Optimizing Interval Training Protocols Using Data Mining Decision Trees

Abstract

Interval training consists of interleaving high intensity exercises with rest periods. This training method is a well known exercise protocol which helps strengthen and improve one’s cardiovascular fitness. However, there is no known method for formulating and tailoring an optimized interval training protocol for a specific individual which maximizes the amount of work done while limiting fatigue. But by using data mining schemes with various attributes, conditions, and data gathered from an individual’s exercise session, we are able to efficiently formulate an optimized interval training method for an individual. Recent advances in wireless wearable sensors and smart phones have made available a new generation of fitness monitoring systems. With accelerometers embedded in an iPhone, a Bluetooth pulse oximeter, and the Weka data mining tool, we are able to formulate the optimized interval training protocols, which can increase the amount of calorie burned up to 29.54%, compared with the modified Tabata interval training protocol.