Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Yu Fei, Lye, Sun Woh, Rajamanickam, Yuvaraj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
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
_version_ 1784679668726628352
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
work_keys_str_mv AT liyufei assessingattentivemonitoringlevelsindynamicenvironmentsthroughvisualneuroassistedapproach
AT lyesunwoh assessingattentivemonitoringlevelsindynamicenvironmentsthroughvisualneuroassistedapproach
AT rajamanickamyuvaraj assessingattentivemonitoringlevelsindynamicenvironmentsthroughvisualneuroassistedapproach