Development of Wearable Ultrasonic Sensors for Monitoring Muscle Contraction
Bibliographic record
Abstract
This thesis presents the development of a wearable ultrasonic sensor to monitor muscle contractions.A flexible and lightweight ultrasonic sensor was constructed using a polyvinylidene fluoride piezoelectric polymer film, sandwiched by electrodes on the top and the bottom, and sealed by protection/insulation layer.A numerical simulation model of the sensor, based on Mason's electric equivalent circuit model of piezoelectric resonators, was developed.The internal losses of piezoelectric polymers were considered in the mathematical representation of the numerical simulation model for accurate prediction of its ultrasonic performance.The performance and frequency characteristics of the developed sensors were investigated by numerical simulations and experiments.The results of the numerical simulations and experiments show that the developed sensor operates in dual frequencies due to the effect of the non-piezoelectric layers, specifically of the silicone adhesive layers.The ultrasonic signal strength of the sensor with respect to the sensor size was investigated.Experiments were conducted to demonstrate the capability of the developed wearable sensor for monitoring static and dynamic muscle contractions.The muscle thickness changes measurement has been used to monitor muscle activities during contractions.The flexibility and lightweight of the sensor allows the sensor to be attached to the body area of interest without restricting the underlying tissue movements, which is not feasible using a conventional handheld ultrasonic probe.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".