Mental Workload Assessment in Military Pilots Using Flight Simulators and Physiological Sensors

Silva, Mario Henrique de Oliveira Coutinho da; Macêdo, Thiago Fontes; Lourenço, Cinthia de Carvalho; Rehder, Ivan de Souza; Marchiori, Ana Angélica da Costa; Cesare, Mateus Pereira; Cortes, Raphael Gomes; Cardoso, Moacyr Machado; Villani, Emília

doi:10.1007/978-3-030-91408-0_7

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…The non-experts had an average self-reported skill score of 0.71 (±0.91) on a scale of −2 to 2. The small sample of expert participants is comparable to similar studies that rely on highly skilled expertise [ 28 , 29 ], to understand the cognitive and physiological demands of multi-tasking, brain electrical activity, eye movements, and heart rate were recorded from 7 participants who simultaneously performed complex tasks at two difficulty levels [ 28 ]. Similarly, prior research employing small samples, like a study with six non-experts and two experts in complex surgical training, has successfully investigated the role of expertise in stress, attention, and acceleration [ 30 ].…”

Section: The Proposed Methodsmentioning

confidence: 99%

Determining Cognitive Workload Using Physiological Measurements: Pupillometry and Heart-Rate Variability

Ma,

Monfared,

Grant

et al. 2024

Sensors

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The adoption of Industry 4.0 technologies in manufacturing systems has accelerated in recent years, with a shift towards understanding operators’ well-being and resilience within the context of creating a human-centric manufacturing environment. In addition to measuring physical workload, monitoring operators’ cognitive workload is becoming a key element in maintaining a healthy and high-performing working environment in future digitalized manufacturing systems. The current approaches to the measurement of cognitive workload may be inadequate when human operators are faced with a series of new digitalized technologies, where their impact on operators’ mental workload and performance needs to be better understood. Therefore, a new method for measuring and determining the cognitive workload is required. Here, we propose a new method for determining cognitive-workload indices in a human-centric environment. The approach provides a method to define and verify the relationships between the factors of task complexity, cognitive workload, operators’ level of expertise, and indirectly, the operator performance level in a highly digitalized manufacturing environment. Our strategy is tested in a series of experiments where operators perform assembly tasks on a Wankel Engine block. The physiological signals from heart-rate variability and pupillometry bio-markers of 17 operators were captured and analysed using eye-tracking and electrocardiogram sensors. The experimental results demonstrate statistically significant differences in both cardiac and pupillometry-based cognitive load indices across the four task complexity levels (rest, low, medium, and high). Notably, these developed indices also provide better indications of cognitive load responding to changes in complexity compared to other measures. Additionally, while experts appear to exhibit lower cognitive loads across all complexity levels, further analysis is required to confirm statistically significant differences. In conclusion, the results from both measurement sensors are found to be compatible and in support of the proposed new approach. Our strategy should be useful for designing and optimizing workplace environments based on the cognitive load experienced by operators.

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Section: The Proposed Methodsmentioning

confidence: 99%

Determining Cognitive Workload Using Physiological Measurements: Pupillometry and Heart-Rate Variability

Ma,

Monfared,

Grant

et al. 2024

Sensors

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“…Research about Air Force flight simulators has drawn the attention of professional journals and academic events. The theme is recurrent, both because of technological advances and because of logistical, operational and technical reasons (Mendes, Brandao-Ramos and Mora-Camino, 2014;Bezerra et al, 2020;Silva et al, 2021;Sá, Vieira and Cunha, 2022). The Brazilian Air Force uses six flight simulators to train pilots: A-1 AMX, A-29 Super Tucano, C-105 Amazonas, C-95M Bandeirantes, F-5M Tiger II and T-27 Tucano.…”

Section: Flight Simulators Of the Brazilian Air Forcementioning

confidence: 99%

Prioritizing flight simulators of the brazilian air force by the analytic hierarchy process and hypothesis tests

Assis¹,

Gavião²,

Kostin³

et al. 2022

BJO&PM

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Goal: The purpose of the research is to apply a method of decision support to prioritize flight simulators of the Air Force Command in view of the country”s budget constraints in the defense sector. Methodology: The research was performed with the Analytic Hierarchy Process (AHP), associated with hypothesis tests to define the preference or equivalence relationships between the simulators. Data collection involved the support of 32 Air Force specialists with extensive experience in the chosen simulators. Results: The T-27 Tucano simulator was preferred, followed by the C-95M Bandeirantes and the C-105 Amazonas, which obtained statistical similarity to each other. In fourth place was the A-29 Super Tucano simulator. The two simulators that had the least preference were the F-5M Tiger II and the A-1 AMX, which achieved results that were statistically close to each other. Limitations: Any multicriteria decision aid technique embeds its features and limitations. This is not exclusive to AHP, although the consistency ratio is a differential in relation to other methods. The expert sample also reflects the preferences of a group, with reservations to the generalization of the results. Practical implications: The findings of this research can be used in practice, by assisting the Brazilian Air Force in applying its scarce financial resources to prioritize flight simulators. Originality / Value: The research is unique to the Brazilian Air Force, in particular to the Center that oversees flight simulators, and is also relevant in including hypothesis testing to AHP results.

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“…The measure has been used for various purposes, for example, to support the flight deck design process (e.g., Banks et al, 2018;Li & Chen, 2020), to examine the workload imposed by different in-flight failures (Etherington et al, 2016) or procedures (Efthymiou et al, 2019), and to validate or corroborate other measures of workload. Such measures include heart rate (e.g., Alaimo et al, 2021;Mansikka et al, 2018Mansikka et al, , 2019, bilateral prefrontal cortex blood oxygenation using functional near-infrared spectroscopy (e.g., Bishop et al, 2021;Matthews et al, 2015), pupillometry (Silva et al, 2021), and respiratory sinus arrhythmia (Muth et al, 2012).…”

mentioning

confidence: 99%

Comparison of In-Flight and Post-Flight Use of NASA-TLX

Mansikka,

Virtanen,

Harris

2024

Aviation Psychology and Applied Human Factors

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The objective of this study was to evaluate the feasibility of airborne assignment of NASA-TLX scores. A total of 21 participants flew two simulated flying missions. In both missions, the participants’ task was to fly an instrument landing system (ILS) approach, followed by a stabilized climbing turn maneuver. In one mission, the participants evaluated their mental workload on the ILS approach and assigned the NASA-TLX scores during the climbing turn maneuver. In the other mission, the same tasks were conducted after the simulated flying task. The participants’ NASA-TLX scores and flying performance between missions were compared. There was no significant difference in the participants’ flying performance between missions. There were no significant differences in either NASA-TLX scores or overall indices between two missions.

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Mental Workload Assessment in Military Pilots Using Flight Simulators and Physiological Sensors

Cited by 3 publications

References 7 publications

Determining Cognitive Workload Using Physiological Measurements: Pupillometry and Heart-Rate Variability

Determining Cognitive Workload Using Physiological Measurements: Pupillometry and Heart-Rate Variability

Prioritizing flight simulators of the brazilian air force by the analytic hierarchy process and hypothesis tests

Comparison of In-Flight and Post-Flight Use of NASA-TLX

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