Abstract
This paper demonstrates the application of a proposed eye fixation detection algorithm to eye movement recorded during eye gaze input within immersive Virtual Reality and compares it with the standard frame-by-frame analysis for validation. Pearson correlations and a sample paired t-test indicated strong correlations between the two analysis methods in terms of fixation duration. The results showed that the principle of eye movement event detection in 2D can be applied successfully in a 3D environment and ensures efficient detection when combined with ray-casting and event time.
Highlights
Analysis of eye movement data recorded by eye trackers has been shown to be a valuable tool for psychological research and diagnosis
It is important to determine specific limits or thresholds before the detection of eye movement events. These thresholds of eye movement event detection have no standards or accurately defined values, but can be determined by trial and error, or by experience, or taken from literature in the same paradigm, which may lead to subjective results [1,2,3] whether the analysis is performed manually or algorithmically
This paper proposes a simple methodology to detect eye fixations and objects of interest (OOI) using head-mounted displays (HMDs)-ET in current Virtual reality (VR) environments
Summary
Analysis of eye movement data recorded by eye trackers has been shown to be a valuable tool for psychological research and diagnosis. During the eye-tracking experiment, the data sample represented as a stream of data exhibits specific behaviors which can be used to detect oculomotor events. It is important to determine specific limits or thresholds before the detection of eye movement events. These thresholds of eye movement event detection have no standards or accurately defined values, but can be determined by trial and error, or by experience, or taken from literature in the same paradigm, which may lead to subjective results [1,2,3] whether the analysis is performed manually or algorithmically. The algorithms used in eye movement research are based on general principles for detecting oculomotor events. A saccade is represented as spaced-out gaze points where no visual processing can occur because of the rapid jumps in the eye movement towards a target
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