A NEURAL NETWORK-BASED SYSTEM FOR PREDICTION OF COMPUTER USER COMFORT

This article proposes a neural network-based system for prediction of computer user comfort with respect to the existing settings of the workstation. In this context, anthropometric measures and the existing measures of a computer workstation were related to back-support comfort, distance comfort, keyboard comfort, monitor comfort, and seat comfort using two distinct modeling approaches—multiple linear regression and artificial neural network. The purpose of this article was to compare and contrast the resulting models. The data from 144 computer workstations were used and a total of 12 different data types such as shoulder to elbow, eye to buttock, pan height, monitor height, or distance from the chair were recorded. While multiple linear regression could not be used to adequately predict the computer workstation comfort, the neural network was deemed superior. This approach allows ergonomists to aid in the decision-making process of computer environment design and the prediction of the health risk in an occupational environment.