A Study of Applying Gaze-Tracking Control to Motorized Assistive Devices
Bibliographic record
Abstract
One of the most difficult barriers to alleviating the effects of degenerative diseases is the severe retrogression they cause not only in communication, but also in the ability to manipulate devices designed to restore agency.This project aims to reproduce the muscle control that people with ALS (PALS) have lost, using a lowcost gaze tracker as the input device for a motorized headrest. Eye movement is often the last remaining method of control in a number of progressive neurodegenerative diseases, and harnessing it as an input device allows a broad range of applications that can benefit users of this technology.The tracker and associated electronics are connected to a motorized headrest, the first of its kind, developed on campus at the University of British Columbia (UBC). This system uses the Mirametrix S1 eye-gaze tracking device to take a user’s commands and translate them into head movement, which allows for communication through predefined nods or shakes, the ability to self-direct an otherwise immobile individual’s head position, and comfortable selection of a resting head position.The development of our novel user interface demonstrates the utility of eye-gaze tracking as a functional and promising method to restore control to immobilized persons.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".