Human Error Prediction Using Heart Rate Variability and Electroencephalography

Abstract

As human’s simple tasks are being increasingly replaced by autonomous systems and robots, it is likely that the responsibility of handling more complex tasks will be more often placed on human workers. Thus, situations in which workplace tasks change before human workers become proficient at those tasks will arise more frequently due to rapid changes in business trends. Based on this background, the importance of preventing human error will become increasingly crucial. Existing studies on human error reveal how task errors are related to heart rate variability (HRV) indexes and electroencephalograph (EEG) indexes. However, in terms of preventing human error, analysis on their relationship with conditions before human error occurs (i.e., the human pre-error state) is still insufficient. This study aims at identifying biological indexes potentially useful for the detection of high-risk psychological states. As a result of correlation analysis between the number of errors in a Stroop task and the multiple HRV and EEG indexes obtained before and during the task, significant correlations were obtained with respect to several biological indexes. Specifically, we confirmed that conditions before the task are important for predicting the human error risk in high-cognitive-load tasks while conditions both before and during tasks are important in low-cognitive-load tasks.

Acute combined effects of concurrent physical activities on autonomic nervous activation during cognitive tasks

“…Studies have demonstrated a correlation between ECG signals and psychomotor vigilance task (PVT) performance, as well as the effectiveness of HRV indices in assessing cognitive task-related errors. Leveraging sensitive features extracted from ECG, algorithms like learning vector quantization and random forest tree classifiers achieve impressive accuracy in identifying fatigue states, underscoring HRV as a potential indicator for evaluating worker fatigue ( Chua et al, 2012 ; Zhao et al, 2012 ; Pan et al, 2021 ; Xu et al, 2021 ; Takada et al, 2022 ).…”