AI-Powered Wearable Devices for Real-Time Health Monitoring: Technologies, Challenges, and Emerging Trends
TL;DR
This work connects biomedical signal capture, on-device AI, clinical translation, and digital ethics to delineate a roadmap for frontier AI wearables, and proposed edge-aware transformer-based models, privacy-preserving analytics, and global multimodal sensing advance intelligent, scalable, and fair AI-driven health monitoring systems.
Abstract
Artificial intelligence (AI) enabled wearable devices have revolutionized the healthcare monitoring by offering real-time and continuous monitoring of users' physiological signals used at home. This review analyzed the technological, algorithmic and clinical advancement from 2020 to 2025, detailing the technologies for biosensor design, e-enabled Machine Learning (ML) solutions, edge computing architectures, and privacy-preserving frameworks. Based on the PRISMA-ScR approach, quantitative Neural Networks (NNs), AutoML pipelines, federated learning and secure on-device analytics in a representative fashion were reviewed. Significant innovations in core basic pillars were detected: frontier sensor technologies, including multimodal fusion, and self-powered systems. Resource-efficient embedded AI models customized for constrained environments have further enhanced on-device intelligence, real-time analytics, ethical norms and regulatory paradigms. Nevertheless, such challenges are still very complicated and hard to solve. Signal instability, energy constraints, algorithmic bias and lack of demographic diversity in training data undermine model reliability and generalizability, compromising the safe, fair, and scalable implementation of such AI-driven wearable health monitoring systems in real clinical settings. The lack of validation in real-world scenarios, model explainability and alignment with regulatory frameworks of most have hampered their clinical adoption and broad-scale deployment. Ethical aspects like data sovereignty, fairness, and dynamic consent are still largely to be formed. This work connects biomedical signal capture, on-device AI, clinical translation, and digital ethics to delineate a roadmap for frontier AI wearables. This proposed edge-aware transformer-based models, privacy-preserving analytics, and global multimodal sensing advance intelligent, scalable, and fair AI-driven health monitoring systems.