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A Low-Power High Input Range PPG Readout Amplifier with a Current Buffer Input<sup>*</sup>

2023· article· en· W4389537755 on OpenAlexafffund
Mahziar Serri Mazandarani, Gabriel Gagnon-Turcotte, Reza Papi, Benoit Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsCMC Microsystems (Canada)Université Laval
FundersCMC Microsystems
KeywordsBuffer (optical fiber)AmplifierCurrent (fluid)Buffer amplifierPower (physics)OptoelectronicsRange (aeronautics)Dynamic rangeOperational amplifierElectrical engineeringMaterials sciencePhysicsComputer scienceCMOSEngineeringOptics

Abstract

fetched live from OpenAlex

This paper presents ultra-low power photoplethysmography (PPG) readout circuits. The proposed system architecture uses a current buffer between the photodiode (PD) and the transimpedance amplifier (TIA) to isolate the large parasitic capacitance of the PD leading to improves the power consumption of the TIA. A class AB topology is exploited at the output of the amplifier, which allows for increased drive capability without the use of auxiliary circuits. The maximum input current range of the TIA is 160 µA, so the large DC current of the input signal does not saturate the circuit. In the LED driver circuit, by varying the duty cycle of a pulse wave modulation (PWM) signal, the ON and OFF times of the circuits. The amplifier and LED driver are manufactured in the 130 nm TSMC CMOS process. The power consumption of the circuits with a duty cycle of 1% is 3.28 µW (at VDD = 1.2V).Clinical Relevance- Vital signs are becoming a very important research topic due to the recent prevalence of COVID-19 and other respiratory diseases. This research aims to develop and interface circuits to monitor vital signs including blood pressure, heart rate, and respiratory rate to study respiratory disease, drug safety, and efficacy.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.014
GPT teacher head0.227
Teacher spread0.213 · 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.

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

Citations2
Published2023
Admission routes2
Has abstractyes

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