Epidermal electronics for electromyography: An application to swallowing therapy
Bibliographic record
Abstract
Head and neck cancer treatment alters the anatomy and physiology of patients. Resulting swallowing difficulties can lead to serious health concerns. Surface electromyography (sEMG) is used as an adjuvant to swallowing therapy exercises. sEMG signal collected from the area under the chin provides visual biofeedback from muscle contractions and is used to help patients perform exercises correctly. However, conventional sEMG adhesive pads are relatively thick and difficult to effectively adhere to a patient's altered chin anatomy, potentially leading to poor signal acquisition in this population. Here, the emerging technology of epidermal electronics is introduced, where ultra-thin geometry allows for close contouring of the chin. The two objectives of this study were to (1) assess the potential of epidermal electronics technology for use with swallowing therapy and (2) assess the significance of the reference electrode placement. This study showed comparative signals between the new epidermal sEMG patch and the conventional adhesive patches used by clinicians. Furthermore, an integrated reference yielded optimal signal for clinical use; this configuration was more robust to head movements than when an external reference was used. Improvements for future iterations of epidermal sEMG patches specific to day-to-day clinical use are suggested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".