Impedance Matching Approach for Robust Force Feedback Rendering with Application in Robot-assisted Interventions
Bibliographic record
Abstract
Robust and accurate force feedback is a clinical need for enhancing the safety of remote robot-assisted interventional procedures. In this study, an impedance-based force feedback approach was proposed and validated. Initially a fast impedance identification method for estimating the impedance of catheter-vasculature interaction at the slave module of such systems was obtained. Afterward, a force feedback approach based on matching the mechanical impedance of the master module with the identified impedance was implemented. The proposed force control method was experimentally studied for a robot-assisted remote catheter insertion task. Also, the performance of the proposed method was experimentally compared to the conventional direct force reflection approach. The proposed force feedback method exhibited fair accuracy in force tracking (mean-absolute error of 0.046 ± 0.027 N). The proposed method outperformed the direct force reflection approach in the absence and presence of communication interruptions (0.052N vs. 0.061N) and (0.041N vs. 0.171N), respectively. The proposed force feedback method exhibited favorable robustness and accuracy for remote interventional applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".