Analysis of real-time heartbeat monitoring using wearable device Internet of Things system in sports environment

Technology in the field of Internet of Things (IoT) with smartphones is enormously growing at a rapid pace for assisting people with their health conditions. Wearable sensors can provide real time data in the field of sports for monitoring the heartbeat of the athletes which can assist in physical activities. Heartbeat rate of the players change during different positions while playing sports and heartbeat monitoring will help the players to know the health condition thus improving the health of an individual. In this research, we propose a new method of wearable sensor device for collecting real time data of athletes using IoT-based system for monitoring electrocardiogram (ECG) patterns along with acceleration of body using smart phone and classify the obtained data using Radial-basis Function Network and Levenberg-Marquardt with Probabilistic Neural Network. The experimental setup of the proposed model performed using 100 persons and effectively classifies the data and predicts the heart rate with the precision of validation and training sample being 73.58% and 73.45 respectively. Thus the proposed IoT-based prediction system can be used to monitor health data of the athletes in real time as an alternate solution for monitoring physical health of the athletes.