A Highly Duty-Cycled PPG Sensor With Ultralow-Power Consumption and Wide Input Range
Bibliographic record
Abstract
This article presents an innovative ultralow-power photoplethysmography (PPG) readout circuit designed for driving red (R) and infrared (IR) light-emitting diodes (LEDs) and extracting a PPG signal with high energy efficiency. Implemented in TSMC 130-nm CMOS technology, this new PPG sensor improves upon previous solutions in several ways including: 1) employing a current buffer between the photodiode (PD) and the transimpedance amplifier (TIA) to isolate the PD’s substantial parasitic capacitance, which among others, allows using shorter duty cycles than other solutions, thereby reducing the power consumption significantly; 2) utilizing a class AB output stage in the TIA to enhance the dynamic range and cope with large dc input components; and 3) using a novel LED driver circuit, which relaxes the power supply requirements and, consequently, reduces the overall power consumption. Employing the mentioned techniques, the TIA achieves a wide input current range of$180~\mu $A, preventing circuit saturation. Also, with a 1% duty cycle ($100~\mu $s) and a PWM frequency of 100 Hz, the measured power consumption is 0.3 mW, with typical driving currents of 9 mA for IR, and 15 mA for red LEDs. The fabricated chip was incorporated into a finger-clip PPG sensor integrating LEDs and a PD for facilitating testing and validation in realistic conditions with five participants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".