Abstract
Long-term heart rate (HR) monitoring by wrist-worn photoplethysmograph (PPG) sensors enables the assessment of health conditions during daily life with high user comfort. However, PPG signals are vulnerable to motion artifacts (MAs), which significantly affect the accuracy of estimated physiological parameters such as HR. This paper proposes a novel modular algorithm framework for MA removal based on different wavelengths for wrist-worn PPG sensors. The framework uses a green PPG signal for HR monitoring and an infrared PPG signal as the motion reference. The proposed framework includes four main steps: motion detection, motion removal using continuous wavelet transform, approximate HR estimation and signal reconstruction. The proposed algorithm is evaluated against an electrocardiogram (ECG) in terms of HR error for a dataset of 6 healthy subjects performing 21 types of motion. The proposed MA removal method reduced the average error in HR estimation from 4.3, 3.0 and 3.8 bpm to 0.6, 1.0 and 2.1 bpm in periodic, random, and continuous non-periodic motion situations, respectively.
Highlights
Photoplethysmography (PPG) is a widely used non-invasive optical sensing technology to monitor the cardiovascular and respiratory systems
The performance of the algorithm is evaluated in terms of the error in is performed averagebased
heart rate (HR) is calculated on a beat to beat basis
Summary
Photoplethysmography (PPG) is a widely used non-invasive optical sensing technology to monitor the cardiovascular and respiratory systems. While tapping one of the fingers, the wrist can stay still, which makes accelerometers integrated in a wrist band ineffective as a motion reference These fine-grain movements have a large effect on wrist PPG signal quality. A multichannel PPG sensing strategy could account for the variability in signal quality resulting from how the device is worn across individuals [8,9] Another comparison between accelerometer and the IR PPG channel is with respect to system design. A modular algorithm framework for MAR based on PPG obtained by different wavelengths is proposed in this paper. This approach is verified using data of six heathy subjects performing.
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