SAW Motion Artifact Correction Using Bone Echoes in Wearable Ultrasound Shear-Wave Elastometry
Bibliographic record
Abstract
Monitoring muscle mechanical properties is important for assessing neuromuscular function in health, rehabilitation, and human-machine interfacing. Wearable ultrasound shear-wave elastometry (wUS-SWEM) enables continuous monitoring of muscle mechanical properties during contraction or movements by measuring shear-wave velocity (SWV). However, the skin-mounted actuator not only generates shear waves (SWs) in the target muscle but also excites surface acoustic waves (SAWs) that cause motion of the ultrasound transducer assembly, biasing SWV measurements. In this paper, we introduce a bone echo correction (BEC) method that uses echoes from bone underlying the tissue of interest as a stationary reference to detect and remove SAW-induced motion artifacts (SAW-MAs). An in vivo experiment on relaxed human biceps showed that uncorrected SWV was 8.6 ± 1.69 m/s, whereas BEC reduced it to 5.1 ± 1.02 m/s, revealing a mean bias of 3.5 m/s due to SAW-MAs. These findings demonstrate the feasibility of BEC for SAW-MA removal, and its potential to improve quantitative monitoring of muscle mechanical properties using wUS-SWEM.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 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".