Modelling postural discomfort perception using CHAID decision tree algorithm

Abstract

Developing quantitative models that predict discomfort levels of working postures has been an important ergonomics research topic. Such modeling not only has practical applications, but also may serve as a useful research method to improve our understanding of the human postural discomfort perception process. While the existing models have focused on achieving high prediction accuracy, less attention has been given to model interpretability, which is vital for understanding a process through modeling. Research is needed to identify the model types or modeling methods that offer high interpretability as well as good prediction accuracy. The goal of this study was to evaluate the utility of the Chi-square Automatic Interaction Detector (CHAID) decision tree modeling method in developing postural discomfort prediction models. Ten individual-specific decision tree models were developed, which predicted overall upper-body discomfort from local body part discomfort ratings. The prediction models were found to achieve high prediction accuracy and interpretability. (150 words)