IoT inspired smart environment for personal healthcare in gym

Abstract

The Internet of Things (IoT) has the ability to collect health-related data from surroundings. As a result, the Cloud Centric IoT (CCIoT) Technology is used in this paper to measure a trainee’s health-related traits during fitness time in a gym. The proposed system can forecast a trainee’s probabilistic sensitivity to health status during workouts. Back-propagation based Artificial Neural Network (ANN) methodology is used as a prediction model for this purpose, and it is divided into 3 phases: Observation, Learning, and Prediction. In addition, the trainee’s health status is depicted in real-time using a colour scheme strategy that depicts the probabilistic vulnerability. The presented framework was tested by a 6 day trial in which five individuals were supervised at various gymnasiums. For assessing the general efficacy of the proposed framework, the outcomes are compared to various state-of-the-art approaches in terms of prediction efficiency, temporal prediction, and stability.