User evaluation and eye tracking-based prediction model for tractor hood product design

摘要

To objectively evaluate the product design of tractor hoods, they have been set as variables, and the remaining components of the hood have been taken as the rations. Eye-tracking and semantic difference-based experiments were performed to determine the level of attention a user gave to the hood and an image evaluation value for the same; morphological analysis was used to deconstruct the structural elements of the tractor hood. The structural elements and image evaluation values were implemented as input and output layers, respectively, in a back-propagation neural network (BPNN) used to train and verify a user-evaluation prediction model for tractor hood designs. The results show that the BPNN model can accurately predict a user’s evaluation of the tractor hood design, thereby providing a reference for designers in terms of the tractor hood shape, and quantify user evaluations of the hood design.