CCMS: A calorie consumption monitoring system for exercising with least-squares calibration

Nowadays, with increasing work stress and quick pace of modern life, people generally do not have enough time for exercising, however, they curiously pay much attention to the direct effect of exercising - calorie consumption. In this paper, we investigate several popular calorie consumption monitoring approaches and propose CCMS - a novel Calorie Consumption Monitoring System for exercising with least-squares calibration based on smartphones. Specifically, CCMS uses smartphone built-in sensors to collect the sensed data from accelerometer, barometer and GPS. With the sensed data, CCMS computes the calorie consumption based on the energy consumption formulas of American College of Sports Medicine. We apply an improved Naive Bayesian Classifier, which enables intelligent classification of several exercise types and achieves an average accuracy of 92.6% for determining the exercise types. We adopt the leastsquares method to calibrate the result of calorie consumption and find that the method can increase the precision of CCMS up to 6%. We evaluate the performance of CCMS against other popular fitness applications, including Gudong and Jawbone UP3 which is a commercial device. The experimental results demonstrate that CCMS outperforms state-of-the-art calorie consumption monitoring systems in terms of measuring accuracy, with the average accuracy increase of about 5%.