Design and Execution of Machine Learningenabled Wearable Sensor System for Real-Time Physiological Health Monitoring

In the present scenario Physiological health monitoring became a major challenge for every scientist, researchers and doctors. Through this paper an attempt has been made to overcome and focus on this issue. To achieve the same from basic principles to major technologies has been used. First of all, a deep research has been done on different sensor technologies, wearable devices, different parameters, materials, human health's, environmental response and factors etc. Later a very strong and modern age technology called Machine Learning has been used to enabled the diverse wearable devices in healthcare monitoring-based issues. Initially, the basic studies of sensors and different parameters has been done in COMSOL tool later after understanding the entire basic concept machine learning concept has been combined. In this paper a comprehensive approach has been implemented for the machine learning based wearable devices which helps to monitor the human health like body temperature, ECZ, heart rate in real time. The main purpose is to execute and develop a system which is able to provide the effortless targeted outputs. As a main advantage of this paper is as an outcome there are several enhancements like almost exact and improved usability, accuracy, and responsiveness as compare to existing and conventional methods. Finally, it has a potential use for the personal as well as clinical point of view, which makes the point of research attraction and center of reputation. This research is recommended for smart cities as well as smart clinical operations.