Generalisable machine learning models trained on heart rate variability data to predict mental fatigue.

ABSTRACT: A prolonged period of cognitive performance often leads to mental fatigue, a psychobiological state that increases the risk of injury and accidents. Previous studies have trained machine learning algorithms on Heart Rate Variability (HRV) data to detect fatigue in order to prevent its consequences. However, the results of these studies cannot be generalised because of various methodological issues including the use of only one type of cognitive task to induce fatigue which makes any predictions task-specific. In this study, we combined the datasets of three experiments each of which applied different cognitive tasks for fatigue induction and trained algorithms that detect fatigue and predict its severity. We also tested different time window lengths and compared algorithms trained on resti

SUBMITTER: Matuz A 

PROVIDER: S-EPMC9681752 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature