QT1 is not applicable in this case. Recently, PLS (Partial Least Squares) is becoming popular in the field of Chemometrics, which deal with extremely large number and interacted predictor variables. In this study, we utilized PLS for analyzing Kansei evaluation on residential gardens and their 89 design elements. Analyzing results of PLS and QT1 are compared. QT1 analyses were done on 5-fold design elements. Even when incorporating 89 variables, PLS's multiple correlation coefficient was much higher than QT1. Analyzing result was made into hand-made virtual reality Kansei engineering system. The system contains two projectors and a PC. 3D models of parts such as trees and stones are dynamically chosen and allocated in the scene. The system was based on originally developed 3D computation and rendering library on Java.