Design Synthesis of a Robotic Uniaxial Torque Device for Orthopedic Haptic Simulation
Bibliographic record
Abstract
Abstract Robotic devices are commonly used in surgical simulators to provide tactile, or haptic, feedback. They can provide customized feedback that can be rapidly modified with minimal hardware changes in comparison to nonrobotic systems. This work describes the design, development, and evaluation of one such tool: a novel uniaxial torque haptic device for a surgical training simulator. The objective of the work was to design a single connection haptic device that could augment an existing six degree-of-freedom haptic device to mimic a Concorde Clear vacuum curette. Design and evaluations focused on the tool's ability to deliver adequate torque, imitate a surgical tool, and be integrated into the haptic device. Twenty-nine surgeons tested the tool in the simulator and evaluated it via a questionnaire. The device was found to deliver the 800 N⋅mm of torque necessary to mimic an orthopedic procedure. Surgeons found it accurately imitated surgical tools physical appearance and maneuverability, scoring them 3.9 ± 1.0 and 3.3 ± 1.2, respectively, on a 1–5 Likert scale. By virtue of the functionality necessary for testing and evaluation, the device could be connected to the haptic device for mechanical and electrical engagement. This device is a step forward in the field of augmentable haptic devices for surgical simulation. By changing the number of robotically controlled degrees-of-freedom of a haptic device, existing devices can be tuned to meet the demands of a particular simulator, which has the potential to improve surgeon training standards.
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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 | 0.003 | 0.003 |
| 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) | 0.002 | 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 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".