Using eye-tracking technology for communication in Rett syndrome: perceptions of impact
Bibliographic record
Abstract
Studies have investigated the use of eye-tracking technology to assess cognition in individuals with Rett syndrome, but few have looked at this access method for communication for this group. Loss of speech, decreased hand use, and severe motor apraxia significantly impact functional communication for this population. Eye gaze is one modality that may be used successfully by individuals with Rett syndrome. This multiple case study explored whether using eye-tracking technology, with ongoing support from a team of augmentative and alternative communication (AAC) therapists, could help four participants with Rett syndrome meet individualized communication goals. Two secondary objectives were to examine parents' perspectives on (a) the psychosocial impact of their child's use of the technology, and (b) satisfaction with using the technology. All four participants were rated by the treating therapists to have made improvement on their goals. According to both quantitative findings and descriptive information, eye-tracking technology was viewed by parents as contributing to participants' improved psychosocial functioning. Parents reported being highly satisfied with both the device and the clinical services received. This study provides initial evidence that eye-tracking may be perceived as a worthwhile and potentially satisfactory technology to support individuals with Rett syndrome in communicating. Future, more rigorous research that addresses the limitations of a case study design is required to substantiate study findings.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".