The comfort and ease of human lives are significantly improved by the Internet of Things (IoT) devices' integration of medical signal processing capabilities with cutting-edge sensors. Providing healthcare services to every patient, especially the elderly person those are living in the remote areas and suffering chronic diseases need to monitor in real-time. Remote patient monitoring systems are designed to obtain a number of physiological data from the patients. Most common data are Electrocardiogram (ECG), Electroencephalogram (EEG), heart beats and respiration rate, oxygen volume in blood or pulse oximetry, signals from the nervous system, blood pressure, body/skin temperature and blood glucose level. In this research work, a FOG computing architecture is suggested for the real-time deployment of a remote patient monitoring system. Reducing latency and network consumption is the main driver behind the suggested approach's use of the FOG paradigm. Due to the large amount of data involved in healthcare and the enormous value that accurate predictions hold, the integration of machine learning (ML) algorithms is imperative. The use of Bayesian Belief Network (BBN) classifier algorithms in the healthcare applications that are covered in this work has demonstrated experimental evidence of accuracy and usefulness.

Keywords : Internet of Things (IoT), FOG, Remote Patient Monitoring, ECG, Machine Learning, Bayesian Belief Network.