Human motion classification based on inertial sensors with extreme gradient boosting

In this paper, we apply Extreme Gradient Boosting (XGBoost) widely used in many areas to human motion classification. During this research, we compare the performance of XGBoost and other machine learning methods, such as Support Vector Machine (SVM), Naive Bayes (NB), k-Nearest Neighbors (k-NN). In addition, we make a comprehensive comparison of XGBoost and Random Forest (RF). The experimental results reveal that XGBoost can achieve better results in activity classification based on inertial sensors.