The Complementary Role of Activity Context in the Mental Workload Evaluation of Helicopter Pilots: A Multi-tasking Learning Approach

Bargiotas, Ioannis; Nicolaï, A.; Vidal, Pierre‐Paul; Labourdette, Christophe; Vayatis, Nicolas; Buffat, Stéphane

doi:10.1007/978-3-030-14273-5_13

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…[1]) and measure the operator's workload. From that moment on, the measure of the workload has spread from the aeronautical [4] [5], the educational [6] [7] and to the clinical [8] fields. Even the aims of workload measurement have evolved: the ultimate goal in all environments is mainly the Workload Adaptation, the process of workload management to aid learning, healing or limiting human errors.…”

Section: Multifaceted Aspects Of Workloadmentioning

confidence: 99%

On the Use of Machine Learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task

Sciaraffa

Aricò

Borghini

et al. 2019

Communications in Computer and Information Science

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The measurement of the mental workload during real tasks by means of neurophysiological signals is still challenging. The employment of Machine Learning techniques has allowed a step forward in this direction, however, most of the work has dealt with binary classification. This study proposed to examine the surveys already performed in the context of EEG-based workload classification and to test different machine learning algorithms on real multitasking activity like the Air Traffic Management. The results obtained on 35 professional Air Traffic Controllers showed that a KNN algorithm allows discriminating up to three workload levels (low, medium and high) with more than 84 % of accuracy on average. Moreover, in such realistic employment it emerges how important is to opportunely choose the set of features to ward off that task-related confounds could affect the workload assessment.

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Section: Multifaceted Aspects Of Workloadmentioning

confidence: 99%

On the Use of Machine Learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task

Sciaraffa

Aricò

Borghini

et al. 2019

Communications in Computer and Information Science

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In the Sky Between Expertise and Unexpected Feelings and Resources of Pilots’ Resilient Ego: A Psychoanalytic Point of View

Pediconi

Bigi

Brunori

et al. 2020

Communications in Computer and Information Science

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Advancing Aviation Safety Through Machine Learning and Psychophysiological Data: A Systematic Review

Alreshidi,

Moulitsas,

Jenkins

2024

IEEE Access

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In the aviation industry, safety remains vital, often compromised by pilot errors attributed to factors such as workload, fatigue, stress, and emotional disturbances. To address these challenges, recent research has increasingly leveraged psychophysiological data and machine learning techniques, offering the potential to enhance safety by understanding pilot behavior. This systematic literature review rigorously follows a widely accepted methodology, scrutinizing 80 peer-reviewed studies out of 3352 studies from five key electronic databases. The paper focuses on behavioral aspects, data types, preprocessing techniques, machine learning models, and performance metrics used in existing studies. It reveals that the majority of research disproportionately concentrates on workload and fatigue, leaving behavioral aspects like emotional responses and attention dynamics less explored. Machine learning models such as tree-based and support vector machines are most commonly employed, but the utilization of advanced techniques like deep learning remains limited. Traditional preprocessing techniques dominate the landscape, urging the need for advanced methods. Data imbalance and its impact on model performance is identified as a critical, underresearched area. The review uncovers significant methodological gaps, including the unexplored influence of preprocessing on model efficacy, lack of diversification in data collection environments, and limited focus on model explainability. The paper concludes by advocating for targeted future research to address these gaps, thereby promoting both methodological innovation and a more comprehensive understanding of pilot behavior.

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The Complementary Role of Activity Context in the Mental Workload Evaluation of Helicopter Pilots: A Multi-tasking Learning Approach

Cited by 5 publications

References 29 publications

On the Use of Machine Learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task

On the Use of Machine Learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task

In the Sky Between Expertise and Unexpected Feelings and Resources of Pilots’ Resilient Ego: A Psychoanalytic Point of View

Advancing Aviation Safety Through Machine Learning and Psychophysiological Data: A Systematic Review

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