Design of a portable device: Toward assisting in tongue-strengthening exercises and dysphagia management
Bibliographic record
Abstract
A Tongue-Machine Interaction System (TMIS) can assist in tongue-motor training and would consequently help in mending the oropharyngeal pattern of swallowing.Requiring movement of tongue musculature, TMIS's would increase salvia production in the mouth to lubricate mastication and help dysphagia patients in swallowing and digesting food.Using a TMIS for interacting with computers, a variety of communication devices and mobility support systems, would be tantamount to performing indirect tongue muscle strengthening exercises.Such exercises can help cancer and stroke patients who suffer from dysphagia, and people who receive injuries above C7 on spinal vertebrae.Such facilitation may also improve the swallowing physiology in applying force and maintaining speed.TMIS's features would allow using them for supervised and structured tongue muscle exercises as well.The real-life adoption of TMIS's is impeded by the fact that many of them require undergoing a procedure for mounting a palatal plate or some component of interactivity in mouth and/or on the tongue.We report the design and implementation of a portable, lowcost, minimally invasive and, easy to learn and use TMIS for direct and indirect non-swallowing training of tongue muscles.We highlight selection and incorporation of design features important to the target patient demography.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.008 | 0.003 |
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".