GAN-Enhanced Reconstruction of Motion-Corrupted Physiological Signals for Wearable Health Monitoring
Keywords:
wearable health monitoring; generative adversarial networks; motion artifact correction; physiological signal reconstruction; responsible deployment; edge computingAbstract
Wearable health monitoring systems increasingly collect photoplethysmographic, electrocardiographic, and bioimpedance signals during everyday activity, but ambulatory motion introduces complex and nonstationary artifacts that degrade clinical and wellness inferences. Conventional filtering and handcrafted signal decomposition methods often fail when noise overlaps with physiological bandwidths or when motion patterns vary across individuals and contexts. This paper examines the system-level role of generative adversarial networks in reconstructing motion-corrupted physiological signals, focusing not on a single algorithmic formulation but on architectural trade-offs, infrastructure constraints, governance requirements, and deployment pathways. The paper discusses how adversarial training can learn a manifold of plausible physiological waveforms and produce reconstructions that preserve diagnostically relevant morphology while suppressing motion-related distortion. The analysis addresses conditional architectures, domain adaptation, sensor heterogeneity, and the need for lightweight models suitable for edge execution. It further considers fairness risks arising from training data that underrepresent skin tones, body compositions, and motion profiles, as well as the sustainability implications of training and deploying deep generative models in wearable ecosystems. The paper argues that GAN-enhanced reconstruction should be treated as a socio-technical infrastructure rather than an isolated signal processing module, requiring careful attention to validation, regulatory oversight, privacy, and long-term maintenance. A forward-looking perspective is offered for responsible integration into remote monitoring, digital clinical trials, and population health platforms.
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