A Method to Characterize Skeletal Muscle Contractile Properties Using a Wearable Ultrasonic Sensor
Bibliographic record
Abstract
Skeletal muscle monitoring provides valuable information about the muscle contractile properties which could be useful in various clinical applications.A wearable ultrasonic sensor (WUS) was constructed using a 110 µm thick polyvinylidene fluoride polymer piezoelectric film to perform a continuous hands-free muscle monitoring.The lightweight and flexible properties of WUS enables the stable attachment to the skin surface without affecting the tissue motion of interest, which results in reduced motion artifacts.In order to perform reliable estimation of tissue thicknesses by the WUS in an ultrasonic pulse echo mode, the accuracy of selected signal analysis techniques was evaluated using a laser displacement measurement result as a reference.Then, a mechanical simulation experiment is conducted to evaluate the accuracy and repeatability of the WUS measurement system.In addition, the effects of moving averaging on the signal-to-noise ratio of the ultrasonic signals and the estimation accuracy of the tissue thickness were investigated.In order to demonstrate the feasibility of the proposed ultrasound technique for in-vivo measurements, continuous monitoring of the lateral head of triceps muscle of three healthy male subjects were monitored using the WUS.The tissue thickness changes due to the evoked muscle contraction by an electromyostimulation (EMS) at various pulse repetition frequencies were monitored.vii
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".