A feasibility study of eye gaze with biofeedback in a human-robot interface
Bibliographic record
Abstract
Play is a vital activity in which children learn skills and explore the environment through object manipulation. Assistive robots have been used to provide access to play, and Forbidden Region Virtual Fixture (FRVF) guidance at the user interface could help the users make the robot traverse the play environment more efficiently because it behaves like virtual walls to follow. Eye gaze was used to indicate the user's intended target and generate the location of the virtual walls in a card sorting task. We eliminated the typical computer screen required for visual feedback to confirm gaze location, and examined the use of alternative feedback. In this feasibility study, first a group of adults without physical impairment tested the system with auditory and vibrotactile feedback modalities for the gaze fixation and with the virtual walls on and off for robot movement. Then case studies with children and individuals with physical impairments were performed. Even though gaze fixation feedback and the virtual wall did not improve the performance of adult participants without impairment, the feedback increased the speed and accuracy of the gaze fixation and the virtual walls improved the movement efficiency for the participants with impairment and a 6-year-old child without impairment.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".