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Record W3096604775 · doi:10.1109/bia50171.2020.9244512

Accurate Estimation of Tip Force on Tendon-driven Catheters using Inverse Cosserat Rod Model

2020· article· en· W3096604775 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsConcordia University
FundersConcordia University
KeywordsSpline (mechanical)ComputationMathematicsInverseBézier curveApproximation errorInverse kinematicsKinematicsMathematical analysisAlgorithmComputer scienceApplied mathematicsPhysicsGeometryClassical mechanics

Abstract

fetched live from OpenAlex

Tip force estimation on tendon-driven catheters is of crucial clinical importance for catheter-based surgeries such as radio-frequency ablation (RFA). In this study, a force estimation framework based on Bezier spline shape approximation and inverse Cosserat rod model was proposed and validated. Initially, the necessary condition for a Bezier spline to approximate a constant bending radius catheter was derived. Afterward, the kinematics and balance equations of the catheter were derived based on the proposed shape approximation technique and solved to find the tip force. The proposed method was experimentally tested for accuracy and computation time. The results showed that the estimated forces were in fair agreement with reference with a mean-absolute error of $0.024\pm 0.020\mathrm{N}$ and a computation time of $0.007\pm 0.005\mathrm{s}$ per frame. The exhibited performance was comparable to other studies and was in compliance with the requirements of interventional surgery applications.

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.

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 score0.667
Threshold uncertainty score0.277

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 head0.051
GPT teacher head0.260
Teacher spread0.209 · 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

Quick stats

Citations22
Published2020
Admission routes2
Has abstractyes

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