Miniaturized Optical Force Sensor for Minimally Invasive Surgery With Learning-Based Nonlinear Calibration
Why this work is in the frame
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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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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 it