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Record W2162377276 · doi:10.1080/03091900210142459

Intelligent multifunction myoelectric control of hand prostheses

2002· article· en· W2162377276 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Medical Engineering & Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsProsthetic handProcess (computing)Prehensile tailController (irrigation)Computer scienceSIGNAL (programming language)Computer visionHuman–computer interactionEngineeringComputer hardwareArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

Intuitive myoelectric prosthesis control is difficult to achieve due to the absence of proprioceptive feedback, which forces the user to monitor grip pressure by visual information. Existing myoelectric hand prostheses form a single degree of freedom pincer motion that inhibits the stable prehension of a range of objects. Multi-axis hands may address this lack of functionality, but as with multifunction devices in general, serve to increase the cognitive burden on the user. Intelligent hierarchical control of multiple degree-of-freedom hand prostheses has been used to reduce the need for visual feedback by automating the grasping process. This paper presents a hybrid controller that has been developed to enable different prehensile functions to be initiated directly from the user's myoelectric signal. A digital signal processor (DSP) regulates the grip pressure of a new six-degree-of-freedom hand prosthesis thereby ensuring secure prehension without continuous visual feedback.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.896
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.200
Teacher spread0.191 · 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