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Record W1986728338 · doi:10.1109/embc.2012.6346092

Compensation for relative velocity between needle and soft tissue for friction modeling in needle insertion

2012· article· en· W1986728338 on OpenAlexaff
Ali Asadian, Rajni V. Patel, Mehrdad R. Kermani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsSoft tissueCompensation (psychology)Observer (physics)Deformation (meteorology)Relative motionComputer scienceMaterials scienceBiomedical engineeringMechanicsAcousticsPhysicsEngineeringSurgeryComposite materialMedicine

Abstract

fetched live from OpenAlex

Percutaneous therapies and in particular needle insertion treatments require an accurate model of needle-tissue interaction. Friction is a major interaction force component during needle insertion, and force-velocity mapping is an indicator of the interaction characteristics. However, soft tissue deformation is an inevitable and complex phenomenon that should be taken into account in order to correct the friction-velocity cycle. The main purpose of this study is to provide a means of compensating for the velocity of tissue with respect to a moving needle in the insertion direction. A high-gain observer is presented which can track this relative motion in a simple manner with no need for computer vision that may pose further challenges. To evaluate the performance of the velocity observer, experiments were carried out on gelatin and agar phantoms.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.642
Threshold uncertainty score0.283

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 head0.059
GPT teacher head0.283
Teacher spread0.224 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations6
Published2012
Admission routes1
Has abstractyes

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