QRWOA-VMD Enhanced Heart Rate Monitoring Using PCR Radar
Bibliographic record
Abstract
Monitoring vital signs is essential for assessing individuals' health status and supporting various medical interventions; however, conventional methods depend on expensive and invasive hospital-based or wearable devices. This article presents a novel approach to contactless heart rate monitoring that leverages an Antenna-on-Package Pulse Coherent Radar (AoP PCR) system. To address the inherently low sampling rates associated with the pulse repetition frequency of the PCR during remote monitoring, a signal enhancement algorithm is presented. This algorithm leverages the quasi-periodic nature of chest displacement signals, leading to significantly improved temporal resolution and enabling reliable heart rate monitoring using a cost-effective PCR system. Furthermore, extracting heartbeat signals faces a significant challenge in optimally tuning the parameters of Variational Modal Decomposition (VMD) due to variations in distance and angle. To tackle this, an enhanced method called VMD based on the Whale Optimization Algorithm with Quasi-Reflection Learning (QRWOA-VMD) has been devised to enhance the precision of parameter optimization in VMD, thereby improving the decomposition accuracy of heartbeat signals across diverse angles and distances, leading to more reliable and robust heartbeat signal extraction. Comprehensive evaluation demonstrates that the proposed method achieves over 97% accuracy in heart rate monitoring under standard conditions, with the radar facing the chest within a 1.5-meter range. Even in challenging scenarios, such as a ±30° azimuth angles and a 20° elevation angle relative to the chest, accuracy remains above 93%.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".