Ambient Light-Driven Wireless Wearable Finger Patch for Monitoring Vital Signs From PPG Signal
Bibliographic record
Abstract
In recent years, wearable health monitoring using photoplethysmogram (PPG) has become a popular trend. However, one of the main challenges of PPG sensing is the power usage of the light-emitting diode (LED) in the technique. In this work, we designed a wireless wearable finger patch that can record PPG signals solely using ambient light, entirely eliminating the requirement for LED power consumption. The finger patch is implemented on a two-layer flexible polyimide substrate and is based on a high sensitivity silicon photodiode (PD) and a high dynamic range analog front end (AFE). The entire circuit is powered by a rechargeable Li-ion coin battery. It also uses a Bluetooth module to send the PPG data wirelessly. The system is validated with 12 healthy subjects for the collection of PPG signals under three ambient light conditions. Assessment of the PPG signals quality demonstrates that the PPG signals acquired under the ambient light conditions can be placed in the reliable or acceptable category. Finally, two vital signs: heart rate (HR) and blood pressure (BP) are extracted from the no-LED mode PPG signals obtained from 12 subjects using the finger patch. The calculated maximum mean absolute error (MAE) for HR in comparison to the reference measurement is small (3.4 BPM). For BP prediction AlexNet network with training of all layers with finger patch PPG data shows better performance and produces BP with an average MAE of 8.1 mmHg and 6.05 mmHg for systolic BP (SBP) and diastolic BP (DBP), respectively. Without any need to turn on any LED, the finger patch can save power and will have the potential for long-term health monitoring without frequently charging batteries.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".