Continuous Assessment of Mental Workload During Complex Human–Machine Interaction: Inferring Cognitive State from Signals External to the Operator
The use of complex human–machine interfaces (HMIs) has grown rapidly over the last few decades in both industrial and personal contexts. Now more than ever, the study of mental workload (MWL) in HMI operators appears essential: when mental demand exceeds task load, cognitive overload arises, increas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/12/3624 |
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| author | Axel Roques Dimitri Keriven Serpollet Alice Nicolaï Stéphane Buffat Yannick James Nicolas Vayatis Ioannis Bargiotas Pierre-Paul Vidal |
| author_facet | Axel Roques Dimitri Keriven Serpollet Alice Nicolaï Stéphane Buffat Yannick James Nicolas Vayatis Ioannis Bargiotas Pierre-Paul Vidal |
| author_sort | Axel Roques |
| collection | DOAJ |
| description | The use of complex human–machine interfaces (HMIs) has grown rapidly over the last few decades in both industrial and personal contexts. Now more than ever, the study of mental workload (MWL) in HMI operators appears essential: when mental demand exceeds task load, cognitive overload arises, increasing the risk of work-related fatigue or accidents. In this paper, we propose a data-driven approach for the continuous estimation of the MWL of professional helicopter pilots in realistic simulated flights. Physiological and operational parameters were used to train a novel machine-learning model of MWL. Our algorithm achieves good performance (ROC AUC score 0.836 ± 0.081, the maximum F1 score 0.842 ± 0.078 and PR AUC score 0.820 ± 0.097) and shows that the operational information outperforms the physiological signals in terms of predictive power for MWL. Our results pave the way towards intelligent systems able to monitor the MWL of HMI operators in real time and question the relevancy of physiology-derived metrics for this task. |
| format | Article |
| id | doaj-art-c812a993df484166a97af7747995f7f4 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c812a993df484166a97af7747995f7f42025-08-20T03:16:39ZengMDPI AGSensors1424-82202025-06-012512362410.3390/s25123624Continuous Assessment of Mental Workload During Complex Human–Machine Interaction: Inferring Cognitive State from Signals External to the OperatorAxel Roques0Dimitri Keriven Serpollet1Alice Nicolaï2Stéphane Buffat3Yannick James4Nicolas Vayatis5Ioannis Bargiotas6Pierre-Paul Vidal7Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006 Paris, FranceUniversité Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006 Paris, FranceUniversité Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006 Paris, FranceLaboratoire d’Accidentologie de Biomécanique et du Comportement des Conducteurs, GIE Renault-PSA Groupes, 92000 Nanterre, FranceTraining & Simulation, Thales AVS France SAS, 95520 Osny, FranceUniversité Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006 Paris, FranceCIAMS, Inria, Université Paris-Saclay, 91190 Gif-sur-Yvette, FranceUniversité Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006 Paris, FranceThe use of complex human–machine interfaces (HMIs) has grown rapidly over the last few decades in both industrial and personal contexts. Now more than ever, the study of mental workload (MWL) in HMI operators appears essential: when mental demand exceeds task load, cognitive overload arises, increasing the risk of work-related fatigue or accidents. In this paper, we propose a data-driven approach for the continuous estimation of the MWL of professional helicopter pilots in realistic simulated flights. Physiological and operational parameters were used to train a novel machine-learning model of MWL. Our algorithm achieves good performance (ROC AUC score 0.836 ± 0.081, the maximum F1 score 0.842 ± 0.078 and PR AUC score 0.820 ± 0.097) and shows that the operational information outperforms the physiological signals in terms of predictive power for MWL. Our results pave the way towards intelligent systems able to monitor the MWL of HMI operators in real time and question the relevancy of physiology-derived metrics for this task.https://www.mdpi.com/1424-8220/25/12/3624mental workloadmachine learningpilotssimulator |
| spellingShingle | Axel Roques Dimitri Keriven Serpollet Alice Nicolaï Stéphane Buffat Yannick James Nicolas Vayatis Ioannis Bargiotas Pierre-Paul Vidal Continuous Assessment of Mental Workload During Complex Human–Machine Interaction: Inferring Cognitive State from Signals External to the Operator Sensors mental workload machine learning pilots simulator |
| title | Continuous Assessment of Mental Workload During Complex Human–Machine Interaction: Inferring Cognitive State from Signals External to the Operator |
| title_full | Continuous Assessment of Mental Workload During Complex Human–Machine Interaction: Inferring Cognitive State from Signals External to the Operator |
| title_fullStr | Continuous Assessment of Mental Workload During Complex Human–Machine Interaction: Inferring Cognitive State from Signals External to the Operator |
| title_full_unstemmed | Continuous Assessment of Mental Workload During Complex Human–Machine Interaction: Inferring Cognitive State from Signals External to the Operator |
| title_short | Continuous Assessment of Mental Workload During Complex Human–Machine Interaction: Inferring Cognitive State from Signals External to the Operator |
| title_sort | continuous assessment of mental workload during complex human machine interaction inferring cognitive state from signals external to the operator |
| topic | mental workload machine learning pilots simulator |
| url | https://www.mdpi.com/1424-8220/25/12/3624 |
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