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      OSCANN: Technical Characterization of a Novel Gaze Tracking Analyzer

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          Abstract

          Eye-movement analysis has grown exponentially in recent decades. The reason is that abnormalities in oculomotor movements are usually symptoms of injuries in the nervous system. This paper presents a novel regulated solution named OSCANN. OSCANN aims at providing an innovative tool for the control, management and visualization of oculomotor neurological examinations. This solution utilizes an eye-tracker sensor based on video electro-oculography (VOG) technology to capture eye movements and store them in video files. Such a sensor can store images at a rate of 100 frames per second. A characterization study was performed using twenty-two volunteers (13 male, 9 female, ages 22–45 years, mean 29.3 years, SD = 6.7) to assess the accuracy and precision specifications of OSCANN during oculomotor movement analysis. The accuracy was evaluated based on the offset, whereas precision was estimated with Root Means Square (RMS). Such a study reported values lower than 0.4 and 0.03 of accuracy and precision, respectively. These results suggest that OSCANN can be considered as a powerful tool to measure oculomotor movement alterations involved in some neurological disease progression.

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          Most cited references19

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          In the eye of the beholder: a survey of models for eyes and gaze.

          Despite active research and significant progress in the last 30 years, eye detection and tracking remains challenging due to the individuality of eyes, occlusion, variability in scale, location, and light conditions. Data on eye location and details of eye movements have numerous applications and are essential in face detection, biometric identification, and particular human-computer interaction tasks. This paper reviews current progress and state of the art in video-based eye detection and tracking in order to identify promising techniques as well as issues to be further addressed. We present a detailed review of recent eye models and techniques for eye detection and tracking. We also survey methods for gaze estimation and compare them based on their geometric properties and reported accuracies. This review shows that, despite their apparent simplicity, the development of a general eye detection technique involves addressing many challenges, requires further theoretical developments, and is consequently of interest to many other domains problems in computer vision and beyond.
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            THE MECHANICS OF HUMAN SACCADIC EYE MOVEMENT.

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              The influence of calibration method and eye physiology on eyetracking data quality.

              Recording eye movement data with high quality is often a prerequisite for producing valid and replicable results and for drawing well-founded conclusions about the oculomotor system. Today, many aspects of data quality are often informally discussed among researchers but are very seldom measured, quantified, and reported. Here we systematically investigated how the calibration method, aspects of participants' eye physiologies, the influences of recording time and gaze direction, and the experience of operators affect the quality of data recorded with a common tower-mounted, video-based eyetracker. We quantified accuracy, precision, and the amount of valid data, and found an increase in data quality when the participant indicated that he or she was looking at a calibration target, as compared to leaving this decision to the operator or the eyetracker software. Moreover, our results provide statistical evidence of how factors such as glasses, contact lenses, eye color, eyelashes, and mascara influence data quality. This method and the results provide eye movement researchers with an understanding of what is required to record high-quality data, as well as providing manufacturers with the knowledge to build better eyetrackers.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                09 February 2018
                February 2018
                : 18
                : 2
                : 522
                Affiliations
                [1 ]Aura Innovative Robotics, 28045 Madrid, Spain; ehernandez@ 123456aurarobotix.com (E.H.); santiago@ 123456aurarobotix.com (S.H.); dmolina@ 123456aurarobotix.com (D.M.); rafael@ 123456aurarobotix.com (R.A.)
                [2 ]Centre for Robotics and Automation, UPM-CSIC, José Gutiérrez Abascal Street, 28006 Madrid, Spain
                Author notes
                [* ]Correspondence: cecilia.garcia@ 123456upm.es ; Tel.: +34-911-389-638
                [†]

                These authors contributed equally to this work.

                Article
                sensors-18-00522
                10.3390/s18020522
                5855867
                29425134
                158e2e2c-e58b-4f89-a0e4-e535cfcd060f
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 04 December 2017
                : 06 February 2018
                Categories
                Article

                Biomedical engineering
                eye-tracker,neurodegenerative diseases,medical sensor,video electro-oculography,oculomotor movements,clinical practice,early diagnosis

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