Various algorithms, including statistical approaches and adaptive filtering with or without accelerometer data, can effectively detect and reduce motion artifacts in PPG signals.
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.
Abstract With the rise of wearable devices, which integrate myriad of health-care and fitness procedures into daily life, a reliable method for measuring various bio-signals in a daily setup is more desired than ever. Many of these physiological parameters, such as Heart rate (HR) and Respiratory Rate (RR), are extracted indirectly and using other signals such as Photoplethysmograph (PPG). Part of the reason is that in some cases, such as RR measurements, the devices which directly measure them are cumbersome to wear and thus, rather impractical. On the other hand, signals, such as PPG from which the RR can be extracted, are not very clean. This poses a challenge on reliable extraction of these metrics. The most important problem is that they are corrupted by motion artifacts. In this paper, we review the state of the art algorithms which are used to detect and filter motion artifacts in PPG signals and compare them in terms of their performance. The insight provided by this paper can help the scientists and engineers to obtain a better understanding of the field and be able to use the most suitable technique for their work, or come up with innovative solutions based on existing ones.
Pollreisz et al. (Thu,) 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.