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

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.