Using machine learning based on eye gaze to predict targets: An exploratory study
Bibliographic record
Abstract
Play is a crucial activity for child development. Play in children with physical disabilities may be compromised due to their physical limitations, such as having difficulties reaching and manipulating objects. Assistive technology robotic systems have been used as tools for children with disabilities to play and interact with the environment. Robots have shown a positive impact on children's independence, cognitive, and social skills. The present study is the first stage of a project to develop a telerobotic haptic system, with the goal of supporting the reaching of toys during play by children with severe physical disabilities. The end goal is to provide haptic guidance towards the toys that the children want to play with. The objective of this paper was to investigate the feasibility of predicting the selection of targets in a three-block task. This prediction was based on the Point of Gaze (POG) data of five participants while performing the task using a telerobotic haptic system. Two fixation-based algorithms, longest fixation and last fixation, and two learning algorithms, a Double Q-learning and a Multi-Layer Perceptron neural network, were implemented, tested, and compared. Results showed that the learning algorithms were better at predicting the targets than the fixation-based algorithms, with above 92% accuracy. This demonstrated that the learning algorithms can be utilized for activating haptic guidance towards the targets (toys).
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".