@article{2204,
  author = {Nhac Lu Dang},
  title = {Mobile Online Activity Recognition System­ Based on Smartphone Sensors},
  journal = {Journal of Intelligent Computing},
  year = {2017},
  volume = {8},
  number = {1},
  doi = {},
  url = {http://www.dline.info/jic/fulltext/v8n1/jicv8n1_3.pdf},
  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, Fast Fourier Transform (FFT) is used to extract a set of features from sensor data. Then, we deploy Random Forest, Naïve Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) classification algorithms for recognizing a set of user activities. Our framework dynamically takes into account real-time user feedbacks to increase prediction accuracy. Our framework will be able to apply for intelligent mobile applications. A number of experiments were carried out to show the high accuracy of the proposed framework for detecting user activity when walking or driving a motorbike.
},
}