Ultrasonic Signal Processing for Continuous Measurements of Tissue Displacement and Thickness During Muscle Contraction
Bibliographic record
Abstract
Tissue displacement and thickness are useful parameters for quantifying muscle function.These parameters can be obtained using ultrasound with high frame rate and reasonable spatial resolution.However, a conventional hand-held ultrasonic probe that is bulky, rigid, and heavy may not be suitable for continuous muscle monitoring during physical activities.This research aimed to measure tissue displacement and thickness variation during muscle contraction using a wearable ultrasonic sensor.However, the energy of the transmitted ultrasonic waves using the wearable ultrasonic sensor is lower than that of a conventional probe.In order to overcome this issue, selected signal processing techniques were applied and compared.It was found in the numerical simulation experiments that the frequency-domain techniques, in particular LQ-factorization, had better tracking accuracy than the time-domain techniques.In the in-vivo experiment, ultrasonic signals were acquired at a forearm during isometric contraction.The tissue boundary displacements and thickness changes were successfully obtained.Foremost, I would like to express my sincere
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".