Multi-Axis Force Sensing in Laparoscopic Surgery
Bibliographic record
Abstract
This letter presents a novel approach to multi-axis force-sensing in laparoscopic surgery. It requires no modification to the surgical instrument and is therefore adaptable to different surgical tools. The sensing approach relies on a novel cannula design and utilizes a very high-resolution transducer for deflection measurement at the proximal shaft of the surgical instrument. The proposed cannula has an inner tube and an outer tube; the inner tube is attached to the cannula's interface to the robot frame through a compliant leaf spring with adjustable stiffness. It allows bending of the instrument shaft due to the tip forces. The outer tube mechanically filters out the body forces so they do not affect the instrument's bending behavior. An optical transducer with integrated electronics was mounted onto the proximal shaft of a da Vinci EndoWrist. A mathematical model of the sensing system was developed. A setup was built for calibration and testing, and its hardware and software are discussed in detail. Model-based and data-driven calibration approaches were compared. Comprehensive testing was conducted to validate that the sensor can successfully measure the lateral forces and moments and the axial torque applied to the instrument's distal end within the desired resolution, accuracy, and range requirements.
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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".