Abstract
This article presents an analysis of the use of wearable devices on the Internet of Things. To prevent serious situations in the medical field, it is necessary to develop methods that ensure fast and accurate detection of infections in remote areas. The main task of technology is to perform many functions. In order to reduce the waiting time and minimize communication loss during detection, as well as improve the accuracy of diagnosis, the proposed device uses a multi-purpose architecture. In this work, a wearable IoT design method was applied using various mathematical approaches to solve the problems. The obtained parametric data is stored for an engineering method using machine learning, which is used to reduce device processes. Such technologies are integrated with modern monitoring methods. The developed wearable devices can be used in medical applications for remote health monitoring in real time. The proposed approach was tested in real conditions, and the IoT model was used to analyze the comparative results. In all cases, the proposed method showed better results compared to existing solutions.