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Record W4385881282 · doi:10.33696/rehabilitation.5.034

Kinematic and Kinetic Assessment of a Novel Polycentric Knee Joint for Powered Walking Assist Exoskeletons

2023· article· en· W4385881282 on OpenAlexafffund
Émélie Séguin, Marc Doumit

Bibliographic record

VenueJournal of Physical Medicine and Rehabilitation · 2023
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Ottawa
KeywordsExoskeletonKinematicsWearable computerComputer scienceOffset (computer science)SimulationPhysical medicine and rehabilitationEngineeringPhysicsMedicine

Abstract

fetched live from OpenAlex

Loss of mobility and independence directly affects the quality of life of many vulnerable individuals. In order to address this, researchers have been developing wearable walking assist exoskeletons to aid users with their daily activities. While this technology has advanced tremendously in the past decade, current exoskeletons are yet to be ergonomic, causing discomfort and injuries to the user, leading to early device rejection. This research intends to assess the kinematic and kinetic compatibility of a novel knee joint suitable for exoskeletons. The proposed knee design can be adapted to accommodate a kinematic offset and optimize force delivery. This is achieved by ensuring that the mechanical and biological joint rotation axes are aligned and that the moment arm can be varied to mimic the mechanical characteristics of a biological knee. Model simulations and mechanical testing of fabricated prototypes were achieved to analyze and validate the design. Results confirmed the compatibility of the design, which demonstrated kinematic absolute error values of 1.68 and 0.32 mm for the offset and aligned joints, respectively. It was shown that the moment arm can be varied throughout flexion, allowing future iterations to achieve optimal and effective moment transfer.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.018
GPT teacher head0.301
Teacher spread0.283 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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