Validating an infrared thermal switch as a novel access technology
Bibliographic record
Abstract
BACKGROUND: Recently, a novel single-switch access technology based on infrared thermography was proposed. The technology exploits the temperature differences between the inside and surrounding areas of the mouth as a switch trigger, thereby allowing voluntary switch activation upon mouth opening. However, for this technology to be clinically viable, it must be validated against a gold standard switch, such as a chin switch, that taps into the same voluntary motion. METHODS: In this study, we report an experiment designed to gauge the concurrent validity of the infrared thermal switch. Ten able-bodied adults participated in a series of 3 test sessions where they simultaneously used both an infrared thermal and conventional chin switch to perform multiple trials of a number identification task with visual, auditory and audiovisual stimuli. Participants also provided qualitative feedback about switch use. User performance with the two switches was quantified using an efficiency measure based on mutual information. RESULTS: User performance (p = 0.16) and response time (p = 0.25) with the infrared thermal switch were comparable to those of the gold standard. Users reported preference for the infrared thermal switch given its non-contact nature and robustness to changes in user posture. CONCLUSIONS: Thermal infrared access technology appears to be a valid single switch alternative for individuals with disabilities who retain voluntary mouth opening and closing.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".