WEARABLE-SENSOR FUSION FOR UBIQUITOUS HUMAN HEALTH MONITORING

Abstract

Wearable sensors employed in human health monitoring have seen improvements in recent years due to the integration of advanced data fusion techniques. As an example, physiological sensors such as accelerometers and gyroscopes, photoplethysmography sensors, and even electrocardiography uses are incorporated into non-invasive wearable devices that are capable of monitoring vital signs in real-time. Data from diverse sensors is combined by sensor fusion algorithms for accurate and reliable assessments of wearable devices. Machine learning is now incorporated into patients' current health status, and the condition of the patients is evaluated in real-time, enabling timely healthcare access. Latest innovations enhance visual indicators and even physical movements for monitoring, allowing potential health complications to be detected beforehand. Apart from prospects, concerns remain regarding data integration, potential data leakage, privacy issues, data compliance, and ensuring overall user compliance. The goal of the research paper is to provide solutions for securing wearable-sensor-based health monitoring systems, along with a well-rounded review of the corresponding challenges and future advantages. By addressing these goals, the study aims to develop wearable monitoring systems that are capable of enabling security, reliability, and usability.