A Predictive Framework for Real-Time Health Monitoring via Wearable Biosensors

Abstract

Wearable biosensors, coupled with predictive analytics, are transforming real-time health monitoring by providing continuous, personalized insights into physiological metrics. This framework leverages wearable technology and advanced machine learning algorithms to process biometric data, enabling early detection of health anomalies and proactive intervention. Through the integration of biosensors monitoring parameters such as heart rate, blood pressure, glucose levels, and respiratory rate, the predictive framework can identify patterns indicative of potential health risks. Machine learning algorithms analyze real-time data streams, facilitating precise predictions and personalized feedback to users and healthcare providers. This approach not only enhances patient engagement and preventive care but also supports the management of chronic conditions by allowing continuous tracking outside clinical settings. Despite its potential, challenges such as data privacy, battery life limitations, and the accuracy of sensor data must be addressed. This research explores the design, functionality, and implications of a predictive health monitoring framework, examining the role of wearable biosensors in enabling proactive healthcare solutions.