Design and Fabrication of Piezoelectric-based Tactile Sensor for Detecting Compliance
Bibliographic record
Abstract
Present day endoscopic graspers are designed to be tooth-like in order to grasp slippery tissues. They are not, however, able to measure tissue tactile properties such as force and softness. This paper reports on proof of a conceptual and innovative design of an endoscopic piezoelectric tactile sensor. The sensor is capable of measuring the total applied force on the sensed object, as well as the compliance of the tissue/sensed object. Detecting the sensed objects compliance is based on the relative deformation of contact object/tissue on the rigid and compliant elements. One polyvinylidene fluoride (PVDF) film is placed between the rigid cylinder and the plate which measures the force applied on the rigid element. Another PVDF film is sandwiched between the two base plates measuring the total force applied on the sensor. The compliance of the sensed object is measured by recording the PVDF films response under different load sets. The data obtained for the force variation are plotted against the modulus of elasticity of the sensed object. Finite element modeling was used for comparison with the experimental data. The results show that the experimental and the FE approach agree with each other with a reasonable band.
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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 | 0.000 |
| 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 | 0.001 | 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".