Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151781
Title: Biomarker-informed machine learning model of cognitive fatigue from a heart rate response perspective
Authors: Lee, Kar Fye Alvin
Gan, Woon-Seng
Christopoulos, Georgios
Keywords: Social sciences::Psychology
Issue Date: 2021
Source: Lee, K. F. A., Gan, W. & Christopoulos, G. (2021). Biomarker-informed machine learning model of cognitive fatigue from a heart rate response perspective. Sensors, 21(11), 3843-. https://dx.doi.org/10.3390/s21113843
Project: COT-V4-2020-1
Journal: Sensors 
Abstract: Cognitive fatigue is a psychological state characterised by feelings of tiredness and impaired cognitive functioning arising from high cognitive demands. This paper examines the recent research progress on the assessment of cognitive fatigue and provides informed recommendations for future research. Traditionally, cognitive fatigue is introspectively assessed through self-report or objectively inferred from a decline in behavioural performance. However, more recently, researchers have attempted to explore the biological underpinnings of cognitive fatigue to understand and measure this phenomenon. In particular, there is evidence indicating that the imbalance between sympathetic and parasympathetic nervous activity appears to be a physiological correlate of cognitive fatigue. This imbalance has been indexed through various heart rate variability indices that have also been proposed as putative biomarkers of cognitive fatigue. Moreover, in contrast to traditional inferential methods, there is also a growing research interest in using data-driven approaches to assessing cognitive fatigue. The ubiquity of wearables with the capability to collect large amounts of physiological data appears to be a major facilitator in the growth of data-driven research in this area. Preliminary findings indicate that such large datasets can be used to accurately predict cognitive fatigue through various machine learning approaches. Overall, the potential of combining domain-specific knowledge gained from biomarker research with machine learning approaches should be further explored to build more robust predictive models of cognitive fatigue.
URI: https://hdl.handle.net/10356/151781
ISSN: 1424-8220
DOI: 10.3390/s21113843
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles
NBS Journal Articles

Page view(s)

61
Updated on Sep 21, 2021

Download(s)

3
Updated on Sep 21, 2021

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.