Modeling and optimal design of an optical MEMS tactile sensor for use in robotically assisted surgery
Bibliographic record
Abstract
Currently, Minimally Invasive Surgery (MIS) performs through keyhole incisions using commercially available robotic surgery systems. One of the most famous examples of these robotic surgery systems is the da Vinci surgical system. In the current robotic surgery systems like the da Vinci, surgeons are faced with problems such as lack of tactile feedback during the surgery. Therefore, providing a real-time tactile feedback from interaction between surgical instruments and tissue can help the surgeons to perform MIS more reliably. The present paper proposes an optical tactile sensor to measure the contact force between the bio-tissue and the surgical instrument. A model is proposed for simulating the interaction between a flexible membrane and bio-tissue based on the finite element methods. The tissue is considered as a hyperelastic material with the material properties similar to the heart tissue. The flexible membrane is assumed as a thin layer of silicon which can be microfabricated using the technology of Micro Electro Mechanical Systems (MEMS). The simulation results are used to optimize the geometric design parameters of a proposed MEMS tactile sensor for use in robotic surgical systems to perform MIS.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".