Stiffness perception during pinching and dissection with teleoperated haptic forceps
Bibliographic record
Abstract
Robotic-assisted surgery requires an intuitive and effective human-machine interface. Providing haptic feedback for pinching and dissecting motions of bipolar forceps, a tool commonly used in neurosurgery, could potentially improve the surgeon's experience. Current haptic hand controllers have limited actuation and feedback capability, requiring surgeons to hold the handle differently compared to a conventional tool. This paper presents a new master design that provides 1-DOF force feedback by adding a Hall-effect sensor and a voice coil actuator directly onto a bipolar forceps. Twenty participants used this interface to perform a remote stiffness perception test that employed the method of constant stimuli. Ten participants pinched the samples, and the other ten dissected them. Each participant did two blocks of 35 trials with only visual feedback or with visual and haptic feedback in random order. Psychometric functions were created from the results to compare perceptual capabilities, metrics were calculated from the force and position data, and participant survey responses were analyzed. The results show that providing the force feedback made the task seem easier, increased the participant's confidence, and reduced the total tip distance traveled in the pinching task. The haptic feedback slightly improved stiffness perception in the pinching task but did not improve perception in the dissection task. These results support the utility of a force-feedback attachment to conventional forceps for pinching and motivate further investigation into the design for dissection.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".