Design and Construction of a Double-Layer PVDF Wearable Ultrasonic Sensor for the Quantitative Assessment of Muscle Contractile Properties
Bibliographic record
Abstract
Assessment of the skeletal muscle contractile properties provides valuable information for various medical applications.This thesis presents the development of a wearable ultrasonic sensor (WUS) and a method to measure the skeletal muscle contractile parameters.The proposed WUS was made of flexible polyvinylidene fluoride (PVDF) piezoelectric polymer film.A double-layer PVDF configuration was proposed to improve ultrasonic performance such as ultrasound signal strength.In order to study the double-layer PVDF WUS performance for its design consideration, a formulation of a numerical simulation model for the double-layer PVDF WUS was derived, based on Mason's equivalent circuit model of piezoelectric resonators.The double-layer PVDF configuration and the effects of non-piezoelectric layers on ultrasonic performance, such as backing, bonding, and electrode layers, were studied in detail using the simulation model developed to obtain a guideline for the design and construction of double-layer PVDF WUS.The construction procedure of the proposed design of the double-layer PVDF WUS was simple and relatively low-cost.The experimental evaluation showed the improved ultrasound performance of the developed double-layer PVDF WUS.The flexibility, lightweight, thinness, and small size of the double-layer PVDF WUS enable a steady attachment to the skin surface without affecting the underlying tissue motion in the area of interest.Such features could reduce the motion artifacts in the ultrasound measurement of tissue thickness.The developed double-layer PVDF WUS was tested for in vivo measurements of the skeletal muscle contractile parameters.Comparative measurements of the electrically-evoked static contractions of a skeletal muscle i were performed by the developed double-layer PVDF WUS and the laser displacement sensor (LDS).The double-layer PVDF WUS demonstrated less variability in the extracted contractile parameters than the LDS.In addition, it was verified that the double-layer PVDF WUS was less susceptible to the motion artifacts induced by the body/limb motion than the LDS.The contractile parameters were successfully extracted from the tissue thickness changes measured by the double-layer PVDF WUS during voluntary and tetanic contractions.Furthermore, the muscle tetanic progression level was quantitatively assessed using the fusion index (FI) parameter obtained.
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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 | 0.001 |
| Open science | 0.001 | 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".