Workers/crews’ mental workload dynamics in closed cabins: A task-difficulty-adaptive random forest model for instrument monitoring tasks using multimodal biosignals.
Accurate assessment of operator mental workload (MWL) is critical for ensuring safety in closed-cabin environments, yet traditional contact-based sensors are intrusive.This study aimed to develop and validate a fully non-contact, multimodal physiological monitoring framework for assessing levels of...
| Publicado en: | Work p. 1 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Sage Publications Inc.
Apr2026
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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=193131087&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193131087 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: Apr2026 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 193131087 10.1177/10519815261440247 193131087 ppf: 1 formats: fmt: @attributes: type: P tig: atl: Workers/crews’ mental workload dynamics in closed cabins: A task-difficulty-adaptive random forest model for instrument monitoring tasks using multimodal biosignals. aug: au: Wang, Hanyu Fan, Hao Gu, Sen Zhang, Yahan Huang, Yuexin Sun, Yiwei Sun, Jianhua Zhou, Yao Chen, Dengkai affil: Key Laboratory of Industrial Design and Ergonomics, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an, China sug: ab: Accurate assessment of operator mental workload (MWL) is critical for ensuring safety in closed-cabin environments, yet traditional contact-based sensors are intrusive.This study aimed to develop and validate a fully non-contact, multimodal physiological monitoring framework for assessing levels of Mental Workload in closed-cabin environments.This study employed a millimeter-wave radar and a camera to non-contactually acquire ECG, respiration, and eye movement signals from 30 participants performing a four-level monitoring task.Physiological features demonstrated a significant correlation with task difficulty. A Random Forest classifier built on these features achieved 83.33% accuracy in distinguishing the four MWL levels.This study validates a fully non-contact, multimodal physiological monitoring framework, providing a practical paradigm for non-intrusive, continuous cognitive state assessment in safety-critical domains. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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