Recognising and explaining mental workload using low-interference method by fusing speech, ECG and eye tracking signals during simulated flight.
The mental workload of pilots is a critical factor influencing performance. This study aims to propose a low-interference mental workload recognition method based on aviation ecological validity. A simulated flight tracking experiment was conducted with twenty-six pilot cadets. Speech, ECG and eye-t...
| Publicado en: | Ergonomics Vol. 69; no. 7; pp. 1235 - 1253 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jul2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196440496&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196440496 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00140139 ERO jtl: Ergonomics issn: 00140139 maglogo: Y pubinfo: dt: Jul2026 vid: 69 iid: 7 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 196440496 185645476 196440496 196440496 10.1080/00140139.2025.2511877 196440496 ppf: 1235 ppct: 18 formats: tig: atl: Recognising and explaining mental workload using low-interference method by fusing speech, ECG and eye tracking signals during simulated flight. aug: au: Xu, Haixin Wang, Lijing Zou, Jiaying Zhang, Jun Li, Runhao Ma, Xianchao Wang, Yanlong Zhao, Yanzeng affil: School of Aeronautic Science and Engineering, Beihang University, Beijing, China sug: subj: Aviation Pilots Workload Electrocardiography Eye Movement Measurements Speech Human Male Young Adult Psychophysiology Simulations Machine Learning Heart Rate Physiology Pupil Physiology Task Performance and Analysis Algorithms Descriptive Statistics Male ab: The mental workload of pilots is a critical factor influencing performance. This study aims to propose a low-interference mental workload recognition method based on aviation ecological validity. A simulated flight tracking experiment was conducted with twenty-six pilot cadets. Speech, ECG and eye-tracking data were collected at different workload states corresponding to various training stages. The recognition performance of different feature combinations was evaluated using various machine learning model. Furthermore, the SHapley Additive exPlanations (SHAP) method was used to analyse the relationship between the features and workload state. The results indicate that MFCC_13, Mean NN, W and average pupil diameter were the most influential features in the recognition model. The highest recognition accuracy of 87.36% was achieved using the random forest method trained with full modalities. This study addresses the gap in the field of mental workload recognition regarding low-interference recognition methods and demonstrates its feasibility. PRACTITIONER SUMMARY: This study aimed to propose a novel low-interference method for recognising pilots' mental workload based on speech, ECG, and eye-tracking signals. Through feature combination and machine learning algorithms, recognition model achieved a high accuracy of 87.36%. The explainability analysis of the model was conducted, identifying the necessity of multimodal recognition. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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