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Record W2538046394 · doi:10.1109/iecon.2008.4758441

The design of an intelligent mechanical Active Prosthetic Knee

2008· article· en· W2538046394 on OpenAlexaff
Roozbeh Borjian, Jee‐Hae Lim, Mir Behrad Khamesee, William Melek

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTorqueFuzzy logicComputer scienceGait cycleIntelligent controlKnee JointFuzzy control systemGaitEngineeringControl engineeringSimulationArtificial intelligencePhysical medicine and rehabilitationMedicineKinematics

Abstract

fetched live from OpenAlex

With the cost of active prostheses continuing to rise, war amputees, specifically in developing war-torn countries are unable to partake in the advancement of intelligent limbs. Many of the commercial prosthetic knee joints available are either wholly passive or provide only partial active feedback and control. The purpose of the proposed Active Prosthetic Knee (APK) design is to investigate a new schema that allows the device to provide the full necessary torque at the knee joint. Utilizing gait trend detection and streaming it to an intelligent logic, allows autonomous movement that mimics human locomotion. This study involves the design methodology of both the mechanical aspects as well as the fuzzy-based control system. The logic behind the fuzzy system can replicate the human gait cycle by dividing the cycle into its own unique phases.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.160

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 head0.025
GPT teacher head0.223
Teacher spread0.197 · 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 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

Citations27
Published2008
Admission routes1
Has abstractyes

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