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Eye movement prediction by Kalman filter with integrated linear horizontal oculomotor plant mechanical model

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TLDR
A linear horizontal oculomotor plant mechanical model is developed that consists of the eye globe and two extraocular muscles: lateral and medial recti and provides continuous eye movement prediction with a high degree of accuracy.
Abstract
The goal of this paper is to predict future horizontal eye movement trajectories within a specified time interval. To achieve this goal a linear horizontal oculomotor plant mechanical model is developed. The model consists of the eye globe and two extraocular muscles: lateral and medial recti. The model accounts for such anatomical properties of the eye as muscle location, elasticity, viscosity, eye-globe rotational inertia, muscle active state tension, length tension and force velocity relationships. The mathematical equations describing the oculomotor plant mechanical model are transformed into a Kalman filter form. Such transformation provides continuous eye movement prediction with a high degree of accuracy. The model was tested with 21 subjects and three multimedia files. Practical application of this model lies with direct eye gaze input and interactive displays systems as a method to compensate for detection, transmission and processing delays.

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Citations
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Biometric identification via an oculomotor plant mathematical model

TL;DR: A new biometric approach that involves an estimation of the unique oculomotor plant or eye globe muscle parameters from an eye movement trace provides a number of advantages for biometric identification: it includes both behavioral and physiological human attributes, is difficult to counterfeit, non-intrusive, and could easily be incorporated into existing biometric systems to provide an extra layer of security.
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Eye-tracking data quality as affected by ethnicity and experimental design

TL;DR: It was found that accuracy and precision for Asian participants was worse than that for African and Caucasian participants, and operating distance had the largest effect on data quality, since it affected all indicators for all ethnic groups.
References
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Book

Eye Movements and Vision

Book

introduction to random signals and applied kalman filtering

TL;DR: In this paper, the Discrete Kalman Filter (DFL) is used for smoothing and prediction linearization in the Global Positioning System (GPS) and a case study is presented.
Book

Eye Tracking Methodology: Theory and Practice

TL;DR: To the Human Visual System (HVS), Visual Attention, Neurological Substrate of the HVS, and Neuroscience and Psychology, and Industrial Engineering and Human Factors.
Proceedings ArticleDOI

Identifying fixations and saccades in eye-tracking protocols

TL;DR: A taxonomy of fixation identification algorithms is proposed that classifies algorithms in terms of how they utilize spatial and temporal information in eye-tracking protocols in order to evaluate and compare these algorithms with respect to a number of qualitative characteristics.
Journal ArticleDOI

The brainstem control of saccadic eye movements

TL;DR: Research carried out in the intervening years has made it possible to provide a detailed description of the saccadic command signals that are generated by motor neurons and the formation of these signals in premotor brainstem regions.