EEG-Based Evaluation of Mental Workload in a Simulated Industrial Human-Robot Interaction Task.
Background: The rapid advancement of robotics and artificial intelligence is poised to revolutionize industrial settings through widespread automation. This study investigates the impact of robotic assistance on human operator mental workload (MWL) within a simulated industrial environment. Utilizin...
| Publicado en: | Journal of Health Scope Vol. 14; no. 1; pp. 1 - 14 |
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| Autores principales: | , , , , , , |
| Formato: | pictorial protocol research tables/charts Journal Article |
| Publicado: |
Brieflands
Feb2025
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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=184342574&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184342574 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22518959 GXL5 jtl: Journal of Health Scope issn: 22518959 maglogo: N pubinfo: dt: Feb2025 vid: 14 iid: 1 pid: 69510 pub: Brieflands place: , <Blank> artinfo: ui: 184342574 184342574 184342574 10.5812/healthscope-158096 184342574 ppf: 1 ppct: 13 formats: tig: atl: EEG-Based Evaluation of Mental Workload in a Simulated Industrial Human-Robot Interaction Task. aug: au: Fazli, Babak Sajadi, Seyed Saman Jafari, Amir Homayoun Garosi, Ehsan Hosseinzadeh, Soheila Zakerian, Seyed Abolfazl Azam, Kamal affil: Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran sug: subj: Electroencephalography Mental Health Evaluation Workload Simulations Robotics Task Performance and Analysis Industry Work Environment Cognition Evaluation Occupational Safety Collaboration Human Adult Brain Physiology Analysis of Variance Repeated Measures Male Neuropsychological Tests Memory, Short Term Attention Funding Source Adult: 19-44 years Male ab: Background: The rapid advancement of robotics and artificial intelligence is poised to revolutionize industrial settings through widespread automation. This study investigates the impact of robotic assistance on human operator mental workload (MWL) within a simulated industrial environment. Utilizing electroencephalography (EEG) to measure changes in alpha and theta band power, we aim to identify the cognitive challenges associated with human-robot collaboration (HRC) and inform the design of safer and more efficient collaborative systems. Objectives: The main objective of the current study was to assess the MWL in a simulated industrial human-robot interaction (HRI) task. Methods: The EEG data were collected from 17 participants (aged 25 - 35 years) using a 64-channel system while they engaged in an ecologically valid robotic task that induced three distinct levels of cognitive load: Low, medium, and high. Subsequent analysis focused on EEG power within the alpha and theta frequency bands, employing repeated-measures ANOVA to assess the impact of cognitive load on brain activity. Results: A repeated-measures ANOVA revealed significant changes in EEG power across different task difficulty levels. The theta and alpha bands in F3, F4, and Fz, as well as the alpha, beta, and gamma bands in P3, P4, and Pz, emerged as promising indicators for differentiating between varying levels of cognitive load in human-robot tasks. Conclusions: Electroencephalography spectral power, particularly within the alpha and theta frequency bands, is a reliable indicator of human MWL. These frequency bands exhibit dynamic changes in response to fluctuating cognitive demands, especially in human-robotic interaction tasks. pubtype: Academic Journal doctype: pictorial protocol research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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