IP Library › Granted Patent US 12,525,058
Granted Patent B2
US 12,525,058 · App. 17/734,896 · Granted Jan 13, 2026

Synchronizing eye movement parameters for a delay from a monitor output to a sensor

Inventors: Edmund Ben-Ami (Tel Aviv, IL); Micha Yochanan Breakstone (Austin, TX); Rotem Zvi Bar-Or (Jerusalem, IL); Vladimir Anisimov (Tel-Aviv, IL)
Assignee: NeuraLight Ltd.
G06V40/18A61B3/0025A61B3/113A61B5/163G06F3/013G06T7/0012G06T2207/20081G06T2207/30041
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Quick Facts
Patent No.
US 12,525,058
App. No.
17/734,896
Granted
Jan 13, 2026
Kind
B2
Abstract

Disclosed are systems and methods for extracting high resolution oculometric parameters and eye movement parameters. A video stream having a video of a face of a user is processed to obtain a set of oculometric parameters, such as eyelid data, iris data (e.g., iris translation, iris radius and iris rotation), and pupil data (e.g., pupil center and pupil radius). The oculometric parameters are generated at a first temporal resolution. The oculometric parameters are up sampled to increase the temporal resolution to a second temporal resolution. The oculometric parameters are then processed to generate various eye movement parameters such as blink parameter, pupil response parameter, saccade parameter, anti-saccade parameter, fixation parameter, or smooth pursuit parameter. The oculometric parameters are synchronized with a video stimulus presented on a user device prior to generating the eye movement parameters.

Claims (81)

1 . A method comprising:

obtaining a video stream from a client device associated with a user, the video stream including a video of a face of the user;

processing the video stream to obtain a set of oculometric parameters, wherein the set of oculometric parameters includes (a) eye tracking data that is indicative of a rotation of an iris associated with an eye of the user, and a location of a pupil and the iris, (b) eye size data indicative of a pupil radius and iris radius, and (c) eye openness data that is indicative of an openness of the eye, wherein the set of oculometric parameters is time series data;

synchronizing the set of oculometric parameters with a video stimulus presented on a display of the client device to obtain a synchronized set of oculometric parameters, wherein the synchronization is based on light modulation in the video stimulus, wherein the synchronizing comprises:

obtaining video stimulus information, wherein the video stimulus information includes stimulus type information, information descriptive of graphic features of the video stimulus, or brightness and color modulation information of the video stimulus;

obtaining reflected light information, wherein the reflected light information includes information regarding light reflected from a target in an environment in which the user is located, and red, green, and blue (RGB) intensity values sampled from the video stream received from the client device; and

synchronizing a time component in the set of the oculometric parameters time series data based on the video stimulus information and the reflected light information to generate the synchronized set of oculometric parameters;

obtaining, based on the synchronized set of oculometric parameters, a set of eye movement parameters that are indicative of a movement of the eye in response to the video stimulus; and

determining one or more digital markers of the user based on the set of eye movement parameters.

2 . The method of claim 1 , wherein the eye openness data includes iris visible fraction information, iris coverage asymmetry information, pupil visible fraction information, and pupil coverage asymmetry information.

3 . The method of claim 1 , wherein obtaining the set of eye movement parameters includes:

obtaining, based on the eye openness data, blink information that is indicative of blinking of the eye.

4 . The method of claim 3 , wherein obtaining the blink information includes:

obtaining, based on a time series of the eye openness data, a time series of blink presence data that is indicative of a presence or absence of a blink at a given instant; and

obtaining the blink information based on the time series of the blink presence data.

5 . The method of claim 3 , wherein the blink information includes (a) blink duration information that is indicative of a duration of blinking of the eye for a sequence of the video stimulus, and (b) blink frequency information that is indicative of a frequency of blink for the sequence.

6 . The method of claim 1 , wherein the eye size data includes iris radius and pupil radius.

7 . The method of claim 1 , wherein obtaining the set of eye movement parameters includes:

obtaining pupil response information based on the eye size data, blink presence data and a stimulus type of the video stimulus, wherein the pupil response information is indicative of a response of the pupil or the eye to the video stimulus.

8 . The method of claim 7 , wherein the pupil response information includes (a) pupil response amplitude that is indicative of a maximal variance of pupil diameter for an event, and (b) pupil response rate that is indicative of mean temporal rate of variance in the pupil diameter for the event.

