A Robust Fusion Method for Motion Artifacts Reduction in Photoplethysmography Signal
Why this work is in the frame
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Bibliographic record
Abstract
Robustness of estimating cardiorespiratory parameters from photoplethysmography (PPG) signal is highly dependent on the quality of the signal, which is heavily affected by motion artifacts. To increase the estimation accuracy of cardiorespiratory parameters, this article describes a novel fusion method to efficiently and effectively reduce the motion artifacts from the acquired PPG signal. The proposed fusion technique requires simultaneously acquiring data from a PPG sensor and accelerometer. To filter out the frequencies associated with motion, the method uses stopband filters with a central rejection frequency and bandwidth determined by the output signal of the accelerometer. Under such conditions, the proposed method to remove the motion artifacts does not depend on the quality of the reference signal and has almost no impact on the nature of PPG signals (i.e., amplitude, baseline, and periodicity). The effectiveness of the proposed method in the suppression of in-band and out-of-band frequencies of motion is numerically and experimentally evaluated. It is shown that the filtered PPG signal has sufficient information to estimate different cardiac parameters such as heart- (HR), respiration rate (RR), and blood oxygen saturation (SpO2). The motion artifact-free PPG signal obtained using our proposed method can estimate HR, RR, and SpO2 with an accuracy of above 95%. This level of accuracy confirms the usefulness of the proposed fusion method for accuracy improvement of cardiorespiratory parameters monitored by the filtered PPG signal.
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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 | 0.000 | 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 it