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Acquiring Photoplethysmography (PPG) Signal Without LED

2023· article· en· W4384158808 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
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhotoplethysmogramPhotodiodeWearable computerSIGNAL (programming language)Computer scienceMicroprocessorContext (archaeology)Battery (electricity)Light-emitting diodeWearable technologyPower (physics)Real-time computingElectronic engineeringArtificial intelligenceEmbedded systemElectrical engineeringMaterials scienceEngineeringWirelessTelecommunicationsOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Photoplethysmography (PPG) sensors should use the least amount of power possible when integrated into wearables as battery life is one of the main concerns in wearable area. In this context, we propose a system which can collect PPG signals under ambient light conditions without turning on any LED of the PPG sensor (NO-LED mode). The system consists of an efficient analog front end, a photodiode and a microprocessor. Results show that PPG signals recorded with the proposed system under different ambient light conditions can be placed in reliable or acceptable category. Moreover, heart rate estimated from NO-LED mode PPG signal shows less than 6% error when compared with heart rate estimated from a typical PPG signal acquired with LED on. Since LED is the most power consuming part of the PPG sensor, the proposed system has potential for increasing the battery life of PPG sensor-based wearables.

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 score0.038
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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