Surveillance-Behavior Support by a Real-Time Gaze-Based Tool Integrated with Augmented Reality
Bibliographic record
Abstract
Video surveillance can be cognitively very demanding as it imposes operators to stay focus for a long time and to provide the right response for relevant stimuli among a lot of information. In this challenging activity, it is relevant to consider the use of decision-aid techniques to improve operators’ alertness. The purpose of this study was to examine the impact of a real-time gaze-based tool named Scantracker—which can identify instances of neglect, over-focus and vigilance decrement using eye tracking and display visual notifications to mitigate such situations—on surveillance performance measures during a surveillance simulation. Augmented reality glasses were used to monitor eye movements in real time for all non-expert participants, but notifications presentation to support attention was visually active for only half of them (Scantracker group), as opposed to the control group without support from the Scantracker. No significant differences were observed across those two groups. However, a within-group comparison contrasting trials with active notifications vs. a silent condition showed a reliable improvement in task accuracy and a reduction in screen neglect duration. Results are discussed in light of potential applications of Scantracker with augmented reality.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".