Evaluation of haptic devices and end‐users: Novel performance metrics in<scp>tele‐robotic</scp>microsurgery
Bibliographic record
Abstract
Abstract Background Here, we present performance evaluation methodology that distinguishes the performance of a haptic device from end‐user skill level in a tele‐robotic system. Methods A pick‐&‐place experiment was designed and eight participants micromanipulated cotton strips, similar to maneuvers performed during microsurgery. Using three nonredundant haptic devices:neuroArmPLUSHD, a custom developed master manipulator, and two commercially available products, sigma.7 and HD2, several features including the speed, effort, consistency, hand/gimbal agility, and force characteristics were measured and recorded for each participant and device. Results The participants showed variable skill level. For consistency, hand/gimbal agility and force characteristics, they performed significantly better when usingneuroArmPLUSHDprototype. Based on the experimental data, performance metrics for both the device and the end‐users were established. Conclusions Theintegrated performance metricsallows independent evaluation of both the user and haptic device, thereby quantifying human‐machine interactions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".