@article{SisLab1897,
          volume = {538},
          author = {Dang Nhac Lu and Thu Trang Nguyen and Thi Thu Trang Ngo and Thi Hau Nguyen and Ha Nam Nguyen},
       booktitle = {Advances in Information and Communication Technology},
           title = {Mobile Online Activity Recognition System Based on Smartphone Sensors},
         journal = {Advances in Information and Communication Technology},
             doi = {doi:10.1007/978-3-319-49073-1\_39},
           pages = {357--366},
            year = {2016},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/1897/},
        abstract = {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, Na{\"i}ve 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.}
}