The rapid evolution of smart wearable sensors has revolutionized the landscape of continuous health monitoring, enabling a paradigm shift from reactive to proactive healthcare delivery. These advanced devices integrate a range of miniaturized, non-invasive sensors capable of capturing real time physiological and environmental data, offering critical insights into an individual's health status. With the convergence of wireless communication, cloud computing, and artificial intelligence, wearable technologies are now equipped to process complex data streams and deliver actionable health feedback to both users and healthcare providers. This chapter presents a comprehensive exploration of the technological foundations, sensor types, emerging innovations, and key applications of smart wearable devices in healthcare. It highlights how machine learning and AI are enhancing data interpretation and personalization, while also addressing significant challenges such as data interoperability, system integration, and security compliance. The chapter identifies existing research gaps and emerging trends that are shaping the future direction of wearable health monitoring systems. By synthesizing current advancements and unresolved issues, this work aims to support future research and development efforts in creating more intelligent, accessible, and effective wearable health solutions.
The emergence of smart wearable sensors has ushered in a new era in healthcare, characterized by continuous, real-time health monitoring and personalized care delivery [1]. These devices, embedded with advanced sensing technologies, are capable of collecting diverse physiological and behavioral data such as heart rate, blood pressure, blood oxygen saturation, glucose levels, and even emotional stress indicators [2]. Unlike traditional healthcare systems that rely heavily on episodic data from infrequent clinical visits, wearable sensors allow for uninterrupted data acquisition throughout an individual’s daily life [3]. This continuous stream of information enables a shift from reactive treatments to preventive and predictive health management, where early warning signs of disease or abnormal health patterns can be identified before the onset of clinical symptoms [4]. The ability to monitor patients remotely was particularly transformative in managing chronic diseases and supporting aging populations, thereby reducing the burden on healthcare infrastructures [5]. Smart wearable devices have evolved significantly due to the convergence of several technological advancements, including microelectromechanical systems (MEMS), wireless communication protocols, cloud computing, and artificial intelligence [6]. These components allow wearable sensors to be lightweight, energy-efficient, and highly sensitive while supporting real-time data transmission to healthcare systems [7]. The integration of machine learning algorithms further enhances their capability to detect complex patterns in physiological data, enabling more accurate diagnoses and personalized recommendations [8]. With the growing emphasis on mobile health (mHealth), these wearables serve as vital tools for both patients and healthcare providers, supporting home-based care, remote diagnostics, and telemedicine [9]. The development of wearable biosensors, which can detect biochemical markers in sweat, saliva, or interstitial fluid, has further extended the potential applications of wearable technology into non invasive diagnostics and real-time biochemical monitoring [10].ÂÂÂ
