Evaluation of a non-invasive vocal cord vibration switch as an alternative access pathway for an individual with hypotonic cerebral palsy – a case study
Bibliographic record
Abstract
PURPOSE: A novel non-invasive vocal cord vibration switch for computer access was designed for an individual with hypotonic cerebral palsy. An evaluative case study was performed to compare the new device to an existing commercially available voice-activated switch in terms of sensitivity, specificity, speed, and user fatigue. METHOD: The participant wrote pangram sentences with the two switches over 4 days with two sessions each day (morning and afternoon). The order of the switches was alternated and a new sentence was used each day. The user's perceived level of exertion was noted before and after each task and activation errors were logged for performance analysis. After using the device for 2 months, a qualitative survey was administered with the participant and his educational assistant. RESULTS: The vocal cord vibration switch outperformed the voice-activated switch in terms of sensitivity (p < 10(-4), t-test), speed (p < 10(-3)), and user-perceived exertion (p < 10(-4)). Qualitatively, both the participant and his educational assistant were more satisfied with the proposed switch relative to his existing solution. CONCLUSIONS: The results of this study show that the vocal cord vibration switch provides a promising new alternative for individuals with severe and multiple disabilities who are able to hum or produce vocalizations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".