9 . The method of claim 7 , wherein the pupil response information includes (a) pupil response latency that is indicative of a time interval between an event in the video stimulus and an initial change in pupil diameter, and (b) pupil response duration that is indicative of a time interval between the initial change in the pupil diameter following the event and a stabilization of change in the size of the pupil diameter.

10 . The method of claim 1 , wherein the eye tracking data includes iris translation and pupil location.

11 . The method of claim 1 , wherein obtaining the set of eye movement parameters includes:

obtaining saccade information based on the eye tracking data, blink presence data and a stimulus type of the video stimulus, wherein the saccade information is indicative of a rapid movement of the eye in response to the video stimulus.

12 . The method of claim 11 , wherein the saccade information includes at least one of (a) saccade latency, (b) saccade amplitude, (c) saccade peak velocity, (d) saccade overshoot amplitude, or (e) saccade correction time.

13 . The method of claim 1 , wherein obtaining the set of eye movement parameters includes:

obtaining anti-saccade information based on the eye tracking data, blink presence data and a stimulus type of the video stimulus, wherein the anti-saccade information is indicative of a movement of the eye in a direction opposite to a side where a target is presented in the video stimulus.

14 . The method of claim 13 , wherein the anti-saccade information includes at least one of anti-saccade latency, anti-saccade amplitude, anti-saccade peak velocity, anti-saccade overshoot amplitude, anti-saccade correction time, anti-saccade direction confusion rate, or anticipated anti-saccadic information.

15 . The method of claim 1 , wherein obtaining the set of eye movement parameters includes:

obtaining fixation information based on the eye tracking data, blink presence data and a stimulus type of the video stimulus, wherein the fixation information is indicative of an ability of the eye to maintain a gaze on a single location in the video stimulus.

16 . The method of claim 15 , wherein the fixation information includes micro-saccade amplitude distribution that is indicative of a distribution of both angular and amplitude around a target in the video stimulus.

17 . The method of claim 15 , wherein obtaining the fixation information includes obtaining square wave jerk information.

18 . The method of claim 17 , wherein the square wave jerk information includes at least one of square wave jerk amplitude, square wave jerk frequency, or square wave jerk offset duration.

19 . The method of claim 1 , wherein obtaining the set of eye movement parameters includes:

obtaining smooth pursuit information based on the eye tracking data, blink presence data and a stimulus type of the video stimulus, wherein the smooth pursuit information is indicative of a type of eye movement in which the eyes remain fixated on a moving object in the video stimulus.

20 . The method of claim 19 , wherein obtaining smooth pursuit information includes:

determining a movement of the eye as one of saccade, fixation, or smooth pursuit.

21 . The method of claim 19 , wherein obtaining the smooth pursuit information includes:

obtaining at least one of saccadic movement amplitude percentage, or smooth movement amplitude percentage.

22 . The method of claim 19 , wherein obtaining the smooth pursuit information includes:

obtaining saccadic movement temporal percentage that is indicative of a time fraction of saccadic movements for an act in the video stimulus.

23 . The method of claim 19 , wherein obtaining the smooth pursuit information includes:

obtaining fixation temporal percentage that is indicative of a time fraction of fixation for an act in the video stimulus.

24 . The method of claim 19 , wherein obtaining the smooth pursuit information includes:

obtaining smooth movement temporal percentage that is indicative of a time fraction of smooth movements for an act in the video stimulus.

25 . The method of claim 1 , wherein the set of eye movement parameters is measured for at least one of an event, a brick, a sequence, or an act.

26 . The method of claim 25 , wherein the event corresponds to an event of a specified duration in the video stimulus, wherein the brick corresponds to a collection of consecutive events in the video stimulus, wherein the sequence corresponds to a collection of consecutive bricks of the same type of brick, and wherein the act corresponds to a collection of consecutive sequences recorded for the user.

27 . The method of claim 1 , wherein processing the video stream includes:

obtaining the set of oculometric parameters at a first temporal resolution; and

up sampling the set of oculometric parameters to obtain the set of oculometric parameters at a second temporal resolution greater than the first temporal resolution.

28 . The method of claim 27 , wherein up sampling the set of oculometric parameters includes:

up sampling the set of oculometric parameters prior to synchronizing the set of oculometric parameters.

29 . The method of claim 27 , wherein up sampling the set of oculometric parameters includes:

obtaining a first plurality of sets of oculometric parameters at the first temporal resolution;

obtaining, using an eye tracking device configured to generate oculometric parameters at the second temporal resolution, the first plurality of sets of oculometric parameters at the second temporal resolution; and

training a machine learning model with the first plurality of sets of oculometric parameters to generate a predicted set of oculometric parameters at the second temporal resolution.

