FlexiMA: Robust Motion Artifact Removal in PPG Signals Using an Adversarial Variational Autoencoder

Motion artifacts (MA) significantly compromise the quality of photoplethysmographic (PPG) signals in wearable devices, hindering the accurate extraction of vital sign parameters. To address the limitations of existing methods in cross-user MA detection and the low accuracy of signal reconstruction, this paper proposes FlexiMA, a two-stage co-optimization framework that operates without reference signals. This framework is designed to tackle two key tasks: MA detection and MA removal. In the detection phase, a task-adaptive diffusion model, IMD-XResNet1D, is developed by integrating meta-learning and diffusion mechanisms, enabling efficient modeling of artifact-related features in new users. In the removal phase, an adversarial variational autoencoder (AVAE) is employed to achieve high-fidelity reconstruction of PPG signals. Experiments conducted on multi-user, multi-scenario datasets demonstrate that FlexiMA achieves 99.68% accuracy in MA detection and maintains a cross-user generalization performance of 99.32%. In the MA removal task, it attains 87.85% waveform structure similarity with an interbeat interval (IBI) error of 4.60 ms, indicating reliable signal reconstruction. These results verify the robustness and adaptability of FlexiMA in dynamic and complex environments, highlighting its strong potential for deployment in wearable applications.