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Record W2974408967 · doi:10.1109/coase.2019.8843275

Semi-Autonomous Surgical Robot Control for Beating-Heart Surgery

2019· article· en· W2974408967 on OpenAlexaff
Lingbo Cheng, Jason Fong, Mahdi Tavakoli

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHeartbeatRobotSurgical robotRobotic surgeryComputer scienceImpedance controlSimulationSurgical instrumentArtificial intelligenceSurgeryMedicine

Abstract

fetched live from OpenAlex

In this paper, a semi-autonomous robot control system is developed for 3D robotic tracking of the complex physiological organ motion introduced by respiration and heartbeat in cardiac surgery. The same control system enables the surgeon's hand to perceive the non-oscillatory portion of the surgical robot-heart tissue interaction force. The semi-autonomous surgical system includes a slave surgical robot which can compensate for the physiological organ motion automatically and a master robot (user interface) which is manipulated by the surgeon to provide task commands to the surgical robot. The proposed impedance control method for the surgical robot only needs the frequency range of the physiological motion to synchronize the surgical instrument with the organ motion automatically. Another reference impedance model for the master robot is designed to provide non-oscillatory force feedback to the surgeon. A usability study emulating the motion requirements of tissue ablation is carried out. Experimental results are presented to show the effectiveness of the proposed method by comparing the results to the manual compensation method.

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: Empirical
Teacher disagreement score0.456
Threshold uncertainty score0.339

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 head0.010
GPT teacher head0.218
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.

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

Citations4
Published2019
Admission routes1
Has abstractyes

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