30 . The method of claim 1 further comprising:

generating a set of digital biomarkers based on the set of eye movement parameters.

31 . A non-transitory computer-readable medium having instructions that, when executed by a computer, cause the computer to execute a method, the method comprising:

obtaining a video stream from a client device associated with a user, the video stream including a video of a face of the user;

obtaining a time series of a set of oculometric parameters from the video stream, wherein the set of oculometric parameters includes (a) eye tracking data that is indicative of a rotation of an iris associated with an eye of the user, and a location of a pupil and the iris, (b) eye size data indicative of a pupil radius and iris radius, and (c) eye openness data that is indicative of an openness of the eye, wherein the set of oculometric parameters is obtained at a first temporal resolution;

up sampling the set of oculometric parameters to a second temporal resolution to generate an up sampled set of oculometric parameters, wherein the second temporal resolution is greater than the first temporal resolution;

synchronizing the up sampled set of oculometric parameters with a video stimulus presented on a display of the client device to obtain a synchronized set of oculometric parameters, wherein the synchronizing is based on light modulation in the video stimulus, wherein the synchronizing comprises:

obtaining video stimulus information, wherein the video stimulus information includes stimulus type information, information descriptive of graphic features of the video stimulus, or brightness and color modulation information of the video stimulus;

obtaining reflected light information, wherein the reflected light information includes information regarding light reflected from a target in an environment in which the user is located, and red, green, and blue (RGB) intensity values sampled from the video stream received from the client device; and

synchronizing a time component in the set of the up sampled set of oculometric parameters based on the video stimulus information and the reflected light information to generate the synchronized set of oculometric parameters; and

obtaining, based on the synchronized set of oculometric parameters, a set of eye movement parameters that are indicative of a movement of the eye in response to the video stimulus.

32 . The computer-readable medium of claim 31 , wherein obtaining the set of eye movement parameters includes:

obtaining, based on the eye openness data, blink information that is indicative of blinking of the eye;

obtaining pupillary response information based on the eye size data, the blink information, and a stimulus type of the video stimulus, wherein the pupillary response information is indicative of a response of the pupil to the video stimulus; and

obtaining at least one of saccade information, anti-saccade information, fixation information, or smooth pursuit information based on the eye tracking data, the blink information, and the stimulus type.

33 . A system, comprising:

a memory storing a set of instructions; and

a processor configured to execute the set of instructions to cause the system to perform a method of:

obtaining a video stream from a client device associated with a user, the video stream including a video of a face of the user;

processing the video stream to obtain a set of oculometric parameters, wherein the set of oculometric parameters includes (a) eye tracking data that is indicative of a rotation of an iris associated with an eye of the user, and a location of a pupil and the iris, (b) eye size data indicative of a pupil radius and iris radius, and (c) eye openness data that is indicative of an openness of the eye;

synchronizing the set of oculometric parameters with a video stimulus presented on a display of the client device to obtain a synchronized set of oculometric parameters, wherein the synchronizing is based on light modulation in the video stimulus, wherein the synchronizing comprises:

obtaining video stimulus information, wherein the video stimulus information includes stimulus type information, information descriptive of graphic features of the video stimulus, or brightness and color modulation information of the video stimulus;

obtaining reflected light information, wherein the reflected light information includes information regarding light reflected from a target in an environment in which the user is located, and red, green, and blue (RGB) intensity values sampled from the video stream received from the client device; and

synchronizing a time component in the set of the up sampled set of oculometric parameters based on the video stimulus information and the reflected light information to generate the synchronized set of oculometric parameters; and

