title: Automobile seat comfort prediction: statistical model vs. artificial neural network.
abstract: The current automobile seat comfort development process, which is executed in a trial and error fashion, is expensive and outdated. The prevailing thought is that process improvements are contingent upon the implementation of empirical/prediction models. In this context, seat-interface pressure measures, anthropometric characteristics, demographic information, and perceptions of seat appearance were related to an overall comfort index (which was a single score derived from a previously published 10-item survey with demonstrated levels of reliability and validity) using two distinct modeling approaches-stepwise, linear regression and artificial neural network. The purpose of this paper was to compare and contrast the resulting models. While both models could be used to adequately predict subjective perceptions of comfort, the neural network was deemed superior because it produced higher r2 values (0.832 vs. 0.713) and lower average error values (1.192 vs. 1.779).
authors.affiliation: Department of Industrial and Manufacturing Systems Engineering, University of Windsor, Windsor, Ont. N9B-3P4, Canada. michael.kolich@jci.com
authors.forename: M
authors.initials: M
authors.lastname: Kolich
authors.forename: N
authors.initials: N
authors.lastname: Seal
authors.forename: S
authors.initials: S
authors.lastname: Taboun
mesh_terms: D000886:Anthropometry; D001334:Automobile Driving; D002170:Canada; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D018986:Motor Vehicles; D016571:Neural Networks, Computer