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Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning
Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtrusively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive f...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9183003/ https://www.ncbi.nlm.nih.gov/pubmed/35684626 http://dx.doi.org/10.3390/s22114010 |
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author | Varandas, Rui Lima, Rodrigo Bermúdez I Badia, Sergi Silva, Hugo Gamboa, Hugo |
author_facet | Varandas, Rui Lima, Rodrigo Bermúdez I Badia, Sergi Silva, Hugo Gamboa, Hugo |
author_sort | Varandas, Rui |
collection | PubMed |
description | Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtrusively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67 %. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human–computer interaction variables. |
format | Online Article Text |
id | pubmed-9183003 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91830032022-06-10 Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning Varandas, Rui Lima, Rodrigo Bermúdez I Badia, Sergi Silva, Hugo Gamboa, Hugo Sensors (Basel) Article Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtrusively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67 %. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human–computer interaction variables. MDPI 2022-05-25 /pmc/articles/PMC9183003/ /pubmed/35684626 http://dx.doi.org/10.3390/s22114010 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/). |
spellingShingle | Article Varandas, Rui Lima, Rodrigo Bermúdez I Badia, Sergi Silva, Hugo Gamboa, Hugo Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning |
title | Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning |
title_full | Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning |
title_fullStr | Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning |
title_full_unstemmed | Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning |
title_short | Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning |
title_sort | automatic cognitive fatigue detection using wearable fnirs and machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9183003/ https://www.ncbi.nlm.nih.gov/pubmed/35684626 http://dx.doi.org/10.3390/s22114010 |
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