Cognitive fatigue assessment in operational settings: a review and UAS implications

Jahanpour, Emilie; Berberian, Bruno; Imbert, Jean-Paul; Roy, Raphaëlle N.

doi:10.1016/j.ifacol.2021.04.188

Cited by 5 publications

(6 citation statements)

References 64 publications

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“…The relation between sleep and mental fatigue is complex and difficult to explore, as definitions and experimental procedures vary widely (Åkerstedt et al, 2004 ; Matthews, 2012 ; Jahanpour et al, 2020 ). The impact of prolonged use depending on solicited sensory modality is also to be studied, as well as the interaction with individual differences of operators, such as expertise and preferences.…”

Section: Discussionmentioning

confidence: 99%

“…The downsides are that training data need to be acquired, as well as the intrusiveness of many systems, making them less practical. Regarding cerebral metrics, increases in EEG power of the alpha (8-12 Hz) and the theta (4–7 Hz) bands have been repeatedly associated with increased mental fatigue (Borghini et al, 2014 ; Roy et al, 2014 ; Jahanpour et al, 2020 ; Tran et al, 2020 ). EEG based connectivity measures have also been successfully used to predict performance in a simulated driving task (Sun et al, 2014 ).…”

Section: Markers/triggers For Adaptationmentioning

confidence: 99%

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Cognitive effects of prolonged continuous human-machine interaction: The case for mental state-based adaptive interfaces

Hinss

Brock

Roy

2022

Front. Neuroergonomics

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Operators of complex systems across multiple domains (e.g., aviation, automotive, and nuclear power industry) are required to perform their tasks over prolonged and continuous periods of time. Mental fatigue as well as reduced cognitive flexibility, attention, and situational awareness all result from prolonged continuous use, putting at risk the safety and efficiency of complex operations. Mental state-based adaptive systems may be a solution to this problem. These systems infer the current mental state of an operator based on a selection of metrics ranging from operator independent measures (e.g., weather and time of day), to behavioral (e.g., reaction time and lane deviation) as well as physiological markers (e.g., electroencephalography and cardiac activity). The interaction between operator and system may then be adapted in one of many ways to mitigate any detected degraded cognitive state, thereby ensuring continued safety and efficiency. Depending on the task at hand and its specific problems, possible adaptations -usually based on machine learning estimations- e.g., include modifications of information, presentation modality or stimuli salience, as well as task scheduling. Research on adaptive systems is at the interface of several domains, including neuroergonomics, human factors, and human-computer interaction in an applied and ecological context, necessitating careful consideration of each of the aforementioned aspects. This article provides an overview of some of the key questions and aspects to be considered by researchers for the design of mental state-based adaptive systems, while also promoting their application during prolonged continuous use to pave the way toward safer and more efficient human-machine interaction.

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Section: Discussionmentioning

confidence: 99%

Section: Markers/triggers For Adaptationmentioning

confidence: 99%

Cognitive effects of prolonged continuous human-machine interaction: The case for mental state-based adaptive interfaces

Hinss

Brock

Roy

2022

Front. Neuroergonomics

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“…Cognitive fatigue assessment using fNIRS has been applied in multiple domains, such as image interpretation in medical settings [ 15 ], physical performance [ 17 ], operational settings [ 7 ], during simulated driving [ 14 ], and during serious games [ 13 ]. However, there is still a lack of evaluation of cognitive fatigue during learning.…”

Section: Related Workmentioning

confidence: 99%

“…Cognitive fatigue can be defined as the difficulty of initiating or sustaining voluntary tasks and its onset is usually triggered by performing complex mental activities over time [ 7 ]. Thus, this cognitive state negatively impacts human performance.…”

Section: Introductionmentioning

confidence: 99%

“…This state hinders learning by impairing executive functioning, which involves the ability to update information in memory. Moreover, cognitive fatigue leads to increased perseverance, which is not beneficial in cases where subjects are unable to evaluate their state, persisting in irrational actions [ 7 ]. Thus, we developed an experimental procedure that effectively induces cognitive fatigue and monitors the participants’ mental state using a set of physiological sensors to obtain the corresponding physiological signals.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning

Varandas

Lima

Badia

et al. 2022

Sensors

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Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtrusively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91±13.67%. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human–computer interaction variables.

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Slowed reaction times in cognitive fatigue are not attributable to declines in motor preparation

2022

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Cognitive fatigue assessment in operational settings: a review and UAS implications

Cited by 5 publications

References 64 publications

Cognitive effects of prolonged continuous human-machine interaction: The case for mental state-based adaptive interfaces

Cognitive effects of prolonged continuous human-machine interaction: The case for mental state-based adaptive interfaces

Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning

Slowed reaction times in cognitive fatigue are not attributable to declines in motor preparation

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