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Record W2079401618 · doi:10.1115/1.2735969

Modeling and Control Considerations for Powered Lower-Limb Orthoses: A Design Study for Assisted STS

2006· article· en· W2079401618 on OpenAlexaff
Wesley R. Eby, Eric Kubica

Bibliographic record

VenueJournal of Medical Devices · 2006
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTask (project management)Key (lock)Control (management)Computer scienceInterface (matter)ActuatorLower limbConceptual designHuman–computer interactionPhysical medicine and rehabilitationEngineeringSystems engineeringMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Lower-limb orthotic devices may be used to aid or restore mobility to the impaired user. Powered orthoses, in particular, hold great potential in improving the quality of life for individuals with locomotor difficulties because active control of an orthosis can aid limb movement in common tasks that may even be impossible if unaided. However, these devices have primarily remained the products of research labs with the number of effective commercial applications for the laity being nearly nonexistent. This paper provides an overview of the current status of powered orthoses and goes on to discuss key issues in modeling and control of powered orthoses so that designers can have a unified framework in developing user-oriented devices. Key concepts are demonstrated for a powered knee-orthosis intended for assisting the sit-to-stand task, and both pneumatic muscle and dc motor actuators are considered in this conceptual design study. In the final analysis, we conclude that the ability to provide sit-to-stand assistance is profoundly dependent on the type of control signal employed to control the actuator from the user–orthosis interface.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.782
Threshold uncertainty score0.268

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.029
GPT teacher head0.286
Teacher spread0.257 · 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

Citations8
Published2006
Admission routes1
Has abstractyes

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