Effect of feedback and target size on eye gaze accuracy in an off-screen task
Bibliographic record
Abstract
PURPOSE: Eye gaze interfaces have been used by people with severe physical impairment to interact with various assistive technologies. If used to control robots, it would be beneficial if individuals could gaze directly at targets in the physical environment rather than have to switch their gaze between a screen with representations of robot commands and the physical environment to see the response of their selection. By using a homogeneous transformation technique, eye gaze coordinates can be mapped between the reference coordinate frame of eye tracker and the coordinate frame of objects in the physical environment. Feedback about where the eye tracker has determined the eye gaze is fixated is needed so users can select targets more accurately. Screen-based assistive technologies can use visual feedback, but in a physical environment, other forms of feedback need to be examined. MATERIALS AND METHODS: In this study, an eye gaze system with different feedback conditions (i.e., visual, auditory, vibrotactile, and no-feedback) was tested when participants received visual feedback on a display (on-screen) and when looking directly at the physical environment (off-screen). Target selection tasks in both screen conditions were performed by ten non-disabled adults, three non-disabled children, and two adults and one child with cerebral palsy. RESULTS: Tasks performed with gaze fixation feedback modalities were accomplished faster and with higher success than tasks performed without feedback, and similar results were observed in both screen conditions. No significant difference was observed in performance across the feedback modalities, but participants had personal preferences. CONCLUSION: The homogeneous transformation technique enabled the use of a stationary eye tracker to select target objects in the physical environment, and auditory and vibrotactile feedback enabled participants to be more accurate selecting targets than without it.Implications for RehabilitationBeing able to select target objects in the physical environment by eye gaze could make it easier for children with disabilities to control assistive robots, because in this way they do not have to change their focus between a computer screen with commands and the robot.Providing auditory or vibrotactile feedback when using an eye gaze system made it faster and easier to know if a target was being gazed upon.Being able to select targets in the environment using eye gaze could be beneficial for other assistive technology, too, such as destination selection for power wheelchairs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".