Research on identification of flight cadets' cognitive load based on multi-source physiological data and CGAN-DBN model.
Modern aircraft cockpit system is highly information-intensive. Pilots often need to receive a large amount of information and make correct judgments and decisions in a short time. However, cognitive load can affect their ability to perceive, judge and make decisions accurately. Furthermore, the exc...
| Publicado en: | Ergonomics Vol. 68; no. 5; pp. 737 - 756 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts tracings Journal Article |
| Publicado: |
Taylor & Francis Ltd
May2025
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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=184595077&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184595077 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00140139 ERO jtl: Ergonomics issn: 00140139 maglogo: Y pubinfo: dt: May2025 vid: 68 iid: 5 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 184595077 178475495 184595077 184595077 10.1080/00140139.2024.2380340 184595077 ppf: 737 ppct: 19 formats: tig: atl: Research on identification of flight cadets' cognitive load based on multi-source physiological data and CGAN-DBN model. aug: au: Pan, Ting Wang, Haibo Si, Haiqing Li, Yixuan Li, Gen Zhu, Yijin affil: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China sug: subj: Pilots Aircraft Cognition Workload Task Performance and Analysis Human Funding Source Male Adult Experimental Studies Generative Adversarial Networks Neural Networks (Computer) Electrocardiography Scales Eye Movements Adult: 19-44 years Male ab: Modern aircraft cockpit system is highly information-intensive. Pilots often need to receive a large amount of information and make correct judgments and decisions in a short time. However, cognitive load can affect their ability to perceive, judge and make decisions accurately. Furthermore, the excessive cognitive load will induce incorrect operations and even lead to flight accidents. Accordingly, the research on cognitive load is crucial to reduce errors and even accidents caused by human factors. By using physiological acquisition systems such as eye movement, ECG, and respiration, multi-source physiological signals of flight cadets performing different flight tasks during the flight simulation experiment are obtained. Based on the characteristic indexes extracted from multi-source physiological data, the CGAN-DBN model is established by combining the conditional generative adversarial networks (CGAN) model with the deep belief network (DBN) model to identify the flight cadets' cognitive load. The research results show that the flight cadets' cognitive load identification based on the CGAN-DBN model established has high accuracy. And it can effectively identify the cognitive load of flight cadets. The research paper has important practical significance to reduce the flight accidents caused by the high cognitive load of pilots. Practitioner Summary: In our study, a highly accurate cognitive load identification model for flight cadets was established by using multi-source physiological data. Moreover, it provides a theoretical basis for identifying the cognitive load of pilots through wearable physiological devices. Our intent is to catalyse further research and technological development. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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