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Record W2784098778

Quantifying The Signal-To-Noise Ratio of Silicon- Embedded Sensors for Mechanomyography

2017· article· en· W2784098778 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCMBES Proceedings · 2017
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSiliconSIGNAL (programming language)AccelerometerNoise (video)Signal-to-noise ratio (imaging)AcousticsComputer scienceMaterials scienceElectronic engineeringEngineeringOptoelectronicsArtificial intelligencePhysicsTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

Experiments consisted of systematic measurements of the signal-to-noise ratio (SNR) of signals acquired from a mechanical stimulator, using silicon-embedded accelerometers. The objective of using the latter was to determine the combination of embedding properties which provide the highest SNR for mechanomyography (MMG) signal recording. Variations in silicon hardness and geometry were tested. Two important conclusions can be derived from the experiments: (1) It is possible to acquire MMG signals using silicon-embedded sensors; and (2) The embedded sensor's performance is affected by changes in the geometry of the embedding. The intended application of this study is the use of soft silicon suction sockets with embedded sensors as a more comfortable and functional alternative to current hard-socket powered prostheses for below-elbow amputees currently using electromyography as the control signal(s).

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.

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 score0.072
Threshold uncertainty score0.540

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 studies0.0010.000
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 head0.032
GPT teacher head0.268
Teacher spread0.236 · 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