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Novel FMRI-Compatible wrist robotic device for brain activation assessment during rehabilitation exercise

2020· article· en· W3024416646 on OpenAlexaff
Hamid Sharini, Hamid Khabiri, Hossein Arabalibeik, Hassan Hashemi, Amirreza Azimi, Sadegh Masjoodi

Bibliographic record

VenueMedical Engineering & Physics · 2020
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsConcordia University
FundersTehran University of Medical Sciences and Health Services
KeywordsWristRobotFunctional magnetic resonance imagingComputer scienceSomatosensory systemMagnetic resonance imagingArtificial intelligenceSimulationMedicinePsychologyNeuroscienceSurgeryRadiology

Abstract

fetched live from OpenAlex

Magnetic Resonance Imaging (MRI) can be applied to study the effects of rehabilitation strategies for neuroscience research. An MRI-wrist robot is designed and used as a clinical tool to examine the process of the brain plasticity changes. In this robot, the patient actuation is accomplished with two standard air cylinders, located inside the MRI chamber with two degrees of freedom (flexion-extension and ulna-radial deviation) with pneumatic air transmission, consisting of simple mechanism converting rotary motion to linear independently. A pilot study of brain image aiming at revealing more effective therapeutic strategies carried out to confirm the technical aspects of the development and validation. In a healthy subject, both wrist movement of robot and subject demonstrated brain activity in the contralateral primary somatosensory cortex. Because the robot does not move during the patient's body, a stand was designed to allow the wrist robot and patient to fit comfortably within the MRI machine. While all the parts of the robot were carefully selected with strict MRI compatibility requirements, the robot was tested by presenting some pilot imaging data with null effects on the image quality, as well. Finally, the possible further development of the robot has been introduced for a rehabilitation assessment.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.672
Threshold uncertainty score0.551

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 head0.021
GPT teacher head0.317
Teacher spread0.296 · 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
GenreMethods

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
Published2020
Admission routes1
Has abstractyes

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