Enabling Saccade Latency Measurements with Consumer-Grade Cameras

Lai, Hsin-Yu; Saavedra-Pena, Gladynel; Sodini, C.G.; Heldt, Thomas; Sze, Vivienne

doi:10.1109/icip.2018.8451354

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…daily) than is currently possible with laboratory-based eye-movement assessment and thereby potentially aiding the characterization of disease progression and quantification of patient state. A preliminary version of this work has been reported in [30] and [31]. A subject is seated facing an iPhone, (in some experiments) a high-speed camera, and a laptop displaying the visual stimulus task.…”

Section: Introductionmentioning

confidence: 99%

Measuring Saccade Latency Using Smartphone Cameras

Lai

Saavedra-Pena

Sodini

et al. 2020

IEEE J. Biomed. Health Inform.

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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 modelbased approach for saccade onset determination that provides automated signal-quality quantification and artifact rejection. Results: Simultaneous recordings with a smartphone and a highspeed 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-10ms 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.

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Section: Introductionmentioning

confidence: 99%

Measuring Saccade Latency Using Smartphone Cameras

Lai

Saavedra-Pena

Sodini

et al. 2020

IEEE J. Biomed. Health Inform.

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“…5. Building on our prior work [13]- [15], we used iTracker-face to estimate the gaze of the subject (Fig. 6).…”

Section: A Measurement Pipeline Requirementsmentioning

confidence: 99%

“…However, these features are commonly measured with dedicated infrared cameras and chinrests, which limits the measurements to the doctor's office or the neurophysiological laboratory. In our previous work [13]- [15], we showed that we can accurately and robustly determine saccade latency from recordings obtained with a smartphone camera.…”

Section: Introductionmentioning

confidence: 99%

App-based saccade latency and error determination across the adult age spectrum

Lai,

Saavedra-Pena,

Sodini

et al. 2020

Preprint

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We aid in neurocognitive monitoring outside the hospital environment by enabling appbased measurements of visual reaction time (saccade latency) and error rate in a cohort of subjects spanning the adult age spectrum. Methods: We developed an iOS app to record subjects with the frontal camera during proand anti-saccade tasks. We further developed automated algorithms for measuring saccade latency and error rate that take into account the possibility that it might not always be possible to determine the eye movement from appbased recordings. Results: To measure saccade latency on a tablet, we ensured that the absolute timing error between on-screen task presentation and the camera recording is within 5 ms. We collected over 235,000 eye movements in 80 subjects ranging in age from 20 to 92 years, with 96% of recorded eye movements either declared good or directional errors. Our error detection code achieved a sensitivity of 0.97 and a specificity of 0.97. Confirming prior reports, we observed a positive correlation between saccade latency and age while the relationship between error rate and age was not significant. Finally, we observed significant intra-and inter-subject variations in saccade latency and error rate distributions, which highlights the importance of individualized tracking of these visual digital biomarkers. Conclusion and Significance: Our system and algorithms allow ubiquitous tracking of saccade latency and error rate, which opens up the possibility of quantifying patient state on a finer timescale in a broader population than previously possible.

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