Smartphone-Based Human Activity Recognition Using Bagging and Boosting

In today's healthcare applications, the use of mobile technologies brings together physicians and patients for intelligent and automatic monitoring of daily clinical activities, remote life assistants, and preventive care, especially for the elderly and those under medical control. As smartphones become an important part of our everyday life, they are ever more employed in human activities recognition (HAR) including the monitoring of personal health care and wellbeing. However, HAR is complex and it is important to use the best technology and learn about human activity using machine learning. The purpose of this paper is to develop a HAR system based on the smartphone sensors' data using Bagging and Adaboost ensemble classifiers. The experimental results for the HAR data have been evaluated after performing different data mining techniques. For each subject, the total classification accuracy, the F-measure, and the ROC area were calculated. Adaboost ensemble classifiers algorithm improved significantly the performance of smartphone-based HAR, combined with SVM, it reached 97.44% accuracy compared to the rest of the classifiers. The proposed algorithm of Adaboost SVM can lead to an accurate HAR for elderly and disabled patients who need continuous care as well as it is a tool that supports the decisions of all medical practitioners.