A micromachined piezoelectric tactile sensor for use in endoscopic graspers
Bibliographic record
Abstract
Present day endoscopic graspers do not have any sensors built in them. Thus the surgeon cannot estimate the amount of pressure being applied to manipulate the tissue safely. The paper reports on the design, fabrication and results of a silicon micromachined piezoelectric tactile sensor which can be integrated on to the tip of the endoscopic grasper. The sensor has a rigid tooth-like surface similar to the present day endoscopic grasper. It consists of upper silicon, a perspex substrate and a patterned polyvinylidene fluoride(PVDF) film, which is sandwiched between the two layer. It is shown that the magnitude and the position of the applied force on the sensor can be found from the magnitude of the output signals from the PVDF sensing elements and the slope of the signal at the position of the application of the force respectively. The sensor exhibits high force sensitivity, large dynamic range and good linearity. The theoretical analysis of the sensor is made and compared with the experimental values. The advantages and limitations of the sensor are also reported. Since the sensor simulates the teeth-like surface of the existing endoscopic grasper, it is possible to integrate the device on to the grasper of the endoscopic surgical instrument.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".