obtaining, based on the synchronized set of oculometric parameters, a set of eye movement parameters that are indicative of a movement of the eye in response to the video stimulus.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 27, 2022
From: BEN-AMI, EDMUND; BREAKSTONE, MICHA YOCHANAN; BAR-OR, ROTEM ZVI; ANISIMOV, VLADIMIR
To: NEURALIGHT LTD.
Reel/Frame 060041/0783 →
Continuity (2)
Provisional Application 63183388 · May 3, 2021
Related Publication 20220354363A1 · Nov 10, 2022
References Cited (152)
US 6820979B1 · Stark et al. · 2004 [cited by applicant]
US 7428320B2 · Northcott et al. · 2008 [cited by applicant]
US 7753525B2 · Hara et al. · 2010 [cited by applicant]
US 7869627B2 · Northcott et al. · 2011 [cited by applicant]
US 8061840B2 · Mizuochi · 2011 [cited by applicant]
US 8132916B2 · Johansson · 2012 [cited by applicant]
US 9965860B2 · Nguyen et al. · 2018 [cited by applicant]
US 10016130B2 · Ganesan et al. · 2018 [cited by applicant]
US 10039445B1 · Torch · 2018 [cited by applicant]
US 10109056B2 · Nguyen et al. · 2018 [cited by applicant]
US 10231614B2 · Krueger · 2019 [cited by applicant]
US 10575728B2 · Zakariaie et al. · 2020 [cited by applicant]
US 10620700B2 · Publicover et al. · 2020 [cited by applicant]
US 10713813B2 · De Villers-Sidani et al. · 2020 [cited by applicant]
US 10713814B2 · De Villers-Sidani et al. · 2020 [cited by applicant]
US 11074714B2 · De Villers-Sidani et al. · 2021 [cited by applicant]
US 11382545B2 · Zakariaie et al. · 2022 [cited by applicant]
US 11503998B1 · De Villers-Sidani et al. · 2022 [cited by applicant]
US 11513593B2 · Drozdov et al. · 2022 [cited by applicant]
US 11514720B2 · Haimovitch-Yogev et al. · 2022 [cited by applicant]
US 11556741B2 · Dierkes et al. · 2023 [cited by applicant]
US 11776315B2 · Haimovitch-Yogev et al. · 2023 [cited by applicant]
US 12033432B2 · Ben-Ami et al. · 2024 [cited by applicant]
US 20050228236A1 · Diederich et al. · 2005 [cited by applicant]
US 20070009169A1 · Bhattacharjya · 2007 [cited by applicant]
US 20090018419A1 · Torch · 2009 [cited by examiner]
US 20110077548A1 · Torch · 2011 [cited by examiner]
US 20140152792A1 · Krueger · 2014 [cited by examiner]
US 20140171756A1 · Waldorf · 2014 [cited by examiner]
US 20140313488A1 · Kiderman · 2014 [cited by examiner]
US 20140320397A1 · Hennessey · 2014 [cited by examiner]
US 20140364761A1 · Benson et al. · 2014 [cited by applicant]
US 20150077543A1 · Kerr et al. · 2015 [cited by applicant]
US 20150293588A1 · Strupczewski · 2015 [cited by examiner]
US 20160150955A1 · Kiderman · 2016 [cited by examiner]
US 20170135577A1 · Komogortsev · 2017 [cited by examiner]
US 20170151089A1 · Chernak · 2017 [cited by applicant]
US 20170231490A1 · Toth et al. · 2017 [cited by applicant]
US 20170249434A1 · Brunner · 2017 [cited by applicant]
US 20170364732A1 · Komogortsev · 2017 [cited by applicant]
US 20180018515A1 · Spizhevoy et al. · 2018 [cited by applicant]
US 20190239790A1 · Gross · 2019 [cited by examiner]
US 20190246969A1 · Thomas et al. · 2019 [cited by applicant]
US 20200268296A1 · Alcaide et al. · 2020 [cited by applicant]
US 20200305708A1 · Krueger · 2020 [cited by applicant]
US 20200323480A1 · Shaked et al. · 2020 [cited by applicant]
US 20200405148A1 · Tran · 2020 [cited by applicant]
US 20210174959A1 · Abel Fernandez · 2021 [cited by applicant]
US 20210186318A1 · Yellin et al. · 2021 [cited by applicant]
US 20210186395A1 · Zakariaie et al. · 2021 [cited by applicant]
US 20210186397A1 · Weisberg et al. · 2021 [cited by applicant]
US 20210228142A1 · Sheehy et al. · 2021 [cited by applicant]
US 20210248720A1 · Ziesche et al. · 2021 [cited by applicant]
US 20210327595A1 · Abdallah · 2021 [cited by applicant]
US 20220019791A1 · Drozdov et al. · 2022 [cited by applicant]
US 20220180993A1 · Kuperman · 2022 [cited by examiner]
US 20220206571A1 · Drozdov et al. · 2022 [cited by applicant]
