WEARABLE SENSORS FOR REMOTE HEALTH MONITORING: SIGNAL PROCESSING AND DATA ANALYTICS

Abstract

The advent of wearable sensor technology has revolutionized the field of remote health monitoring, enabling the continuous and non-intrusive collection of vital health data. This paper explores the integration of signal processing and data analytics in harnessing the potential of wearable sensors for enhancing healthcare delivery. The primary objective of this research is to provide a comprehensive overview of the methodologies and technologies that enable remote health monitoring, focusing on signal processing techniques and data analytics. Through an extensive literature review, we examine the evolution of wearable health sensors and their applications in health monitoring. We delve into signal processing, emphasizing data preprocessing, feature extraction, and time-frequency analysis to enhance the quality and interpretability of sensor data. Furthermore, we explore data analytics, encompassing data storage, visualization, machine learning algorithms, and real-time analytics for deriving actionable insights from the collected data. Additionally, we discuss the integration of these two approaches, highlighting their synergy, benefits, and the challenges they pose. This research addresses the technical challenges, ethical considerations, and regulatory aspects associated with wearable health sensors. Finally, we provide insights into emerging trends and future research directions in the rapidly evolving field of remote health monitoring. The convergence of signal processing and data analytics holds promise for enhancing healthcare by enabling early disease detection, personalized treatment, and improved patient outcomes.

Keywords: Wearable Sensors, Remote Health Monitoring, Signal Processing, Data Analytics