Self-Supervised Learning for Reliable Photoplethysmographic Signal Reconstruction Under Dynamic Motion

Authors

  • Ralph Virtanen Department of Computer Science, Binghamton University, Binghamton, NY, USA.
  • Ajay Cohen Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
  • Niklas A. Park Department of Computer Science, George Mason University, Fairfax, VA, USA.

Keywords:

photoplethysmography; self-supervised learning; motion artifact; wearable health; signal reconstruction; responsible artificial intelligence; health systems

Abstract

Photoplethysmography has become central to wearable health monitoring, but its clinical utility is constrained by motion-induced signal corruption. Recent developments in self-supervised learning offer a promising route for reconstructing photoplethysmographic waveforms without relying on large amounts of labeled clinical data. This paper presents a systems-level analysis of self-supervised signal reconstruction under dynamic motion. It examines the architectural trade-offs between reconstruction fidelity, computational burden, and deployability across edge and cloud resources. Rather than focusing narrowly on algorithmic performance, the discussion integrates data infrastructure, lifecycle governance, robustness, fairness, and regulatory policy. The analysis highlights how self-supervised pre-training can exploit large unlabeled corpora of ambulatory physiological recordings, while downstream adaptation must still satisfy clinical validation demands. It further addresses concerns related to population heterogeneity, skin tone, sensor placement, motion intensity, and longitudinal drift. We argue that reliable photoplethysmographic reconstruction is not solely a signal processing problem but a socio-technical systems challenge requiring coordinated advances in representation learning, data curation, model auditing, and deployment practice. The paper concludes with forward-looking perspectives on sustainable and equitable integration of self-supervised reconstruction into wearable health infrastructures.

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Published

2026-07-07

How to Cite

Ralph Virtanen, Ajay Cohen, & Niklas A. Park. (2026). Self-Supervised Learning for Reliable Photoplethysmographic Signal Reconstruction Under Dynamic Motion. International Journal of Clinical and Translational Medicine, 1(1). Retrieved from https://ijctmed.org/index.php/home/article/view/212

Issue

Vol. 1 No. 1 (2026): International Journal of Clinical and Translational Medicine

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Articles

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