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dc.contributor.authorLai, Hsin-Yu
dc.contributor.authorSaavedra-Pena, Gladynel
dc.contributor.authorSodini, Charles G.
dc.contributor.authorSze, Vivienne
dc.contributor.authorHeldt, Thomas
dc.date.accessioned2020-03-04T20:00:25Z
dc.date.available2020-03-04T20:00:25Z
dc.date.issued2019-04
dc.identifier.issn2168-2194
dc.identifier.issn2168-2208
dc.identifier.urihttps://hdl.handle.net/1721.1/124009
dc.description.abstractObjective: Accurate quantification of neurodegenerative disease progression is an ongoing challenge that complicates efforts to understand and treat these conditions. Clinical studies have shown that eye movement features may serve as objective biomarkers to support diagnosis and tracking of disease progression. Here, we demonstrate that saccade latency - an eye movement measure of reaction time - can be measured robustly outside of the clinical environment with a smartphone camera. Methods: To enable tracking of saccade latency in large cohorts of patients and control subjects, we combined a deep convolutional neural network for gaze estimation with a model-based approach for saccade onset determination that provides automated signal-quality quantification and artifact rejection. Results: Simultaneous recordings with a smartphone and a high-speed camera resulted in negligible differences in saccade latency distributions. Furthermore, we demonstrated that the constraint of chinrest support can be removed when recording healthy subjects. Repeat smartphone-based measurements of saccade latency in eleven self-reported healthy subjects resulted in an intraclass correlation coefficient of 0.76, showing our approach has good to excellent test-retest reliability. Additionally, we conducted over 19,000 saccade latency measurements in 29 self-reported healthy subjects and observed significant intra- and inter-subject variability, which highlights the importance of individualized tracking. Lastly, we showed that with around 65 measurements we can estimate mean saccade latency to within less-than-10-ms precision, which takes within four minutes with our setup. Conclusion and Significance: By enabling repeat measurements of saccade latency and its distribution in individual subjects, our framework opens the possibility of quantifying patient state on a finer timescale in a broader population than previously possible.en_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/jbhi.2019.2913846en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceThomas Heldten_US
dc.titleMeasuring Saccade Latency using Smartphone Camerasen_US
dc.typeArticleen_US
dc.identifier.citationLai, Hsin-Yu et al. "Measuring Saccade Latency using Smartphone Cameras." IEEE Journal on Biomedical and Health Informatics (April 2019) © 2019 IEEEen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Microsystems Technology Laboratoriesen_US
dc.relation.journalIEEE Journal on Biomedical and Health Informaticsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.date.submission2019-07-12T00:15:03Z


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