Machine learning-based approach for identifying mental workload of pilots

Mohanavelu, K.; Poonguzhali, S.; Janani, Azin S.; Vinutha, S.

doi:10.1016/j.bspc.2022.103623

Cited by 25 publications

(16 citation statements)

References 30 publications

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“…To verify the representativeness and credibility of the mental workload classification model established by SVM, the same classification factors as SVM1 and SVM2, respectively, were reclassified according to the same classification order using KNN. The results indicated that all SVM evaluation indexes were higher than those of KNN, which was consistent with the findings of Mohanavelu et al (22); the was probably because SVM outperformed KNN provided the sample size was not small (34). For the SVM hierarchical combination classifier, all evaluation indexes of SVM2 were higher than SVM1, probably because the second layer of the classifier had more classification factors than the first, which might also explain why the accuracy of KNN2 was higher than KNN1.…”

Section: Resultssupporting

confidence: 91%

“…Chen et al analyzed the sensitivity of the pilot’s EEG and physiological factors under different flight tasks, and established a pilot workload evaluation model based on SVM ( 21 ). Mohanavelu et al employed an SVM classifier to effectively identify the pilots’ cognitive workload level during takeoff, cruise, and landing phases with ECG and EEG ( 22 ). Thus, the current study selected SVM to establish the mental workload classifier.…”

mentioning

confidence: 99%

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Variation of Pilots’ Mental Workload Under Emergency Flight Conditions Induced by Different Equipment Failures: A Flight Simulator Study

Zhang

Yuan

Jiao

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Pilots’ excessive mental workload could reduce their ability to perform concurrent tasks during emergency flights, which is one of the most critical aviation safety concerns. Several past efforts have attempted to investigate the underlying issues, but all had limited success owing to the challenge of collecting representative data under realistic operating conditions. This study aimed to address this challenge by conducting a flight simulator study involving a comparatively large number of participants, who were pilot cadets with flight experience, and using noninvasive functional near-infrared spectroscopy (fNIRS) to collect the pilots’ brain activity data. Pilots’ subjective ratings and brain activity records were collected over a total of 75 simulated flights under three subtask scenarios comprising different equipment failures. A statistical analysis was carried out on the subjective ratings and on the changes observed in the saturation of the oxyhemoglobin (ΔOxyHb) of individual fNIRS channels. The mental workload of the pilots was classified using a support vector machine hierarchical combination classifier, focusing on the question of whether it is feasible to classify pilots’ mental workload using brain activity signals (i.e., ΔOxyHb). The results suggested that the pilots’ mental workload levels were highly associated with the ΔOxyHb measures as well as with the activities of different brain regions, including the prefrontal-, motor-, and occipital cortex. The findings from this study could provide a reference for optimizing pilot training systems and improving pilot performance during emergency flight operations.

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Section: Resultssupporting

confidence: 91%

mentioning

confidence: 99%

Variation of Pilots’ Mental Workload Under Emergency Flight Conditions Induced by Different Equipment Failures: A Flight Simulator Study

Zhang

Yuan

Jiao

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…35 ST articles were found [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ]. In this context, the simulation of a flight task enables the control of experimental stimuli, such as the addition of engine failures (e.g., [ 43 ]), turbulence (e.g., [ 35 ]) or visibility setting (e.g., [ 30 ]).…”

Section: Resultsmentioning

confidence: 99%

“…Objective assessment was employed in 30 ST articles [29][30][31][32][33][34]36,[38][39][40][41][42][43][44][45][47][48][49][50][51][52][54][55][56][57][58][59][60][61][62] and in 9 RF articles [68][69][70][71][72][73][74][75][76]. As a result of the analysis of these studies, the performed objective measures, the calculated parameters, and the employed instrumentation are summarized in Table 1.…”

Section: Objective Assessmentmentioning

confidence: 99%

Stress and Workload Assessment in Aviation—A Narrative Review

Masi

Amprimo

Ferraris

et al. 2023

Sensors

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In aviation, any detail can have massive consequences. Among the potential sources of failure, human error is still the most troublesome to handle. Therefore, research concerning the management of mental workload, attention, and stress is of special interest in aviation. Recognizing conditions in which a pilot is over-challenged or cannot act lucidly could avoid serious outcomes. Furthermore, knowing in depth a pilot’s neurophysiological and cognitive–behavioral responses could allow for the optimization of equipment and procedures to minimize risk and increase safety. Furthermore, it could translate into a general enhancement of both the physical and mental well-being of pilots, producing a healthier and more ergonomic work environment. This review brings together literature on the study of stress and workload in the specific case of pilots of both civil and military aircraft. The most common approaches for studying these phenomena in the avionic context are explored in this review, with a focus on objective methodologies (e.g., the collection and analysis of neurophysiological signals). This review aims to identify the pros, cons, and applicability of the various approaches, to enable the design of an optimal protocol for a comprehensive study of these issues.

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“…The proposed convolution attention memory network (CAMNN) model outperforms to the previously used method with an accuracy of 92%. Mohanavelu et al [70] used different machine learning approaches to identify the mental workload of the flight pilots during different phases of flight, i.e., takeoff, landing, etc. Using EEG features, the cognitive workload was classified, and the LDA performed better than the other two methods.…”

Section: Discussionmentioning

confidence: 99%

A deep learning approach to detect the electroencephalogram-based cognitive task states

2023

IJATEE

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Brain-computer interface (BCI) is an important topic for researchers and the scientific community, as indicated by the abundance of research and study materials in the field. The purpose of the BCI is to allow interaction with any device or computer via brain signals. According to this definition, BCI strives to collect the brain signals using sensors, analyze and process these received signals, and then extract features to operate any device. Simply, it is a link between the brain and the device. The user can control the device by using the brain's neural activities. *Author for correspondence BCI was first developed for biomedical applications to enable physically impaired persons to move around by substituting for lost motor functions [1]. Nowadays, it includes non-medical applications as well [2, 3]. Newer areas of BCI research include lie detection, drowsiness detection, cognitive studies, motor imagery, virtual reality, video games, driver fatigue detection, stress detection, and many more. From these applications, cognitive ability is important to understanding brain functioning. Cognitive ability depends from person to person and is essential in controlling various mental activities [4]. BCI research has been accelerated by technological advances enabling processing and observing mental and cognitive activities [5]. Any cognitive task reveals how the person thinks, utilizes,

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Machine learning-based approach for identifying mental workload of pilots

Cited by 25 publications

References 30 publications

Variation of Pilots’ Mental Workload Under Emergency Flight Conditions Induced by Different Equipment Failures: A Flight Simulator Study

Variation of Pilots’ Mental Workload Under Emergency Flight Conditions Induced by Different Equipment Failures: A Flight Simulator Study

Stress and Workload Assessment in Aviation—A Narrative Review

A deep learning approach to detect the electroencephalogram-based cognitive task states

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