| DOI | Trouver le DOI : https://doi.org/10.1109/SMC42975.2020.9283499 |
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| Auteur | Rechercher : Ghosh Hajra, Sujoy1; Rechercher : Xi, Pengcheng2; Rechercher : Law, Andrew1 |
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| Affiliation | - Conseil national de recherches Canada. Aérospatiale
- Conseil national de recherches Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), October 11-14, 2020, Toronto, Ontario |
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| Sujet | workload; helicopter pilot; ECG; EEG; machine learning; flight |
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| Résumé | There is increasing interest in understanding the cognitive and physiological state of operators in safety critical situations (e.g. pilots), specifically as it relates to task difficulty and mental workload. Herein, we evaluate the potential of electrocardiography (ECG) and electroencephalography (EEG) for detecting in-flight changes in helicopter pilot workload. Two National Research Council Canada test pilots performed a series of flight maneuvers in an NRC Bell 205 helicopter which involved a target tracking task with three levels of difficulty. Subjective ratings of pilot workload were collected using the Cooper-Harper handling quality ratings scale and pilot control activity was quantified based on cyclic control movements. ECG derived measures of heart rate and heart rate variability, as well as EEG derived measures of power in three frequency bands (theta 4-8Hz; alpha 8-13Hz; beta 13-22Hz), were computed and compared across task difficulty levels. A set of support vector machine (SVM) regressors were trained and tested to differentiate the three difficulty levels from ECG and EEG features. Differences in subjective ratings and control activity metrics confirmed the task difficulty manipulations (p<0.01). ECG-derived physiological metrics were able to partially resolve differences among the task difficulty levels. Similarly, EEG-derived cognitive measures confirmed the capture of differential neural functioning levels for the task difficulty conditions in the alpha and beta bands (p<0.05), though substantial individual differences were observed between pilots. SVM regressors trained on ECG and EEG features successfully differentiated levels of workload, with the ECG-based regressor (minimum cross-validation MSE ECG = 0.17) performing better than the EEG-based regressor (minimum cross-validation MSE EEG = 0.29). This study provides an initial application demonstration of physiological and cognitive metrics and machine learning approaches for detecting differences in task difficulty during helicopter flight. This is the necessary first step for further development of passive brain computer interfaces for real-time in-flight monitoring of helicopter pilot workload. |
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| Date de publication | 2020-12-14 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 5fc159f2-281c-4d3d-bdbb-fbd2d22e711c |
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| Enregistrement créé | 2021-06-23 |
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| Enregistrement modifié | 2021-06-24 |
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