Advanced Covariance Methods for IoT-Based Remote Health Monitoring

The integration of Internet of Things (IoT) technology in healthcare plays a significant role in remote health management. It enables real-time data collection and patient monitoring. This research study aims to enhance data accuracy, reliability, and predictive capabilities of the IoT network in healthcare by exploring advanced covariance techniques, which include Kalman filters, particle filters, and covariance intersection. Kalman filters process real-time data by minimizing the mean of the squared error and estimating the state of a system accurately. Particle filters are used to handle non-linear systems and provide accurate estimates using a set of random samples, while Covariance intersection fuses data from multiple sources. It does this without needing any knowledge of the correlation between various variables, which makes it ideal for IoT applications. Initially, data is collected from wearable sensors, home monitoring systems, and mobile health applications. Wearable sensors measure heart rate, blood pressure, and glucose levels. Home monitoring systems track environmental factors and patient activities, and Mobile health applications gather patient-reported data. Secondly, Data preprocessing techniques are used to clean the data and handle missing values. Kalman filters provide continuous health updates. Particle filters predict health trends, and Covariance intersection integrates data from multiple IoT devices. To evaluate the performance of these covariance techniques compared with traditional schemes such as simple averaging, weighted averaging, and basic linear regression using various performance metrics, which include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), correlation coefficients, Precision, Recall, F1 Score and Area Under the Curve (AUC). The results show that covariance methods have enhanced overall system performance by 20% in terms of accuracy, 15% in precision, and 18% in recall. By fusing data seamlessly, covariance intersection ensures an accurate understanding of patient health across different environmental and situational contexts.