SS-FD: Internet of medical things based patient health monitoring system

Abstract

Internet of Medical Things (IoMT) consists of connected devices used to collect patient health information in a real-time environment. The IoMT device effectively handles medical issues by using health wearable and medical-grade wearables. Although IoMT can process the collected data, it has few pitfalls, such as interoperability of data, standardization issues, and computation complexity while detecting disease. By considering these issues, in this work, IoMT is utilized in the field of the remote patient monitoring system. Initially, the IoMT devices are placed on the human body and collect their health information continuously. The gathered details are processed using a salp swarm optimized fuzzy deep neural network (SS-FD). This system supports the patient health monitoring process with minimum low-cost consumption. The SS-FD classifier processes the obtained data; primary and emergency data is classified according to the fuzzy rule. This process improves the remote patient health data analysis and reduces the difficulties involved in the patient health analysis. Then the efficiency of the system is evaluated using experimental results