US 20220211310A1 · Zakariaie et al. · 2022 [cited by applicant]
US 20220236797A1 · Drozdov et al. · 2022 [cited by applicant]
US 20220301294A1 · Boutinon · 2022 [cited by applicant]
US 20220313083A1 · Zakariaie et al. · 2022 [cited by applicant]
US 20220351545A1 · Ben-Ami et al. · 2022 [cited by applicant]
US 20220390771A1 · David et al. · 2022 [cited by applicant]
US 20230074569A1 · Drozdov et al. · 2023 [cited by applicant]
US 20230233072A1 · Haimovitch-Yogev et al. · 2023 [cited by applicant]
US 20230334326A1 · Haimovitch-Yogev et al. · 2023 [cited by applicant]
CA 2618352A1 · 2007 [cited by applicant]
CN 106265006A · 2017 [cited by applicant]
EP 1947604A1 · 2008 [cited by applicant]
EP 3140780B1 · 2020 [cited by applicant]
WO WO2020190648A1 · 2020 [cited by applicant]
WO 2020235939A2 · 2020 [cited by applicant]
WO 2021097300A1 · 2021 [cited by applicant]
Qidwai, U., and Qidwai, U., “Blind Deconvolution for Retinal Image Enhancement,” IEEE EMBS Conference on Biomedical Engineering and Sciences pp. 20-25, (2010). [cited by applicant]
Torres-Salomao, L.A., et al., “Pupil Diameter Size Marker for Incremental Mental Stress Detection,” International Conference on E-Health Networking, Application & Services pp. 286-291, (2015). [cited by applicant]
International Search Report and Written Opinion of the International Searching Authority dated Oct. 27, 2022, Issued in corresponding International Application No. PCT/US2022/027312 (13 pgs.). [cited by applicant]
Harold E. Bedell et al., “Eye movement testing in clinical examination”, Vision Research 90 (2013) pp. 32-37. [cited by applicant]
Akinyelu, A.A. and Blignaut, P., “Convolutional Neural Network-Based Technique for Gaze Estimation on Mobile Devices,” Frontiers in Artificial Intelligence 4:796825 11 pages, Frontiers Media SA, Switzerland (Jan. 2022). [cited by applicant]
Alnajar, F., et al., “Auto-Calibrated Gaze Estimation Using Human Gaze Patterns,” International Journal of Computer Vision 124:223-236, (2017). [cited by applicant]
Bafna, T., et al., “EyeTell: Tablet-based Calibration-free Eye-typing Using Smooth-pursuit Movements,” Proceedings of the ETRA '21 Short Papers, (2021). [cited by applicant]
Chernov, N., et al., “Fitting Quadratic Curves to Data Points,” British Journal of Mathematics & Computer Science 4(1):33-60, (2014). [cited by applicant]
Guri, M., et al., “Brightness: Leaking Sensitive Data from Air-Gapped Workstations via Screen Brightness,” IEEE 12th CMI Conference on Cybersecurity and Privacy 7 pages, (2020). [cited by applicant]
Hoffner, S., et al., “Gaze Tracking Using Common Webcams,” Osnabruck University, (Feb. 2018). [cited by applicant]
Hou, L., et al., “Illumination-Based Synchronization of High-Speed Vision Sensors,” Sensors 10(6):5530-5547, Basel, Switzerland (2010). [cited by applicant]
Hutt, S. and D'Mello, S.K., “Evaluating Calibration-free Webcam-based Eye Tracking for Gaze-based User Modeling,” ICMI '22: Proceedings of the 2022 International Conference on Multimodal Interaction 224-235, (Nov. 2022). [cited by applicant]
Liu, G., et al., “A Differential Approach for Gaze Estimation,” IEEE transactions on pattern analysis and machine intelligence 43(3):1092-1099, IEEE Computer Society, United States (Mar. 2021). [cited by applicant]
Model, D., “A Calibration Free Estimation of the Point of Gaze and Objective Measurement of Ocular Alignment in Adults and Infants,” University of Toronto 160 pages, (2011). [cited by applicant]
Mollers, Maximilian., “Calibration-Free Gaze Tracking an Experimental Analysis,” RWTH Aachen University 111 pages, (2007). [cited by applicant]
Park, S., et al., “Few-Shot Adaptive Gaze Estimation,” Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 13 pages, (2019). [cited by applicant]
Patel, A.S., et al., “Optical Axes and Angle Kappa,” American Academy of Ophthalmology, (Oct. 2021). [cited by applicant]
