Miniaturized Optical Force Sensor for Minimally Invasive Surgery With Learning-Based Nonlinear Calibration
Bibliographic record
Abstract
A simple and miniaturized optical tactile sensor for integrating with robotic and manual minimally invasive surgery graspers is proposed in this study. For better miniaturization, the sensing principle of constant-bending-radius light intensity modulation was replaced with a variable-bending-radius modulation principle, and the pertinent theoretical formulation was derived. Afterward, a finite element model of the sensor was optimized using response surface optimization technique. The optimized sensor design was 14.0 mm long, 1.8 mm wide and 4 mm high. Next, the sensor was prototyped using SLA 3D printing technique. Also, the sensor was calibrated using a rate-dependent learning-based support-vector-regression algorithm. Calibration was 96% linear with a goodness-of-fit of 93% and mean absolute error of 0.085±0.096 N. Furthermore, the sensor was tested under cyclic triangular compression with a 3 sec pause between loading and unloading as well as manual grasping. Mean absolute error of 0.12±0.08 N, the minimum force of 0.14 N, and repeatability of 0.07 N showed the acceptable performance of the proposed sensor for surgical applications. Moreover, the sensor showed the capability of working under combined dynamic and static loading conditions with low hysteresis, i.e., 0.057 N/cycle.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".