Abstract
Eye-tracking technology is advancing rapidly, becoming cheaper and easier to use and more robust. This has fueled an increase in its implementation for Augmentative and Alternative Communication (AAC). Nowadays, Eye-Tracking Communication Devices (ETCDs) can be an effective aid for people with disabilities and communication problems. However, it is not clear what level of performance is attainable with these devices or how to optimize them for AAC use. The objective of this observational study was to provide data on non-disabled adults' performance with ETCD regarding (a) range of eye-typing ability in terms of speed and errors for different age groups and (b) relationship between ETCD performance and bimanual writing with a conventional PC keyboard and (c) to suggest a method for a correct implementation of ETCD for AAC. Sixty-seven healthy adult volunteers (aged 20–79 years) were asked to type a sample sentence using, first, a commercial ETCD and then a standard PC keyboard; we recorded the typing speed and error rate. We repeated the test 11 times in order to assess performance changes due to learning. Performances differed between young (20–39 years), middle-aged (40–59 years), and elderly (60–79 years) participants. Age had a negative impact on performance: as age increased, typing speed decreased and the error rate increased. There was a clear learning effect, i.e., repetition of the exercise produced an improvement of performance in all subjects. Bimanual and ETCD typing speed showed a linear relationship, with a Pearson's correlation coefficient of 0.73. The assessment of the effect of age on eye-typing performance can be useful to evaluate the effectiveness of man-machine interaction for use of ETCDs for AAC. Based on our findings, we outline a potential method (obviously requiring further verification) for the setup and tuning of ETCDs for AAC in people with disabilities and communication problems.
Highlights
Eye-tracking technologies, i.e., technologies that automatically track the point of an individual’s gaze while that person views or interacts with a visual image, are widely used in several research fields [1]
Participants were seated on a chair in front of the Eye-Tracking Communication Devices (ETCDs), which was mounted on a movable arm. e LCD screen was placed at 52 cm (∼2 cm) from the user’s eyes and positioned so that the midline connecting both eyes was at the same height as the screen center
Concerning error rate performance, the results show that people with the lowest error rate were those who obtained the highest typing speed
Summary
Eye-tracking technologies, i.e., technologies that automatically track the point of an individual’s gaze while that person views or interacts with a visual image, are widely used in several research fields [1]. Since 1994, the field of language science has seen an exponential rise in the publication of eye-tracking research studies. With this technology, the position of the eyes is determined based on light reflected from the curvature of the corneas and the position of the pupils. A signal processing procedure matches the physical orientation of the eyes to specific x-y coordinates and maps them, through appropriate calibration, to the Computational Intelligence and Neuroscience monitor’s coordinate system. In this way, the point of gaze is sampled and converted into x-y coordinates
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