Pfeuffer, K., et al., “Pursuit Calibration: Making Gaze Calibration Less Tedious and More Flexible,” UIST 13 Proceedings of the 26th Annual Acm Symposium on User Interface Software and Technology 261-270, (Oct. 2013). [cited by applicant]
Pi, J. and Shi, B.E., et al., “Task-embedded Online Eye-tracker Calibration for Improving Robustness to Head Motion,” Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications ETRA '19 8:1-9, (Jun. 2… [cited by applicant]
Smith, J.D., “Viewpointer: Lightweight Calibration-free Eye Tracking for Ubiquitous Handsfree Deixis,” UIST 05: Proceedings of the 18th Annual ACM Symposium on User Interface Software and Technology, 84 pages (2005). [cited by applicant]
Valliappan, N., et al., “Accelerating Eye Movement Research via Accurate and Affordable Smartphone Eye Tracking,” Supplementary Information, 18 pages, (Jul. 2020). [cited by applicant]
Wang, K. and JI, Qiang., “3D Gaze Estimation Without Explicit Personal Calibration,” Pattern Recognition 70:216-227, Elsevier, (Jul. 2018). [cited by applicant]
De Almeida Junior, F.L., et al., “Image Quality Treatment to Improve Iris Biometric Systems,” Infocomp Journal of Computer Science 16(1-2):21-30, (2017). [cited by applicant]
Hooge, I.T.C., et al., “Gaze Tracking Accuracy in Humans: One Eye is Sometimes Better Than Two,” Behavior Research Methods 51(6):2712-2721, Springer, United States (Dec. 2019). [cited by applicant]
Joyce, D.S., et al., “Melanopsin-mediated Pupil Function is Impaired in Parkinson's Disease,” Scientific Reports 8(1):7796, Nature Publishing Group, United Kingdom (May 2018). [cited by applicant]
Kelbsch, C., et al., “Standards in Pupillography,” Frontiers in Neurology 10(129):1-26, Frontiers Research Foundation, Switzerland (Feb. 2019). [cited by applicant]
Schweitzer, R and Rolfs, M., “An Adaptive Algorithm for Fast and Reliable Online Saccade Detection,” Behavior Research Methods 52(3):1122-1139, Springer, United States (Jun. 2020). [cited by applicant]
Villers-Sidani, E.D., et al., “A Novel Tablet-based Software for the Acquisition and Analysis of Gaze and Eye Movement Parameters: a Preliminary Validation Study in Parkinson's Disease,” Frontiers in Neurology 14(120473… [cited by applicant]
Villers-Sidani, E.D., et al., “Oculomotor Analysis to Assess Brain Health: Preliminary Findings From a Longitudinal Study of Multiple Sclerosis Using Novel Tablet-based Eye-tracking Software,” Frontiers in Neurology 14(… [cited by applicant]
International Search Report dated Sep. 19, 2022, issued in corresponding International Application No. PCT/US2022/027201 (2 pgs.). [cited by applicant]
Written Opinion of the International Searching Authority dated Sep. 19, 2022, issued in corresponding International Application No. PCT/US2022/027201 (8 pgs.). [cited by applicant]
Diana Borza et al., “Real-Time Detection and Measurement of Eye Features from Color Images”, Sensors 2016, 16, 1105, pp. 1-24. [cited by applicant]
Siyuan Chen et al., “Eyelid and Pupil Landmark Detection and Blink Estimation Based on Deformable Shape Models for Near-Field Infrared Video”, Frontiers in ICT, Oct. 2019, vol. 6, Article 18, pp. 1-11. [cited by applicant]
Frank A. Rasulo et al., “Essential Noninvasive Multimodality Neuromonitoring for the Critically Ill Patient”, Critical Care, 24, Article No. 100 (2020) 13 pgs. [cited by applicant]
Jamaludin et al., “Deblurring of noisy iris images in iris recognition,” Bulletin of Electrical Engineering and Informatics (BEEI), vol. 10, No. 1, Feb. 2021; pp. 156-159. [cited by applicant]
Mahanama et al., “Eye Movement and Pupil Measures: a Review,” Frontiers in Computer Science, vol. 3, No. 733531; pp. 1-22. [cited by applicant]
Zammarchi et al., “Application of Eye Tracking Technology in Medicine: a Bibliometric Analysis,” Vision, vol. 5, No. 56, Nov. 11, 2021; pp. 1-14. [cited by applicant]
Fink et al., “From pre-processing to advanced dynamic modeling of pupil data,” Behavior Research Methods, Jun. 22, 2023; 37 pages. [cited by applicant]
