Assessment of accuracy for target detection in 3D-space using eye tracking and computer vision
Bibliographic record
Abstract
Over the last decade, eye tracking systems have been developed and used in many fields, mostly to identify targets on a screen, i.e. a plane. For novel applications such as the control of robotic devices by the user vision, there is a great interest in developing methods base on eye tracking to identify target points in free three dimensional environments. The objective of this paper is to characterise the accuracy the eye tracking and computer vision combination that was designed recently to overcome many limitations of eye tracking in 3D space. We propose a characterization protocol to assess the behavior of the accuracy of the system over the workspace of a robotic manipulator assistant. Applying this protocol to 33 subjects, we estimated the behavior of the error of the system relatively to the target position on a cylindrical workspace and to the acquisition time. Over our workspace, targets are located on average at 0.84 m and our method shows an accuracy 12.65 times better than the calculation of the 3D point of gaze. With the current accuracy, many potential applications become possible, such as visually controlled robotic assistants in the field of rehabilitation and adaptation engineering.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".