A novel integrated mechanomyogram-vocalization access solution
Bibliographic record
Abstract
We introduce a novel dual-switch control paradigm based on the simultaneous measurement of frontalis muscle mechanomyography (MMG) and vocalizations (humming) using a single contact microphone attached to the forehead. Vibrations of the face and skull during vocalization are manifested as periodic high-frequency components in the microphone signal recorded at the forehead. The presence of these periodic components is detected by a normalized cross-correlation function, while muscle contractions are detected using a continuous wavelet transform method. The dual-switch provides two independent binary control signals. Eleven participants, including one individual with severe physical disabilities, participated in a cued activation task in which the dual-switch exhibited sensitivities and specificities of 96.8±3% and 98.4±1%, respectively for vocalizations, and 99.7±0.5% and 99.2±0.5%, respectively for muscle contractions. Since skin vibrations due to voiced sounds and muscle contractions have non-overlapping dominant bandwidths, the performance of the MMG switch was not affected by vocalizations. This new integrated MMG-vocalization access solution affords the user two binary switches from a single access site, and may thus augment access alternatives for certain individuals with severe physical disabilities.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".