A new tactile array sensor for viscoelastic tissues with time-dependent behavior
Bibliographic record
Abstract
Purpose – The purpose of this paper is to introduce a new tactile array sensor into the medical field to enhance current robotic minimally invasive surgery (RMIS) procedures that are still limited in scope and versatility. In this paper, a novel idea is proposed in which a tactile sensor array can measure rate of displacement in addition to force and displacement of any viscoelastic material during the course of a single touch. To verify this new array sensor, several experiments were conducted on a diversity of tissues from which it was concluded that this newly developed sensory offers definite and significant enhancements. Design/methodology/approach – The proposed array sensor is capable of extracting force, displacement and displacement rate in the course of a single touch on tissues. Several experiments have been conducted on different tissues and the array sensor to verify the concept and to verify the output of the sensor. Findings – It is shown that this new generation of sensors are required to distinguish the difference in hardness degrees of materials with viscoelastic behavior. Originality/value – In this paper, a new generation of tactile sensors is proposed that is capable of measuring indentation time in addition to force and displacement. This idea is completely unique and has not been submitted to any conference or journal.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".