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Record W2968435559 · doi:10.1109/jsen.2019.2933751

Detection of Neuromuscular Activity Using New Non-Invasive and Flexible Multimaterial Fiber Dry-Electrodes

2019· article· en· W2968435559 on OpenAlexafffund
Mourad Roudjane, Simon Tam, Quentin Mascret, Cheikh Latyr Fall, Mathieu Bielmann, Ricardo Adriano Dorledo de Faria, Laurent J. Bouyer, Benoit Gosselin, Younès Messaddeq

Bibliographic record

VenueIEEE Sensors Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversité Laval
FundersCanada First Research Excellence FundNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationUniversité Laval
KeywordsSIGNAL (programming language)ElectrodeFlexibility (engineering)Materials scienceNoise (video)Biomedical engineeringFiberAcousticsComputer scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

A new non-invasive and flexible sensor was fabricated to monitor the muscular activity of upper body muscles. It consists of a dry electrode made of multimaterial metalpolymer-glass hollow-core fiber electrodes connected to a custom made signal acquisition platform. This fiber electrode only adds a negligible contribution to the recording system's internal noise (7.626 μV peak-to-peak and 1.234 μV RMS; recording system noise: 6.901 μV peak-to-peak and 1.205 μV RMS). Surface electromyograms (sEMGs) of the biceps bracci and trapezius muscles were recorded during maximal voluntary contractions using the new sensor and compared to that obtained with medical grade commercial sensors. Frequency content and time domain analysis show that the new flexible sensor performs similarly to commercial devices in terms of sEMG signal amplitude discrimination and frequency shift evaluation during the development of muscle fatigue. The advantage of the new sensor is the high flexibility of the fibers allowing their easy integration into stretchable garments without compromising the user's comfort. This will lead to the development of a new generation of smart textile with large application for telemedicine and assistive devices.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.555

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 head0.012
GPT teacher head0.220
Teacher spread0.209 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

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