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Record W2288921814 · doi:10.1109/tmech.2015.2429140

Design and Performance Evaluation of a Prototype MRF-based Haptic Interface for Medical Applications

2015· article· en· W2288921814 on OpenAlexafffund
Nima Najmaei, Ali Asadian, Mehrdad R. Kermani, Rajni V. Patel

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2015
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Council on Graduate Studies, Council of Ontario Universities
KeywordsHaptic technologyTransparency (behavior)TeleoperationActuatorComputer scienceInterface (matter)SimulationImaging phantomRobotArtificial intelligence

Abstract

fetched live from OpenAlex

This paper describes the construction and stability and transparency evaluation of a prototype two degrees-of-freedom (DoF) haptic interface, which takes advantage of magneto-rheological fluid (MRF)-based clutches for actuation. These small-scale clutches were designed in our lab, and their evaluation were reported previously [1], [2]. MRF-based actuators exhibit superior characteristics, which can significantly contribute to transparency and stability of haptic devices. Based on these actuators, a distributed antagonistic configuration is used to develop the 2-DoF haptic interface. This device is incorporated in a master-slave teleoperation setup intended for medical percutaneous interventions and soft-tissue palpation. Preliminary studies on the stability and transparency of the haptic interface in this setup using phantom and ex vivo samples show the great potential of MRF-based actuators for integration in haptic devices that require reliable, safe, accurate, highly transparent, and stable force reflection.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.050
GPT teacher head0.290
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations60
Published2015
Admission routes2
Has abstractyes

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