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Record W4313063576 · doi:10.1109/tmech.2022.3214419

Design and Evaluation of a Load Control System for Biomechanical Energy Harvesters and Energy-Removing Exoskeletons

2022· article· en· W4313063576 on OpenAlexafffund
Michael Shepertycky, Yan‐Fei Liu, Qingguo Li

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2022
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExoskeletonMoment (physics)Mechanical energySTRIDETorqueSimulationElectricityControl systemSwingPower (physics)EngineeringEnergy (signal processing)Computer scienceAutomotive engineeringMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Controlling the timing and magnitude of electricity production is a critical factor in reducing the metabolic cost of walking with an energy-removing exoskeleton. This article outlines a novel power electronic control system designed to apply a mechanical loading profile onto the user that extracts kinetic energy. This energy extraction assists the user's muscles, thereby providing metabolic assistance while simultaneously producing electrical power. This open-loop control system estimates the state of both the exoskeleton and the user's lower limbs and uses this estimation to identify and apply a desired knee flexion moment during the terminal swing phase. The control system was evaluated using human treadmill walking experiments and benchtop testing, which determined that the system could identify the user's stride period, ground contact timing, and the device's moment arm with high accuracy and precision. Furthermore, the system could apply the desired cable force within +2.6 and −2.3 N and the muscle-centric knee moment profile within +0.05 and −0.04 N·m. Through proper load control, a user would benefit from walking with an energy-removing exoskeleton, regardless of the need for portable power.

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.222
Teacher spread0.207 · 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
GenreMethods

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

Citations9
Published2022
Admission routes2
Has abstractyes

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Same venueIEEE/ASME Transactions on MechatronicsSame topicProsthetics and Rehabilitation RoboticsFrench-language works237,207