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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$180~\mu $ </tex-math></inline-formula>A, preventing circuit saturation. Also, with a 1% duty cycle (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100~\mu $ </tex-math></inline-formula>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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".