Differentiating pilot distress and eustress via multimodal physiology: towards enhanced human-system integration in intelligent cockpits.
Ensuring safety in next-generation intelligent cockpits demands accurate assessment of pilot states, particularly distinguishing between eustress and distress. Traditional stress monitoring lacks this nuance and struggles across varying flight tasks. This study proposes a multimodal neuro-cardiac fr...
| Publicado en: | Ergonomics Vol. 69; no. 10; pp. 2079 - 2102 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Oct2026
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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=196828148&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196828148 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00140139 ERO jtl: Ergonomics issn: 00140139 maglogo: Y pubinfo: dt: Oct2026 vid: 69 iid: 10 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 196828148 187591329 196828148 196828148 10.1080/00140139.2025.2553131 196828148 ppf: 2079 ppct: 23 formats: tig: atl: Differentiating pilot distress and eustress via multimodal physiology: towards enhanced human-system integration in intelligent cockpits. aug: au: Zhao, Yanzeng Zhu, Keyong Guo, Wei Xu, Haixin Zhang, Jun Zou, Jiaying Li, Runhao Wang, Lijing affil: International Innovation Institute, Beihang University, Hangzhou, China sug: subj: Pilots Stress, Psychological Diagnosis Equipment Design Occupational Safety Ergonomics User-Computer Interface Electrocardiography Spectroscopy, Near-Infrared Human Biological Markers Analysis Simulations Machine Learning Algorithms Validity Task Performance and Analysis Monitoring, Physiologic ab: Ensuring safety in next-generation intelligent cockpits demands accurate assessment of pilot states, particularly distinguishing between eustress and distress. Traditional stress monitoring lacks this nuance and struggles across varying flight tasks. This study proposes a multimodal neuro-cardiac framework combining functional near-infrared spectroscopy (fNIRS) and electrocardiography (ECG) to differentiate eustress and distress across tasks. Physiological data were collected from 35 participants under simulated flight missions inducing both stress types. Eleven features showing significant differentiation were identified and used to train classification models with machine learning algorithms. The model achieved 83.04% accuracy across tasks, and up to 90.83% within single tasks. These findings demonstrate the robustness of fNIRS-ECG-based monitoring in pilot stress classification. The proposed method offers objective biomarkers critical for adaptive intelligent cockpit systems, contributing directly to flight safety and human-machine interaction optimisation. PRACTITIONER SUMMARY: This study is the first to distinguish eustress and distress in pilots using multimodal physiological signals (fNIRS and ECG). Machine learning models achieved up to 90.83% accuracy, offering objective indicators of pilot stress states, supporting the development of adaptive intelligent cockpit systems, promoting optimised human-machine interaction and stress-aware decision-making. HIGHLIGHTS: Differentiates distress and eustress, not just stress, aligning with aviation safety. Uses low-interference methods, proving practical feasibility for real-world use. Applies a cross-task approach, enhancing scenario generalisation. Combines ergonomic application with insights into stress physiology. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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