Lightweight and interpretable convolutional neural network for real-time heart rate monitoring using low-cost video camera under realistic conditions
Bibliographic record
Abstract
Recent research has shown that a person's heart rate (HR) can be estimated using video data through remote photoplethysmography (rPPG). However, this approach is faced with various challenges, including the inability to prepare training data that encompasses all realistic conditions, the impact of complex inference models on reasoning speed, and the lack of interpretability that hinders medical and healthcare applications. To tackle these issues, a lightweight and interpretable convolutional neural network is proposed for real-time HR monitoring using a low-cost video camera under realistic conditions. The Mediapipe framework is leveraged to construct a facial detection and tracking pipeline that is robust to head movements and illumination changes. Empirical mode decomposition (EMD) is then combined with a channel-wise attention-based convolutional neural network (CNN) for HR inference. Additionally, a temporal long-term peak merge method is proposed as a post-processing step to further enhance the accuracy of the neural network inference. The results of linear regression and Bland-Altman analysis demonstrate consistency between the estimated HR values and the ground truth. Moreover, experimental outcomes show no significant difference in the inference times of the proposed method running with or without a GPU, with a reasoning speed on mobile CPU remaining within 100 ms, ensuring real-time HR monitoring. Furthermore, this study provides pioneering empirical evidence to open the black box of neural networks in HR monitoring using rPPG signals.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".