Abstract
BackgroundWearable devices have emerged as pivotal tools in revolutionizing healthcare delivery, shifting the paradigm from reactive treatment to proactive, personalized health management. This article explores the multifaceted contributions of these technologies to real-time physiological monitoring and the sophisticated prediction of disease onset, emphasizing their integration with advanced analytical methods.MethodsA comprehensive literature review was conducted, synthesizing findings from prominent studies published up to February 2024. The review focused on wearable device capabilities in continuous data acquisition, the application of machine learning and deep learning algorithms for health insights, and their utility in diverse clinical and wellness contexts. Key areas examined included cardiovascular health, stress detection, and the broader framework of the Internet of Medical Things (IoMT).ResultsWearable devices demonstrate significant efficacy in continuously tracking vital signs, activity levels, and other physiological parameters. Integrated with AI, these systems enable highly accurate real-time detection of anomalies, such as cardiac arrhythmias (Unknown, 2024), and provide robust models for predicting conditions like stress (Lazarou & Exarchos, 2024) and cardiovascular events (Miah, 2019). The synergy between nano-enabled sensors (Doe, 2022) and big data analytics (Unknown, 2019) is enhancing diagnostic precision and facilitating personalized interventions.ConclusionsWearable technology, underpinned by advanced computational methods, offers unprecedented opportunities for real-time health surveillance and predictive diagnostics. While challenges related to data privacy, interoperability, and clinical validation persist, their continued evolution promises to significantly enhance precision health, enabling earlier disease intervention and fostering a more proactive approach to individual well-being.
Keywords
Wearable devices, Real-time monitoring, Disease prediction, Precision health, Machine learning, Internet of Medical Things, Cardiovascular health, Predictive analytics