Joint Attention Mechanism Learning to Facilitate Opto-physiological Monitoring during Physical Activity
This work addresses signal quality issues in wearable health monitoring during exercise, offering incremental improvements for applications like fitness tracking and medical diagnostics.
The paper tackled motion artefacts degrading opto-physiological monitoring during physical activity by proposing an attention-mechanism-based generative adversarial network (AM-GAN), achieving mean absolute errors as low as 1.37 beats/min for heart rate and 1.65% for SpO2 across multiple datasets.
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.