Tracking individuals’ health using mobile applications and Machine Learning

The widespread availability of smartphones and their high processing power have made them powerful mobile tools able to host and run various apps. In addition, wearable devices with low cost and accurate sensors gathering various physiological data and information are now available. Meanwhile, automated activity recognition is a rapidly evolving research area directly related to the mobile Health (mHealth) field. Rapid advancements in the Human Activity Recognition (HAR) field are mainly based on combining smartphones and wearable devices to succeed in advancing health tracking. This paper presents a mobile app designed and developed for monitoring changes in variables related to the physiological health status of an individual when he is moving around. The app tracks the physiological status of a human along with machine learning algorithms able to recognize and identify human activity and produce automatic alerts warning of dangerous health situations.