Leveraging Smart Sensors for Human Function Traceability

Patient health monitoring is a noticeable area of research in society. It helps to monitor the health of human beings regularly. However, a significant amount of work has been done toward health monitoring using sensors and the internet of things(IoT). This paper aims to leverage these intelligent sensing devices for tracking patient position day by day. Patient activity monitors even from their home using sensor devices. We use IMU sensors to monitor patient activity, such as sitting, standing, and sleeping. We use the LoRa protocol for communication purposes to get the date on the server. We apply different machine learning prediction models like SVM, Random Forest, Gaussian Naive Bayes, etc., to predict the position of a person (Sit, Stand, or Sleep) based on data provided by these smart sensors. This study will play a vital role in society by providing time and cost-effective solutions to healthcare problems and other industries. We finally evaluate and compare the accuracy of applying machine learning.