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Record W2041595942 · doi:10.1109/iembs.2010.5626171

Effect of force feedback from each DOF on the motion accuracy of a surgical tool in performing a robot-assisted tracing task

2010· article· en· W2041595942 on OpenAlexaff
Manar D. Samad, Yaoping Hu, Garnette R. Sutherland

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTracingController (irrigation)RobotComputer scienceRobot end effectorMotion (physics)SimulationTask (project management)Haptic technologyMotion controlComputer visionArtificial intelligenceControl theory (sociology)EngineeringControl (management)

Abstract

fetched live from OpenAlex

In robot-assisted surgery, it may be important to provide force feedback to the hand of the surgeon. Here we examine how force feedback from each degree of freedom (DOF) on a hand controller affects the motion accuracy of a surgical tool. We studied the motion accuracy of a needle-shaped tool in performing a robot-assisted tracing task. On a virtual simulation of the tool and neuroArm robot, human participants manipulated a hand controller to move the tool attached to the end-effector of the robot. They used the tool to trace a line on pipes (mimicking blood vessels) along 3 orthogonal directions, corresponding to 3 DOF on the hand controller. We observed that force feedback from each DOF on the hand controller had a significant effect on the motion accuracy of the tool during tracing. Varying force conditions yielded insignificant difference in motion accuracy. These results indicate a need of revising the hand controller for achieving improved motion accuracy in performing robot-assisted tasks.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.269

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 head0.008
GPT teacher head0.236
Teacher spread0.228 · 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 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

Citations5
Published2010
Admission routes1
Has abstractyes

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