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dc.contributor.authorPignoni, Giovanni
dc.contributor.authorKomandur, Sashidharan
dc.contributor.authorVolden, Frode
dc.date.accessioned2021-01-11T15:43:28Z
dc.date.available2021-01-11T15:43:28Z
dc.date.created2020-11-18T11:03:42Z
dc.date.issued2020
dc.identifier.citationIEEE Sensors Journal. 2020, .en_US
dc.identifier.issn1530-437X
dc.identifier.urihttps://hdl.handle.net/11250/2722435
dc.description.abstractEye-tracking is now above and beyond the sole measurement of visual attention. Amongst the multiple measures it provides, some have been explored as a measure of cognitive workload (CW). One such measure is pupil diameter. Although the relationship between pupil size and CW has been extensively documented, pupil diameter is primarily impacted by luminance variations while the cognitive workload has a relatively minor influence. Therefore luminance variations have to be accounted for, either in the experimental design or in the data processing to avoid the masking of the CW effects. This has meant that the use of pupillometry for the measurement of anything but the pupillary light response, has been restricted to highly controlled lighting conditions in a laboratory. This study proposes a new method that uses point of view (POV) video in conjunction with a luminance measurement sensor to dynamically estimate the luminance of the visual stimuli. As currently available off the shelf eye trackers are usually not equipped to record luminance variations, a luminance sensor was added to a commercial eye tracker. Eye-tracking gaze data, POV video recording of the operator/observer and a head-mounted (POV) luminance sensor together estimate the expected pupil diameter. This estimate over time is due to sole influence of luminance variations. This expected pupil diameter is used as baseline for the cognitive workload. The method was validated in laboratory conditions with controlled visual stimuli. The method reliably measures induced cognitive workload despite luminance variation.en_US
dc.language.isoengen_US
dc.titleAccounting for effects of variation in luminance in pupillometry for field measurements of cognitive workloaden_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© 2020 IEEEen_US
dc.source.pagenumber8en_US
dc.source.journalIEEE Sensors Journalen_US
dc.identifier.doi10.1109/JSEN.2020.3038291
dc.identifier.cristin1849154
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.fulltextpreprint
cristin.fulltextoriginal
cristin.qualitycode2


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