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A geometric method for computing ocular kinematics and classifying gaze events using monocular remote eye tracking in a robotic environment

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, January 2016
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (51st percentile)

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Title
A geometric method for computing ocular kinematics and classifying gaze events using monocular remote eye tracking in a robotic environment
Published in
Journal of NeuroEngineering and Rehabilitation, January 2016
DOI 10.1186/s12984-015-0107-4
Pubmed ID
Authors

Tarkeshwar Singh, Christopher M. Perry, Troy M. Herter

Abstract

Robotic and virtual-reality systems offer tremendous potential for improving assessment and rehabilitation of neurological disorders affecting the upper extremity. A key feature of these systems is that visual stimuli are often presented within the same workspace as the hands (i.e., peripersonal space). Integrating video-based remote eye tracking with robotic and virtual-reality systems can provide an additional tool for investigating how cognitive processes influence visuomotor learning and rehabilitation of the upper extremity. However, remote eye tracking systems typically compute ocular kinematics by assuming eye movements are made in a plane with constant depth (e.g. frontal plane). When visual stimuli are presented at variable depths (e.g. transverse plane), eye movements have a vergence component that may influence reliable detection of gaze events (fixations, smooth pursuits and saccades). To our knowledge, there are no available methods to classify gaze events in the transverse plane for monocular remote eye tracking systems. Here we present a geometrical method to compute ocular kinematics from a monocular remote eye tracking system when visual stimuli are presented in the transverse plane. We then use the obtained kinematics to compute velocity-based thresholds that allow us to accurately identify onsets and offsets of fixations, saccades and smooth pursuits. Finally, we validate our algorithm by comparing the gaze events computed by the algorithm with those obtained from the eye-tracking software and manual digitization. Within the transverse plane, our algorithm reliably differentiates saccades from fixations (static visual stimuli) and smooth pursuits from saccades and fixations when visual stimuli are dynamic. The proposed methods provide advancements for examining eye movements in robotic and virtual-reality systems. Our methods can also be used with other video-based or tablet-based systems in which eye movements are performed in a peripersonal plane with variable depth.

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X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Switzerland 1 <1%
Canada 1 <1%
Unknown 106 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 24 22%
Student > Ph. D. Student 19 17%
Researcher 16 15%
Student > Bachelor 10 9%
Student > Doctoral Student 5 5%
Other 14 13%
Unknown 21 19%
Readers by discipline Count As %
Neuroscience 13 12%
Psychology 12 11%
Engineering 11 10%
Computer Science 11 10%
Medicine and Dentistry 9 8%
Other 24 22%
Unknown 29 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 January 2016.
All research outputs
#13,220,363
of 22,842,950 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#618
of 1,279 outputs
Outputs of similar age
#186,152
of 396,750 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#13
of 27 outputs
Altmetric has tracked 22,842,950 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,279 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 396,750 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.