Design of a Portable Device: Toward Assisting in Tongue-Strengthening Exercises and Dysphagia Management
Bibliographic record
Abstract
A Tongue-Machine Interaction System (TMIS) can be a valuable tool for tongue strengthening training which could contribute to rehabilitation of patients with dysphagia. Consequently, a TMIS would also help in mending the oropharyngeal pattern of swallowing. The adoption of TMIS’s in clinical practice has been limited in the past since many of them required patients to have a palatal plate or some other ‘component of interactivity’ mounted in the mouth and/or on the tongue. This paper discusses design and functionality related problems of tongue-computer interaction (TCI) devices and demonstrates incorporation of important TCI features in a TMIS. The design and implementation of a portable, low-cost, minimally invasive and, easy to learn TMIS having four major modules is presented. One of the system modules houses an array of infrared (IR) light emitting diodes. A connected module generates tongue position-based signals and enables tongue to either operate a mouse, use a virtual keyboard or press a button to operate appliances. A Wii remote IR camera and a computer serve as the other two modules. When tested on eight male and three female healthy participants, the TMIS achieved 42° horizontal and 33° vertical range of operation with the maximum signal range of 5 meters. Users could type up to 7.2 words per minute using the TMIS. Overall, the reported TMIS can support indirect exercise for tongue strengthening and treating swallowing difficulties.
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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 | 0.001 |
| 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) | 0.005 | 0.002 |
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".