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Record W2053115824 · doi:10.1109/iscas.2013.6572334

A Low-power wireless multi-channel surface EMG sensor with simplified ADPCM data compression

2013· article· en· W2053115824 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsComputer scienceMicrocontrollerWirelessChannel (broadcasting)Data compressionSIGNAL (programming language)Wireless sensor networkTransmission (telecommunications)Data transmissionComputer hardwareReal-time computingEmbedded systemComputer visionTelecommunicationsComputer network

Abstract

fetched live from OpenAlex

The ubiquitous real-time monitoring and recording of surface electromyography (sEMG) signal is essential to several rehabilitation applications, such as muscle recovery analysis. We present an inexpensive wireless sEMG sensor using a commercial off-the-shelf wireless microcontroller unit (MCU) incorporating a simplified adaptive differential pulse code modulation (ADPCM) routine for real-time data compression. In single-channel configuration, the presented approach reduces power consumption of a transmission subsystem by up to 69%, leading to longer operation life expectancy. Due to the excessive amount of data as well as the limited processing power of embedded MCUs, multi-channel configurations would normally not be feasible. However, the proposed compression method makes a multi-channel EMG sensor possible. The distortion induced by this approach on EMG signal is on the order of 1%. Test results from in vivo trials with humans are presented.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.025
GPT teacher head0.226
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

Quick stats

Citations12
Published2013
Admission routes1
Has abstractyes

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