AI-Assisted Sleep Quality Assessment Using Wrist-Worn Physiological Sensors

Abstract

Sleep quality assessment is undergoing a significant transformation as wrist-worn physiological sensors become capable of capturing continuous photoplethysmography, accelerometry, and derived autonomic signals in naturalistic environments. Artificial intelligence methods can translate these multimodal sensor streams into clinically meaningful sleep quality indicators, but the design of such systems raises substantial interdisciplinary challenges that extend well beyond model accuracy. This paper presents a systems-level examination of AI-assisted sleep quality assessment using wrist-worn physiological sensors. It analyzes the architectural tensions among on-device processing, cloud-based inference, and clinical integration, and it discusses the sensing, data infrastructure, machine learning, robustness, fairness, governance, and deployment dimensions that jointly determine system viability. The paper argues that effective sleep AI systems must be understood as socio-technical infrastructures rather than isolated algorithmic artifacts. Structural trade-offs include the balance between energy efficiency and signal fidelity, the tension between deep learning representational capacity and interpretability, the need for population diversity in training data, and the regulatory requirement for continuous monitoring of model behavior after deployment. The discussion draws on existing wearable sleep research, clinical artificial intelligence, algorithmic fairness, and health data governance to articulate design considerations for sustainable and trustworthy sleep assessment platforms. By foregrounding system architecture and governance rather than narrow algorithmic performance, the paper offers a cross-domain perspective on building responsible AI-enabled sleep health infrastructures.