Design and evaluation of a novel microphone-based mechanomyography sensor with cylindrical and conical acoustic chambers
Bibliographic record
Abstract
Mechanomyography has recently been proposed as a control modality for alternative access technologies for individuals with disabilities. However, MMG recordings are highly susceptible to contamination from limb movements. Pressure-based transducers are touted to be the most robust to external movement although there is some debate about their optimal chamber geometry, in terms of low frequency gain and spectral flatness. To investigate the question of preferred geometry, transducers with cylindrical and conical chambers of varying dimensions were designed, manufactured and tested. Using a computer-controlled electrodynamic shaker, the frequency response of each chamber geometry was empirically derived. Of the cylindrical chambers, the highest gain and the flattest frequency response was exhibited by a chamber 10 mm in diameter and 5-7 mm in height. However, conical chambers offered an average rise in gain of 6.79 ± 1.06 dB/Hz over that achievable with cylindrical geometries. The highest gain and flattest response was achieved with a transducer consisting of a low-frequency MEMS microphone, a 4 μm aluminized mylar membrane and a rigid conical chamber 7 mm in diameter and 5mm in height. This design is recommended for MMG applications where limb movement is prevalent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".