Deep Learning-Based Motion Artifact Removal for Wearable Photoplethysmographic Signal Enhancement

Abstract

Wearable photoplethysmography has become a central sensing modality for continuous cardiovascular monitoring, yet its practical value is constrained by motion artifacts that corrupt the underlying blood volume pulse waveform. Deep learning-based artifact removal has emerged as a promising strategy because it can learn complex mappings between corrupted and clean representations without relying solely on hand-engineered assumptions. This paper provides a system-level analysis of deep learning-based motion artifact removal for wearable photoplethysmographic signal enhancement. It examines architectural choices, training data infrastructure, model governance, deployment models, robustness, fairness, and sustainability. The discussion treats artifact removal as a sociotechnical infrastructure rather than an isolated signal processing task. It explores how convolutional, recurrent, attention-based, and encoder-decoder designs interact with the energy, memory, and latency constraints of wearable platforms. The paper also considers the governance implications of data provenance, annotation uncertainty, performance drift, and algorithmic bias. In particular, it addresses how differential optical behavior across skin pigmentation, sensor placement, and motion conditions can produce uneven performance if not explicitly managed during data collection and validation. The work argues that safe deployment requires integrating deep learning models with continuous monitoring, edge-based inference, and regulatory oversight. It further highlights the importance of multimodal sensor fusion, uncertainty estimation, and federated learning as pathways toward robust and equitable wearable cardiovascular sensing. The conclusion synthesizes these perspectives and identifies future research directions that connect signal enhancement, infrastructure governance, and clinical accountability.