Towards an analytic haptic model for force rendering of soft-tissue dissection
Bibliographic record
Abstract
Both surgical simulation and robot-assisted surgery require haptic models of tool-tissue interaction for force rendering. Most efforts of haptic modeling have focused on characterizing tool-tissue interaction of soft-tissue indentation, insertion and cutting. Less attention has been devoted to soft-tissue dissection however. For the dissection, haptic models remain elusive to meet two requirements as: to represent nonlinearity of soft-tissue responses and to comply with the time constraint of 1 ms for force rendering. Hence, this paper presents a modeling framework towards developing an analytic haptic model for force rendering of the dissection. Based on estimation theories, the framework devises an analytic model to approximate an empirical force-distance profile of the dissection. Applying the framework to 2 different empirical profiles as use cases, the derived models estimated about 72% and 91% of the empirical data, respectively. Algorithm implementation of these models in Matlab yielded a computational time of about 24 μs, much less than 1 ms. The outcomes indicate a potential of using the framework to develop an analytic haptic model for force rendering of soft-tissue dissection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".