A Robust Fusion Method for Motion Artifacts Reduction in Photoplethysmography Signal
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".