Comparison of Attentive and Explicit Eye Gaze Interfaces for Controlling Haptic Guidance of a Robotic Controller
Bibliographic record
Abstract
Children with physical impairments may face challenges during play due to limitations in reaching and handling objects. Telerobotic systems that provide guidance towards toys may help provide access to play, but intuitive methods to control the guidance are required. As a first step towards this, adults without physical impairments tested two eye gaze interfaces. One was an attentive user interface that predicts the target toy that users want to reach using a neural network, trained to recognize the movements performed on the user-side robot and the user’s point of gaze. The other interface was an explicit eye input interface that detects the toy that a user fixates on for at least 500[Formula: see text]ms. This study compared the performance and advantages of each interface in a whack-a-mole game. The purpose was to test the feasibility of activating haptic guidance towards toys with an attentive interface and to assure the safety of the system before children use it. The prediction accuracy of the attentive interface was 86.4% on average, compared to 100% with the explicit interface, thus, seven participants preferred using the explicit interface over the attentive interface. However, using the attentive user interface was significantly faster, and it was less tiring on the eyes. Ways to improve the accuracy of the attentive eye gaze interface are suggested.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".