Assessing attentive monitoring levels in dynamic environments through visual neuro-assisted approach

Yu Fei Li; Sun Woh Lye; Yuvaraj Rajamanickam

doi:10.1016/j.heliyon.2022.e09067

Assessing attentive monitoring levels in dynamic environments through visual neuro-assisted approach

Heliyon. 2022 Mar 21;8(3):e09067. doi: 10.1016/j.heliyon.2022.e09067. eCollection 2022 Mar.

Authors

Yu Fei Li^{1

2}, Sun Woh Lye^{1

2}, Yuvaraj Rajamanickam³

Affiliations

¹ Air Traffic Management Research Institute, Nanyang Technological University, Singapore.
² School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.
³ Office of Education Research, National Institute of Education, Singapore.

Abstract

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.

Keywords: Air traffic control; Attentional processes; Human machine interaction; Physiological measurement; Situational awareness.