Yang et al., “Gaze Angle Estimate and Correction in Iris Recognition,” 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM), Dec. 9, 2014; 7 pages. [cited by applicant]
Lui et al., “Iris Image Deblurring Based on Refinement of Point Spread Function,” Chinese Conference on Biometric Recognition (CCBR), Lecture Notes in Computer Science (LNIP, vol. 7701), Dec. 4, 2012; 9 pages. [cited by applicant]
Zhang et al., “Luminance effects on pupil dilation in speech-in-noise recognition,” PLOS One, vol. 17, No. 12, Dec. 2, 2022; pp. 1-18. [cited by applicant]
Peysakhovich et al., “The impact of luminance on tonic and phasic pupillary responses to sustained cognitive load,” International Journal of Psychophysiology, vol. 112, Feb. 2017; pp. 40-45. [cited by applicant]
Cherng et al., “Background luminance effects on pupil size associated with emotion and saccade preparation,” Scientific Reports, vol. 10, No. 15718, Sep. 24, 2020; 12 pages. [cited by applicant]
Rodrigues A. C., “Response Surface Analysis: a Tutorial for Examining Linear and Curvilinear Effects,” Journal of Contemporary Administration, vol. 25, No. 6, Apr. 6, 2021; pp. 1-14. [cited by applicant]
“System and Method for Measuring Delay from Monitor Output to Camera Sensor,” Unpublished Disclosure; 16 pages. [cited by applicant]
Alessandro Serra et al., “Eye Movement Abnormalities in Multiple Sclerosis: Pathogenesis, Modeling and Treatment”, Frontiers in Neurology, vol. 9, No. 31, Feb. 5, 2018; pp. 1-7. [cited by applicant]
Valliappan et al., “Accelerating eye movement research via accurate and affordable smartphone eye tracking,” Nature Communications, vol. 11, No. 4553, Sep. 11, 2020; pp. 1-12. [cited by applicant]
Strobl et al., “Look me in the eye: evaluating the accuracy of smartphone-based eye tracking for potential application in autism spectrum disorder research,” BioMedical Enginering OnLine, vol. 18, No. 1, May 3, 2019; pp… [cited by applicant]
Krafka et al., “Eye Tracking for Everyone,” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 18, 2016; 9 pages. [cited by applicant]
Lai et al., “Measuring Saccade Latency Using Smartphone Cameras,” IEEE Journal of Biomedical and Health Informatics, Apr. 30, 2019; pp. 1-13. [cited by applicant]
Aljaafreh et al., “A Low-cost Webcam-based Eye Tracker and Saccade Measurement System,” International Journal of Circuits, Systems and Signal Processing, vol. 14, Apr. 2020; pp. 102-107. [cited by applicant]
Em Frohman et al., “Quantitative oculographic characterisation of internuclear ophthalmoparesis in multiple sclerosis: the versional dysconjugacy index Z score,” Journal of Neurology, Neurosurgery, and Psychiatry, vol. … [cited by applicant]
Fielding et al., “Ocular motor signatures of cognitive dysfunction in multiple sclerosis,” Nature Reviews Neurology, vol. 11, No. 11, Sep. 15, 2015; 10 pages. [cited by applicant]
Nij Bijvank et al., “Quantification of Visual Fixation in Multiple Sclerosis”, Investigative Ophthalmology & Visual Science, vol. 60, No. 5, Apr. 2019; pp. 1372-1383. [cited by applicant]
Alessandro Grillini et al., “Eye Movement Evaluation in Multiple Sclerosis and Parkinson's Disease Using a Standardized Oculomotor and Neuro-Ophthalmic Disorder Assessment (SONDA)”, Frontiers in Neurology, vol. 11, No. … [cited by applicant]
Christy K. Sheehy et al., “Fixational microsaccades: a quantitative and objective measure of disability in multiple sclerosis”, Multiple Sclerosis Journal, vol. 26, No. 3, Feb. 2020; pp. 1-11. [cited by applicant]
Nathaniel Lizak et al., “Impairment of Smooth Pursuit as a Marker of Early Multiple Sclerosis”, Frontiers in Neurology, vol. 7, No. 3, Nov. 2016; pp. 1-7. [cited by applicant]
Marisa Ferreira et al., “Using endogenous saccades to characterize fatigue in multiple sclerosis”, Multiple Sclerosis and Related Disorders, vol. 14, May 2017; pp. 1-7. [cited by applicant]
