Continuous Athlete Monitoring Wearable Device Using Machine Learning

Abstract

Nowadays, wearable techniques are widely used in machine learning. Among the various application, IoT based machine learning devices are used widely in health care application for reducing the risk factor. So, this project introduced a sensor-based wearable device for sportspersons' continuous health monitoring system. The goal of the project is to assists each athlete's health and to reduce the coach’s work using machine learning. Firstly, the dataset will be collected from the previous athletes who wore this device. The device will be trained with the dataset by pre-processing feature selection data. The main use of this device is to suggest an athlete run fast/slow based on surroundings. And also assists athlete in what to eat and how to train. It will monitor the athlete’s health condition and compare data with the dataset with the use of the Machine Learning Algorithm J48. J48 algorithm creates a decision tree for machine learning is applied to obtain the athlete’s health condition. The accuracy level of the J48 algorithm is 97.73%.