Evaluating Accuracy of the Tobii Eye Tracker 5

Abstract

Eye-tracking sensors are a relatively new technology and currently has use as an accessibility method to allow those with disabilities to use technology with greater independence. This study evaluates the general accuracy and precision of Tobii eye-tracking software and hardware, along with the efficacy of training a neural network to improve both aspects of the eye-tracker itself. With three human testers observing a grid of data points, the measured and known point locations are recorded and analyzed using over 250 data points. The study was conducted over two days, with each participant performing four trials each. In this study, we use basic statistics and a k-means clustering algorithm to examine the data in depth and give insights into the performance of the Tobii-5 eye-tracker. In addition to evaluating performance, this study also attempts to improve the accuracy of the Tobii-5 eye-tracker by using a Multi-Layer Perceptron Regressor to reassign gaze locations to better line up with the expected gaze location. Potential future developments are also discussed.