Measuring Saccade Latency using Smartphone Cameras
Objective: 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...
| Main Authors: | , , , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers (IEEE)
2020
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| Online Access: | https://hdl.handle.net/1721.1/124009 |
| _version_ | 1826195860833173504 |
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| author | Lai, Hsin-Yu Saavedra-Pena, Gladynel Sodini, Charles G. Sze, Vivienne Heldt, Thomas |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Lai, Hsin-Yu Saavedra-Pena, Gladynel Sodini, Charles G. Sze, Vivienne Heldt, Thomas |
| author_sort | Lai, Hsin-Yu |
| collection | MIT |
| description | Objective: 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. |
| first_indexed | 2024-09-23T10:16:43Z |
| format | Article |
| id | mit-1721.1/124009 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:16:43Z |
| publishDate | 2020 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1240092022-09-30T20:06:54Z Measuring Saccade Latency using Smartphone Cameras Lai, Hsin-Yu Saavedra-Pena, Gladynel Sodini, Charles G. Sze, Vivienne Heldt, Thomas Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Microsystems Technology Laboratories Objective: 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. 2020-03-04T20:00:25Z 2020-03-04T20:00:25Z 2019-04 Article http://purl.org/eprint/type/JournalArticle 2168-2194 2168-2208 https://hdl.handle.net/1721.1/124009 Lai, Hsin-Yu et al. "Measuring Saccade Latency using Smartphone Cameras." IEEE Journal on Biomedical and Health Informatics (April 2019) © 2019 IEEE http://dx.doi.org/10.1109/jbhi.2019.2913846 IEEE Journal on Biomedical and Health Informatics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Thomas Heldt |
| spellingShingle | Lai, Hsin-Yu Saavedra-Pena, Gladynel Sodini, Charles G. Sze, Vivienne Heldt, Thomas Measuring Saccade Latency using Smartphone Cameras |
| title | Measuring Saccade Latency using Smartphone Cameras |
| title_full | Measuring Saccade Latency using Smartphone Cameras |
| title_fullStr | Measuring Saccade Latency using Smartphone Cameras |
| title_full_unstemmed | Measuring Saccade Latency using Smartphone Cameras |
| title_short | Measuring Saccade Latency using Smartphone Cameras |
| title_sort | measuring saccade latency using smartphone cameras |
| url | https://hdl.handle.net/1721.1/124009 |
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