Design and microfabrication of a hybrid piezoelectric‐capacitive tactile sensor
Bibliographic record
Abstract
Purpose To measure the force applied to the tissue, the traditional endoscopic graspers might be equipped with a kind of tactile force sensor. Design/methodology/approach This paper presents the design, analysis, microfabrication and testing of a piezoelectric and capacitive endoscopic tactile sensor with four teeth. This tactile sensor, which is tooth‐like for safe grasping, comprises a Polyvinylidene Fluoride, PVDF film for high sensitivity and is silicon‐based for micromachinability. Being a hybrid sensor, employing both capacitive and piezoelectric techniques, it is possible to measure both the static and dynamic loads. Another feature, to be considered in its design, is the ability to detect pulse. The proposed sensor can be integrated with the tip of any current commercial endoscopic grasper without changing its original design. It is shown that using an array of sensor units, the position of the applied load can still be determined. Findings The static response of the sensor is obtained by applying a static force on the tooth and measuring the change in capacitance between the bottom electrode of the PVDF film and the electrode deposited on the surface of the etched cavity. The dynamic response of the device is determined by applying a sinusoidal force on the tooth of the sensor and measuring the output voltage from the PVDF film. The experimental results are compared with both analytical and finite element results. The sensor exhibits high sensitivity and linearity. Originality/value Capaciyive and piezoelectic are used to obtain both dynamic,pulse, and static loads. The sensor micromachined so, it can be used in various endoscopic applications.
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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 | 0.001 |
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".