En et al., “Pupillary response to sparse multi focal stimuli in multiple sclerosis patients”. Multiple Sclerosis Journal, vol. 20, No. 7, Nov. 21, 2013; pp. 1-8. [cited by applicant]
De Seze J. et al., “Pupillary disturbances in multiple sclerosis: correlation with MRI findings”. Journal of the Neurological Sciences, vol. 188, Nos. 1-2, Jul. 15, 2001; pp. 37-41. [cited by applicant]
Polak PE et al., “Locus coeruleus damage and noradrenaline reductions in multiple sclerosis and experimental autoimmune encephalomyelitis,” Brain, vol. 134, No. 3, Feb. 2011; pp. 665-677. [cited by applicant]
Niestroy A. et al., “Neuro-ophthalmologic aspects of multiple sclerosis: using eye movements as a clinical and experimental tool,” Clinical Ophthalmology, vol. 1, No. 3, Sep. 2007; pp. 267-272. [cited by applicant]
Servillo G. et al., “Bedside tested ocular motor disorders in multiple sclerosis patients”. Multiple Sclerosis International, vol. 5, Apr. 2014; 5 pages. [cited by applicant]
T. C. Frohman et al., “Accuracy of clinical detection of INO in MS: corroboration with quantitative infrared oculography,” Neurology, vol. 61, No. 6, Oct. 2003; pp. 848-850. [cited by applicant]
Van Munster C. E. P. et al., “Outcome Measures in Clinical Trials for Multiple Sclerosis”. CNS Drugs, vol. 31, No. 3, Feb. 9, 2017; pp. 217-236. [cited by applicant]
Tur C et al., “Assessing treatment outcomes in multiple sclerosis trials and in the clinical setting,” Nature Reviews Neurology, vol. 14, Jan. 12, 2018; 164 pages. [cited by applicant]
Matza LS et al., “Multiple sclerosis relapse: Qualitative findings from clinician and patient interviews” Multiple Sclerosis and Related Disorders, vol. 27, Jan. 2019; 27 pages. [cited by applicant]
Felmingham K., “Comprehensive Guide to Post-Traumatic Stress Disorder: Chapter 69: Eye Tracking and PTSD,” Jan. 1, 2015; pp. 1241-1256. [cited by applicant]
Holzman et al., “Eye-Tracking Dysfunctions in Schizophrenic Patients and Their Relatives,” Archives of General Psychiatry, vol. 31, Aug. 1974; pp. 143-151. [cited by applicant]
Rayner K., “Eye Movements in Reading and Information Processing: 20 Years of Research,” Psychological Bulletin, vol. 124, No. 3, Nov. 1998; pp. 372-422. [cited by applicant]
Frohman et al., “The neuro-ophthalmology of multiple sclerosis”, The Lancet Neurology, vol. 4, No. 2, Feb. 2005; pp. 111-121. [cited by applicant]
Serra et al., “Role of eye movement examination and subjective visual vertical in clinical evaluation of multiple sclerosis”, Journal of Neurology, vol. 250, No. 5, May 2003; pp. 569-575. [cited by applicant]
Jakobsen J., “Pupillary function in multiple sclerosis” Acta Neurologica Scandinavica, vol. 82, No. 6, Dec. 1990; pp. 392-395. [cited by applicant]
Derwenskus J. et al., “Abnormal eye movements predict disability in MS: two-year follow-up”. Annals of the New York Academy of Sciences, vol. 1039, Apr. 2005; pp. 521-523. [cited by applicant]
Romero K. et al., “Neurologists' accuracy in predicting cognitive impairment in multiple sclerosis” Multiple Sclerosis and Related Disorders, vol. 4, No. 4, Jul. 2015; pp. 291-295. [cited by applicant]
Ahmad Hasasneh et al., “Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data”, Hindawi Journal of Engineering, vol. 2018, Apr. 30, 2018, 10 pgs. [cited by applicant]
Christian Johannes Schuler, “Machine Learning Approaches to Image Deconvolution”, Dissertation University of Tubingen, Germany, 2017, 142 pgs. [cited by applicant]
Petteri Teikari et al., “Embedded Deep Learning in Ophthalmology: Making Ophthalmic Imaging Smarter”, Therapeutic Advances in Ophthalmology 11 (2019), 21 pgs. [cited by applicant]
Bryan M. Williams et al., “Fast Blur Detection and Parametric Deconvolution of Retinal Fundus Images”, Fetal, Infant and Ophthalmic Medical Image Analysis, Springer, Cham. 2017, 8 pgs. [cited by applicant]