Personalized Machine Learning for Sleep and Recovery Assessment Using Continuous Photoplethysmographic Monitoring

Abstract

The increasing availability of wearable photoplethysmographic sensors has created new opportunities for longitudinal sleep and recovery assessment outside clinical sleep laboratories. However, translating raw optical pulse signals into reliable, individualized estimates of sleep architecture and recovery status remains a systems-level challenge. This paper examines personalized machine learning approaches for sleep and recovery assessment using continuous photoplethysmographic monitoring. Rather than focusing narrowly on model accuracy, the discussion integrates sensor data acquisition, signal quality management, personalization architectures, computational infrastructure, governance, fairness, robustness, and sustainability. The analysis emphasizes that photoplethysmography-derived sleep assessment is not simply a signal processing problem but an infrastructure and policy problem involving heterogeneous devices, noisy ambulatory environments, individual physiological variability, and evolving regulatory expectations. Personalized models must adapt to inter-individual differences in cardiovascular dynamics, sleep physiology, and activity patterns while preserving privacy and maintaining clinical credibility. The paper reviews structural trade-offs between centralized, edge-based, and federated learning architectures, considers the role of domain adaptation and incremental personalization, and evaluates the implications of data drift, algorithmic bias, and long-term system maintenance. A central argument is that sustainable deployment requires balancing model expressiveness with interpretability, local responsiveness with global knowledge transfer, and innovation with transparent governance. The conclusion outlines future directions for resilient, fair, and clinically meaningful personalized sleep and recovery systems based on continuous photoplethysmography.