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Record W2057854532 · doi:10.3109/13645700903516742

A catheter side wall tactile sensor: Design, modeling and experiments

2010· article· en· W2057854532 on OpenAlexaff
Huanran Wang, Peter Liu, Shuxiang Guo, Xiufen Ye

Bibliographic record

VenueMinimally Invasive Therapy & Allied Technologies · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsCarleton University
Fundersnot available
KeywordsPolyvinylidene fluorideHaptic technologyTactile sensorCatheterAcousticsPressure sensorPiezoelectricitySurgical instrumentBiomedical engineeringEngineeringSimulationComputer scienceSurgeryMechanical engineeringMaterials scienceMedicineArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Intravascular neurosurgery is a new and important technique of minimally invasive surgery. The current surgical device, however, does not provide realistic tactile feedback, which makes the operation a very difficult task, and the surgeon must exert extreme caution in order to avoid medical accidents. In this paper, a novel tactile sensor, which is based on polyvinylidene fluoride, is developed to measure the pressure on the side wall of the catheter for intravascular neurosurgery. The relationship between the input force and the output charge signals is identified based on the composite laminate theory, shell theory and linear piezoelectric theory. The design, mathematical model, interface circuit and calibrating experiment of the tactile sensor are presented in detail. With this sensor, surgeons will be able to "feel" the contact force between the side wall of the catheter and the blood vessel. Experimental results show that the tactile sensor measures the pressure well when it contacts the side wall of blood.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.243
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2010
Admission routes1
Has abstractyes

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