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Record W2102872304 · doi:10.1109/haptics.2006.150

A Study on Haptic Rendering in a Simulated Surgical Training Environment

2006· article· en· W2102872304 on OpenAlexaff
Peter Marshall, Shahram Payandeh, John C. Dill

Bibliographic record

VenueSymposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems · 2006
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsHaptic technologyCollision detectionPolygon meshComputer scienceSimulationRendering (computer graphics)ReactionComputer graphics (images)CollisionMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

We present a practical haptic model for a suturing simulator operating on spring-mass surface meshes. The model we use for suturing is a geometry-based model which includes the capability to form a knot. A method of extracting a tension value for a geometric suture is shown. Each reaction force that contributes to the haptic rendering of the tasks is analyzed, including contact forces, cutting forces, piercing, tissue resistance, collision forces between tools, and the cooperative forces inherent when the needle is passed from one hand to the other. The results are shown in a series of haptic force feedback plots over time..

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: Simulation or modeling · Consensus signal: Simulation or modeling
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)0.0010.001
Meta-epidemiology (broad)0.0010.000
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 head0.020
GPT teacher head0.220
Teacher spread0.201 · 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 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

Citations12
Published2006
Admission routes1
Has abstractyes

Explore more

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