Adaptive Algorithm for Motion Artifacts Removal in Wearable Biomedical Sensors During Physical Exercise

Abstract

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Motion artifacts (MA) are significant sources of noise in the wearable devices that are used to collect biological information from the human body. Photoplethysmography (PPG) is an example of the sensors embedded in many of these devices. PPG signals are highly influenced by MA, especially when the motion involves the organ that the PPG sensor is attached to. The aim of this paper is to build an adaptive algorithm and multiresolution analysis technique to denoise the raw PPG signals, suppress the MAs and accurately estimate the HR as an example of MA suppression in the wearable devices. The challenge here is to find the right selection considering high accuracy and low-computational complexity. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> Discrete wavelet transform recursive inverse (DWT-RI) adaptive filter algorithm in addition to multiresolution analysis is implemented to suppress MAs and estimate the HR using the simultaneously recorded accelerations in the three dimensions as reference signals for the adaptive algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The proposed algorithm has a low computational cost and results in an absolute average error of 1.17 beats per minute (BPM) when tested on 12 subjects from a well-known database for this purpose. The performance of the proposed algorithm is compared to the other existing methods in literature used for noise removal and motion artifact suppression. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> DWT-RI adaptive algorithm can successfully suppress motion artifacts in PPG signals with a low computational complexity and outperforms several alternatives in terms of the average absolute error.