Body Functions and Structures Pertinent to Infrared Thermography-Based Access for Clients With Severe Motor Disabilities
Bibliographic record
Abstract
Infrared thermography has been recently proposed as an access technology for individuals with disabilities, but body functions and structures pertinent to its use have not been documented. Seven clients (2 adults, 5 youth) with severe disabilities and their primary caregivers participated in this study. All clients had a Gross Motor Functional Classification System (GMFCS) level of 5, but each possessed a unique set of extant physical movements. We tested the clients' ability to activate the infrared thermal access technology via a cued mouth open-close exercise. In addition, the clients or their primary caregivers were interviewed for descriptive information about the clients' physical, cognitive, and sensory function; communication skills; medical background; and history of switch use. Several impairments were identified as contraindications to infrared thermal access, spanning physiological (e.g., frequent fluctuations in body temperature, seizures, pain), motor (e.g., poor trunk control, involuntary movements, atypical mouth posture), and sensory/cognitive (e.g., inconsistent contingency awareness) subdomains. We identified key impairments in body functions and structures that limit infrared thermography-based access. Potential changes to the access technology (e.g., software and hardware) and physical environment to overcome those limitations are suggested.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".