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Record W4210685378 · doi:10.1177/20556683211061995

Exploring textile-based electrode materials for electromyography smart garments

2022· article· en· W4210685378 on OpenAlexafffund
Emily Lam, Milad Alizadeh-Meghrazi, Alessandra Schlums, Ladan Eskandarian, Amin Mahnam, Bastien Moineau, Miloš R. Popović

Bibliographic record

VenueJournal of Rehabilitation and Assistive Technologies Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity Health NetworkUniversity of WaterlooToronto Rehabilitation InstituteUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsElectrodeMaterials scienceElectromyographyBiomedical engineeringWearable computerIsometric exerciseComposite materialDurabilityComputer sciencePhysical medicine and rehabilitationMedicinePhysical therapyChemistry

Abstract

fetched live from OpenAlex

INTRODUCTION: In recent years, electromyography (EMG) has been increasingly studied for wearable applications. Conventional gel electrodes for electrophysiological recordings have limited use in everyday applications such as prosthetic control or muscular therapy at home. This study investigates the efficacy and feasibility of dry-contact electrode materials employed in smart textiles for EMG recordings. METHODS: Dry-contact electrode materials were selected and implemented on textile substrates. Using these electrodes, EMG was recorded from the forearm of able-bodied subjects. 25% and 50% isometric maximum voluntary contractions were captured. A comparative investigation was performed against gel electrodes, assessing the effect of material properties on signal fidelity and strength compared. RESULTS: < 0.001). The inclusion of ionic liquids in the material composition, and using raised or flat electrodes, did not demonstrate a significant effect in signal quality. CONCLUSIONS: For EMG dry-contact electrodes, comparing the performance against gel electrodes for the application with the selected material is important. Other factors recommended to be studied are electrodes' durability and long-term stability.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.623

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 head0.219
Teacher spread0.201 · 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

Citations33
Published2022
Admission routes2
Has abstractyes

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Same venueJournal of Rehabilitation and Assistive Technologies EngineeringSame topicAdvanced Sensor and Energy Harvesting MaterialsFrench-language works237,207