%0 Journal Article
%@ 2194-5357
%A Lu, Dang Nhac
%A Nguyen, Thu Trang
%A Ngo, Thi Thu Trang
%A Nguyen, Thi Hau
%A Nguyen, Ha Nam
%D 2016
%F SisLab:1897
%J Advances in Information and Communication Technology
%P 357-366
%T Mobile Online Activity Recognition System Based on Smartphone Sensors
%U https://eprints.uet.vnu.edu.vn/eprints/id/eprint/1897/
%V 538
%X In this paper, we propose an efficient and flexible framework for activity recognition based on smartphone sensors. We develop a mobile application that integrates data collection, training and recognition, feedback monitoring. This system allows user smartphones are randomly placed in any position and at any direction. In the proposed framework, a set of power based and frequency-based features is extracted from sensor data. Then, we deploy Random Forest, Naïve Bayes, K-Nearest Neighbor (KNN), Support Vector Ma-chine (SVM) classification algorithms for recognizing a set of user activities. Our framework dynamically takes into account real-time user feedbacks to increase the prediction accuracy. Our framework will be able to apply for intelligent mo-bile applications. A number of experiments were carried out to show the high ac-curacy of the proposed framework for detecting user activities when walking or driving a motorbike.