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Record W2030939594 · doi:10.1179/016164102101200311

Neuroprostheses for grasping

2002· review· en· W2030939594 on OpenAlexaff
Miloš R. Popović, Dejan B. Popović, Thierry Keller

Bibliographic record

VenueNeurological Research · 2002
Typereview
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsNeuroprostheticsFunctional electrical stimulationPhysical medicine and rehabilitationComputer sciencePopulationNeuroscienceMedicinePsychologyStimulation

Abstract

fetched live from OpenAlex

In recent years a number of neuroprostheses have been developed and used to assist stroke and spinal cord injured subjects to restore or improve grasping function. These neuroprostheses clearly demonstrated that the targeted group of subjects can significantly benefit from this technology and that functional electrical stimulation (FES) is a viable method for restoring or improving grasping function. In this article the FES technology is briefly explained and some of the better known neuroprostheses for grasping are discussed. Furthermore, a typical population of subjects that can benefit from this technology is indicated as well as the methodology to select and train these subjects to apply the neuroprosthesis in daily living activities. This article also provides a brief summary of the achieved results with the existing neuroprostheses for grasping and discusses some of the challenges this technology is currently facing.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.004

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 head0.364
GPT teacher head0.431
Teacher spread0.067 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations159
Published2002
Admission routes1
Has abstractyes

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