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Record W2077458356 · doi:10.1108/01439910310457706

User‐adaptive control of a magnetorheological prosthetic knee

2003· article· en· W2077458356 on OpenAlexaff
Hugh Herr, Ari Wilkenfeld

Bibliographic record

VenueIndustrial Robot the international journal of robotics research and application · 2003
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsKinematicsGaitProsthesisMagnetorheological fluidComputer scienceKnee JointAdaptive controlPhysical medicine and rehabilitationKnee flexionGait analysisSimulationMedicineEngineeringArtificial intelligenceControl (management)SurgeryPhysicsStructural engineering

Abstract

fetched live from OpenAlex

A magnetorheological knee prosthesis is presented that automatically adapts knee damping to the gait of the amputee using only local sensing of knee force, torque, and position. To assess the clinical effects of the user‐adaptive knee prosthesis, kinematic gait data were collected on four unilateral trans‐femoral amputees. Using the user‐adaptive knee and a conventional, non‐adaptive knee, gait kinematics were evaluated on both affected and unaffected sides. Results were compared to the kinematics of 12 age, weight and height matched normals. We find that the user‐adaptive knee successfully controls early stance damping, enabling amputee to undergo biologically‐realistic, early stance knee flexion. These results indicate that a user‐adaptive control scheme and local mechanical sensing are all that is required for amputees to walk with an increased level of biological realism compared to mechanically passive prosthetic systems.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.059
GPT teacher head0.304
Teacher spread0.245 · 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

Citations289
Published2003
Admission routes1
Has abstractyes

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Same venueIndustrial Robot the international journal of robotics research and applicationSame topicMuscle activation and electromyography studiesFrench-language works237,207