Design, Fabrication and Testing of a Piezoresistive-Based Tactile Sensor for Minimally Invasive Surgery
Bibliographic record
Abstract
Minimally invasive robotic surgery (MIRS) has become a preferred method for patients for the last two decades, thanks to its crucial advantages over classical open surgeries. Although MIRS has some advantages, it has a few drawbacks. Since MIRS technology includes performing surgery through small incisions using long slender tools, one of the main drawbacks of MIRS becomes the loss of direct contact with the patient’s body in the site of operation. Therefore, the surgeon loses the sense of touch during the operation which is one of the important tools to investigate the health condition of the treated tissue. This paper presents a novel piezoresistive-based multifunctional tactile sensor that is able to measure the contact force, relative hardness of the contact object, and the position of a hidden lump at the same time. A prototype of the designed sensor has been fabricated, simulated, analyzed, and tested numerically and experimentally. The experiments have been performed on hyperelastic materials, which are silicone rubber samples with different hardness values that resemble different biological tissues. The ability of the sensor to measure the contact force and relative hardness of the contact objects is tested with several experiments. A finite element (FE) model has been built in COMSOL Multiphysics (v3.4) environment to simulate both the mechanical behavior of the silicone rubber samples, and the interaction between the sensor and the silicone rubbers. Both numerical and experimental analysis proved the capability of the sensor to distinguish between different silicone rubber samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".