Joint Attention Mechanism Learning to Facilitate Opto-physiological Monitoring during Physical Activity

TL;DR

This paper introduces AM-GAN, an attention-based generative adversarial network, that effectively reduces motion artefacts in PPG signals during physical activity, improving accuracy of cardiac and respiratory measurements.

Contribution

The paper presents a novel AM-GAN model that models and mitigates motion artefacts in PPG signals, enhancing physiological monitoring during physical activity.

Findings

Achieves low MAE for heart rate estimation across multiple datasets.

Effectively reduces motion artefacts in PPG signals during various activity intensities.

Provides accurate SpO2 and respiratory rate measurements under different oxygen levels.

Abstract

Opto-physiological monitoring including photoplethysmography (PPG) provides non-invasive cardiac and respiratory measurements, yet motion artefacts (MAs) during physical activity degrade its signal quality and downstream estimation concurrently. An attention-mechanism-based generative adversarial network (AM-GAN) was proposed to model motion artefacts and mitigate their impact on raw PPG signals. The AM-GAN learns how to transform motion-affected PPG into artefact-reduced waveforms to align with triaxial acceleration signals corresponding to artefact components gained from a triaxial accelerometer. The AM-GAN has been validated across four experimental protocols with 43 participants performing activities from low to high intensity (6--12km/h). With the public datasets, the AM-GAN achieves mean absolute error (MAE) for heart rate (HR) of 1.81 beats/min on IEEE-SPC and 3.86 beats/min on PPGDalia. On the in-house LU dataset, it shows the MAEs < 1.37 beats/min for HR and 2.49 breaths/min for respiratory rate (RR). A further in-house C2 dataset with three oxygen levels (16%, 18%, and 21%) was applied in the AM-GAN to attain a MAE of 1.65% for SpO2. The outcome demonstrates that the AM-GAN offers a robust and reliable physiological estimation under various intensities of physical activity.