Optimizing Sensor Selection in Laparoscopic Simulators: Lessons Learned in a Robotic Platform
Bibliographic record
Abstract
Laparoscopic simulators provide a safe environment in which surgeons can practice and hone specific skills without risk to patients. However, providing effective performance feedback requires selecting the relevant metrics that most accurately reflect skill levels while remaining actionable for the trainee. This study investigates optimal sensor selection for laparoscopic simulators to enhance training assessment accuracy. Six common sensor types were tested across different combinations to evaluate their impact on recognizing surgical gestures, surgical tasks, and surgeon expertise levels using convolutional neural networks and multidimensional dynamic time-warping classifiers. The results show that linear velocity and gripper angle yield high classification accuracy across all metrics. For gesture recognition, velocity and gripper angle consistently appeared in the top-performing sensor combinations, demonstrating that these two parameters alone are highly indicative of a surgeon’s intent and skill. Surprisingly, adding positional data does not improve accuracy, challenging the traditional emphasis on positional metrics in training systems. With the right sensor selection, surgical simulators can achieve accurate and actionable feedback while reducing complexity and cost without sacrificing performance, which can help make simulators more accessible and effective for training purposes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".