Proposal of Human Error Prediction Method by Biometric Index and Logistic Regression

Abstract

Various methods using biometric data have been proposed for the analysis of the mental state of human error. The purpose of this study is to construct a prediction model of errors and to predict them in real time by measuring mental states using EEG, heartbeat, and questionnaire results. We proposed a prediction model of errors for each individual using the EEG, heart rate, and questionnaire results obtained from the Stroop task. As a result, it was found that some indices of EEG, heartbeat, and questionnaire results were related to errors, and these indices were incorporated into the error prediction model. In addition, we tested whether human errors can be prevented by predicting errors in real time. As a result, when an error was predicted, the occurrence of the error was confirmed in 97% of cases.