Design of a Tissue Resonator Indenter Device for Measurement of Soft Tissue Viscoelastic Properties Using Parametric Identification
Bibliographic record
Abstract
The design of a new device called Tissue Resonator Indenter Device (TRID) for measuring soft tissue viscoelastic properties is presented. The two degrees-of-freedom device works based on mechanical vibration principles. When TRID comes into contact with a soft tissue, it can identify the tissue’s viscoelastic properties through the change of the device’s natural frequencies and damping ratios. In this paper, the deign of TRID is presented assuming Kelvin model for tissues. By working in the linear viscoelastic domain, TRID is designed to identify tissue properties in the range of 0–100 Hz. Assuming Kelvin model for tissues, the current paper develops a method for determining unknown tissue parameters using input-output data from TRID. Moreover, it is proved that the TRID’s parameters as well as the Kelvin tissue model parameters are globally identifiable. A parametric identification method using the prediction error approach is proposed for identifying the unknown tissue parameters in a grey-box state-space model. The reliability and effectiveness of the method for measuring soft tissue’s viscoelastic properties is demonstrated through simulation in the presence of considerable input and output noises.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".