Nonlinear Impedance Matching Approach (NIMA) for Robust Haptic Rendering During Robotic Laparoscopy Surgery
Bibliographic record
Abstract
The long overdue clinical need for haptic-enabled surgical robotic systems has been poorly addressed in the past two decades. Haptic systems have shown high agility and precision, which can translate into less risk to patients. However, regulatory requirements, such as validating haptic systems as “human-in-the-loop” components have impeded their commercialization. The complexity of regulatory validation arises from the “human-in-the-loop” design of the system that complicates the validation of inter-subject dynamics variations. In this study, we have implemented an Impedance Matching Approach (IMA) for force feedback estimation and rendering that does not include the “human” in the loop as a component. In other words, the amount of force feedback rendered at the haptic device does not essentially rely on the human to adequately “resist” the motion of the haptic device. This feature makes our proposed method intrinsically safe against the haptic “kick” which commonly happens if the surgeon releases the handle of the haptic interface while rendering non-zero forces. We have previously shown the accuracy and stability of the linear IMA method for single-force components [1]. In addition, we have implemented a general Non-linear IMA (NIMA) framework that accounts for the impedance parameters when the instrument-tissue interaction is not linear. We now present successful test results on the accuracy of the NIMA method with 3D contact forces and motion commands in a robot-assisted laparoscopy environment.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".