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Assessing attentive monitoring levels in dynamic environments through visual neuro-assisted approach
OBJECTIVE: This work aims to establish a framework in measuring the various attentional levels of the human operator in a real-time animated environment through a visual neuro-assisted approach. BACKGROUND: With the increasing trend of automation and remote operations, understanding human-machine in...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8971592/ https://www.ncbi.nlm.nih.gov/pubmed/35368547 http://dx.doi.org/10.1016/j.heliyon.2022.e09067 |
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author | Li, Yu Fei Lye, Sun Woh Rajamanickam, Yuvaraj |
author_facet | Li, Yu Fei Lye, Sun Woh Rajamanickam, Yuvaraj |
author_sort | Li, Yu Fei |
collection | PubMed |
description | OBJECTIVE: This work aims to establish a framework in measuring the various attentional levels of the human operator in a real-time animated environment through a visual neuro-assisted approach. BACKGROUND: With the increasing trend of automation and remote operations, understanding human-machine interaction in dynamic environments can greatly aid to improve performance, promote operational efficiency and safety. METHOD: Two independent 1-hour experiments were conducted on twenty participants where eye-tracking metrics and neuro activities from electroencephalogram (EEG) were recorded. The experiments required participants to exhibit attentive behaviour in one set and inattentive in the other. Two segments (“increasing flight numbers” and “relatively constant flight numbers”) were also extracted to study the participants’ visual behavioral differences in relation to aircraft numbers. RESULTS: For the two experimental studies, those in the attentive behavioral study show incidences of higher fixation count, fixation duration, number of aircraft spotted, and landing fixations whereas those in inattentive behavior study reveal higher zero-fixation frame count. In experiments involving ‘increasing flight numbers’, a higher percentage of aircraft were spotted as compared to those with ‘constant flight numbers’ in both the groups. Three parameters (number of aircraft spotted, and landing fixations and zero-fixation frame count) are newly established. As radar monitoring is a brain engagement activity, positive EEG data were registered in all the participants. A newly Task Engagement Index (TEI) was also formulated to predict different attentional levels. CONCLUSION: Results provide a refined quantifiable tool to differentiate between attentive and inattentive monitoring behavior in a real-time dynamic environment, which can be applied across various sectors. RECOMMENDATION: With the quantitative TEI established, this paves the way for future studies into attentional levels by regions, time based, as well as eye signature studies in relation to visual task engagement and management and determining expertise levels to be explored. Factors relating to fatigue could also be investigated using the TEI approach proposed. |
format | Online Article Text |
id | pubmed-8971592 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-89715922022-04-02 Assessing attentive monitoring levels in dynamic environments through visual neuro-assisted approach Li, Yu Fei Lye, Sun Woh Rajamanickam, Yuvaraj Heliyon Research Article OBJECTIVE: This work aims to establish a framework in measuring the various attentional levels of the human operator in a real-time animated environment through a visual neuro-assisted approach. BACKGROUND: With the increasing trend of automation and remote operations, understanding human-machine interaction in dynamic environments can greatly aid to improve performance, promote operational efficiency and safety. METHOD: Two independent 1-hour experiments were conducted on twenty participants where eye-tracking metrics and neuro activities from electroencephalogram (EEG) were recorded. The experiments required participants to exhibit attentive behaviour in one set and inattentive in the other. Two segments (“increasing flight numbers” and “relatively constant flight numbers”) were also extracted to study the participants’ visual behavioral differences in relation to aircraft numbers. RESULTS: For the two experimental studies, those in the attentive behavioral study show incidences of higher fixation count, fixation duration, number of aircraft spotted, and landing fixations whereas those in inattentive behavior study reveal higher zero-fixation frame count. In experiments involving ‘increasing flight numbers’, a higher percentage of aircraft were spotted as compared to those with ‘constant flight numbers’ in both the groups. Three parameters (number of aircraft spotted, and landing fixations and zero-fixation frame count) are newly established. As radar monitoring is a brain engagement activity, positive EEG data were registered in all the participants. A newly Task Engagement Index (TEI) was also formulated to predict different attentional levels. CONCLUSION: Results provide a refined quantifiable tool to differentiate between attentive and inattentive monitoring behavior in a real-time dynamic environment, which can be applied across various sectors. RECOMMENDATION: With the quantitative TEI established, this paves the way for future studies into attentional levels by regions, time based, as well as eye signature studies in relation to visual task engagement and management and determining expertise levels to be explored. Factors relating to fatigue could also be investigated using the TEI approach proposed. Elsevier 2022-03-21 /pmc/articles/PMC8971592/ /pubmed/35368547 http://dx.doi.org/10.1016/j.heliyon.2022.e09067 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Li, Yu Fei Lye, Sun Woh Rajamanickam, Yuvaraj Assessing attentive monitoring levels in dynamic environments through visual neuro-assisted approach |
title | Assessing attentive monitoring levels in dynamic environments through visual neuro-assisted approach |
title_full | Assessing attentive monitoring levels in dynamic environments through visual neuro-assisted approach |
title_fullStr | Assessing attentive monitoring levels in dynamic environments through visual neuro-assisted approach |
title_full_unstemmed | Assessing attentive monitoring levels in dynamic environments through visual neuro-assisted approach |
title_short | Assessing attentive monitoring levels in dynamic environments through visual neuro-assisted approach |
title_sort | assessing attentive monitoring levels in dynamic environments through visual neuro-assisted approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8971592/ https://www.ncbi.nlm.nih.gov/pubmed/35368547 http://dx.doi.org/10.1016/j.heliyon.2022.e09067 |
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