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Record W2122818579 · doi:10.1109/tnsre.2009.2023292

Evaluation of a Prosthetic Swing-Phase Controller With Electrical Power Generation

2009· article· en· W2122818579 on OpenAlexaff
Jan Andrysek, Tony Liang, Bryan Steinnagel

Bibliographic record

VenueIEEE Transactions on Neural Systems and Rehabilitation Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsHolland Bloorview Kids Rehabilitation Hospital
Fundersnot available
KeywordsGaitSwingCadenceSimulationKinematicsComputer scienceMicroprocessorPower (physics)EngineeringPhysical medicine and rehabilitationMechanical engineeringEmbedded systemMedicineElectronic engineering

Abstract

fetched live from OpenAlex

With the increased presence of microprocessor-based prostheses in the market place, the availability of a self-energizing system has practical applicability. At present, most commercially available systems require the user to routinely recharge on-board batteries, which reduces the utility of these prostheses. To address this limitation, we have proposed a unique system based on an electromechanical generator to not only continually recharge batteries that are on-board the prostheses, but to also serve as a real time swing-phase damper. A prototype system was developed and evaluated with three active individuals with above-knee amputations across four damping conditions and two gait speeds. Gait and power generation performance were assessed via selected temporal, kinematic and kinetic parameters. Gait parameters including cadence and knee angle symmetry were found to be acceptable when knee damping was adapted for each participant. Across the three subjects and two walking speeds, between 0.57 and 1.57 W of electrical power was produced. These results indicate that this technology may be utilized for prosthetic swing-phase control and ultimately may alleviate the need for manually charging of microprocessor-based prostheses.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.586

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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 designSimulation or modeling
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

Citations15
Published2009
Admission routes1
Has abstractyes

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