Key result
Various algorithms, including statistical approaches and adaptive filtering with or without accelerometer data, can effectively detect and reduce motion artifacts in PPG signals.
Why the study?
PPG signals used by wearable devices to extract metrics like heart rate and respiratory rate are often corrupted by motion artifacts, challenging reliable measurement.
Comparison
State of the art algorithms used to detect and filter motion artifacts in PPG signals
Design
Review
Authors
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May improve wearable PPG heart rate accuracy; extends comparative evidence but requires prospective validation before clinical adoption.
This review provides a comprehensive comparison of state-of-the-art algorithms for detecting and removing motion artifacts in PPG signals, highlighting that methods utilizing accelerometer data or synthetic references offer superior noise reduction for accurate heart rate extraction.
Pollreisz et al. (2019) conducted a review in Motion artifacts in PPG signals. Motion artifact detection and removal algorithms vs. Unfiltered/corrupted PPG signals was evaluated on Heart rate estimation error. Various algorithms, including statistical approaches and adaptive filtering with or without accelerometer data, can effectively detect and reduce motion artifacts in PPG signals.
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