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Record W1973768714 · doi:10.1586/17434440.2014.929496

Applications of sensory feedback in motorized upper extremity prosthesis: a review

2014· review· en· W1973768714 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

VenueExpert Review of Medical Devices · 2014
Typereview
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Alberta
FundersNational Institute of Neurological Disorders and Stroke
KeywordsGRASPSensory systemProsthesisComputer sciencePhysical medicine and rehabilitationEfferentSensory substitutionHuman–computer interactionHaptic technologyProsthetic handAfferentSimulationPsychologyNeuroscienceMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Dexterous hand movement is possible due to closed loop control dependent on efferent motor output and afferent sensory feedback. This control strategy is significantly altered in those with upper limb amputation as sensations of touch and movement are inherently lost. For upper limb prosthetic users, the absence of sensory feedback impedes efficient use of the prosthesis and is highlighted as a major factor contributing to user rejection of myoelectric prostheses. Numerous sensory feedback systems have been proposed in literature to address this gap in prosthetic control; however, these systems have yet to be implemented for long term use. Methodologies for communicating prosthetic grasp and touch information are reviewed, including discussion of selected designs and test results. With a focus on clinical and translational challenges, this review highlights and compares techniques employed to provide amputees with sensory feedback. Additionally, promising future directions are discussed and highlighted.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.588
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.022
GPT teacher head0.333
Teacher spread0.312 · 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