International Journal of Advanced Culture Technology

  • 제4권1호
  • /
  • Pages.28-30
  • /
  • 2016
  • /
  • 2288-7202(pISSN)
  • /
  • 2288-7318(eISSN)
국제문화기술진흥원 (The International Promotion Agency of Culture Technology)
권호 표지
DOI QR코드

DOI QR Code

Detecting user status from smartphone sensor data

  • Nguyen, Thu-Trang (University of Engineering and Technology, Vietnam National University in Hanoi) ;
  • Nguyen, Thi-Hau (University of Engineering and Technology, Vietnam National University in Hanoi) ;
  • Nguyen, Ha-Nam (University of Engineering and Technology, Vietnam National University in Hanoi) ;
  • Nguyen, Duc-Nhan (Posts and Telecommunications Institute of Technology) ;
  • Choi, GyooSeok (Chungwoon University)
  • 투고 : 2016.01.17
  • 심사 : 2016.02.27
  • 발행 : 2016.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Due to the high increment in usage and built-in advanced technology of smartphones, human activity recognition relying on smartphone sensor data has become a focused research area. In order to reduce noise of collected data, most of previous studies assume that smartphones are fixed at certain positions. This strategy is impractical for real life applications. To overcome this issue, we here investigate a framework that allows detecting the status of a traveller as idle or moving regardless the position and the direction of smartphones. The application of our work is to estimate the total energy consumption of a traveller during a trip. A number of experiments have been carried out to show the effectiveness of our framework when travellers are not only walking but also using primitive vehicles like motorbikes.

키워드

참고문헌

  1. Bao, L. and Intille, S. 2004. Activity Recognition from User- Annotated Acceleration Data. Lecture Notes Computer Science 3001, 1-17.
  2. W. Premerlani and P. Bizard, Direction cosine matrix imu: Theory, Technical Report, May 17th 2009.
  3. Mridul Khan, Sheikh I. Ahamed, Miftahur Rahman, Roger O. Smith, A Feature Extraction Method for Real time Human Activity Recognition on Cell Phones, RESNA_ICTA Conference, Toronto, ON, Canada, June 5-8, 2011.
  4. C. W. Han, S. J. Kang and N. S. Kim, "Implementation of HMM-Based Human Activity Recognition Using Single Triaxial Accelerometer," IEICE Transactions Fundamentals, Vol. E93-A, No. 7, July 2010.
  5. S. J. Preece, J. Y. Goulermas, L. P. J. Kenney and D. Howard, "A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities from Accelerometer Data," IEEE Transactions on Biomedical Engineering, Vol. 56, No. 3, March 2009.
  6. Michael J.A. Berry and Gordon S. Linoff, Data Mining Techniques, 2nd ed., Wiley Publishing Inc., USA, 2004.