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Real-time Workload Estimation Using Eye Tracking: A Bayesian Inference Approach
dc.contributor.authorLuo, Ruikun
dc.contributor.authorWeng, Yifan
dc.contributor.authorJayakumar, Paramsothy
dc.contributor.authorBrudnak, Mark
dc.contributor.authorPaul, Victor
dc.contributor.authorDesaraju, Vishnu
dc.contributor.authorStein, Jeffrey
dc.contributor.authorErsal, Tulga
dc.contributor.authorYang, X. Jessie
dc.date.accessioned2023-04-13T12:54:40Z
dc.date.available2023-04-13T12:54:40Z
dc.date.issued2023
dc.identifier.urihttps://hdl.handle.net/2027.42/176125en
dc.language.isoen_USen_US
dc.subjectHuman-Automation Interaction, Human-Autonomy Interaction, Bayesian Inference, Workload Estimationen_US
dc.titleReal-time Workload Estimation Using Eye Tracking: A Bayesian Inference Approachen_US
dc.typePreprinten_US
dc.subject.hlbsecondlevelIndustrial and Operations Engineering
dc.subject.hlbtoplevelEngineering
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/176125/1/IJHCI_Bayesian_inference_for_workload_estimation.pdf
dc.identifier.doihttps://dx.doi.org/10.7302/7064 https://doi.org/10.1080/10447318.2023.2205274
dc.identifier.sourceInternational Journal of Human-Computer Interactionen_US
dc.description.mapping-1en_US
dc.description.depositorSELFen_US
dc.working.doi10.7302/7064en_US
dc.owningcollnameIndustrial and Operations Engineering, Department of (IOE)


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