Have a personal or library account? Click to login
Development of Motion Detection Algorithms Based on Simultaneous Execution Using Mobile Phone Sensors Cover

Development of Motion Detection Algorithms Based on Simultaneous Execution Using Mobile Phone Sensors

By: Zsófia Sándor and  Gergely Kis  
Open Access
|Sep 2017

References

  1. [1] Freeman, C., Louca, F., “The Emergence of a New Techno-Economic Paradigm: The Age of Information and Communication Technology (ICT)”, As Time Goes By – From the Industrial Revolutions to the Information Revolution, New York, 2002, pp. 318–324.10.1093/0199251053.003.0009
  2. [2] Naqvi, N. Z., Kumar, A., Chauhan, A., Sahni, K., “Step Counting Using Smartphone-Based Accelerometer”, International Journal on Computer Science and Engineering, 4(3), pp. 675–681, May. 2012.
  3. [3] Jayalath, S., Abhayasinghe, N., Murray, I., “A Gyroscope Based Accurate Pedometer Algorithm”, Presented at International Conference on Indoor Positioning and Indoor Navigation, Oct. 2013.
  4. [4] Cruz-Silva, N., Mendes-Moreira, J., Menezes, P., “Features Selection for Human Activity Recognition with iPhone Inertial Sensors”, Presented at Portuguese Conference on Artificial Intelligence, 2013.
  5. [5] Das, S., Green, L., Perez, B., Murphy, M., “Detecting User Activities using the Accelerometer on Android Smartphones”, Jul. 2010.
  6. [6] Cerqueira da Silva, J. R., “Smartphone Based Human Activity Prediction”, M.S. thesis, Faculdade de Engenhariau., Universidade do Porto., 2013.
  7. [7] Siirtola, P., Röning, J., “Recognizing Human Activities User independently on Smartphones Based on Accelerometer Data”, International Journal of Interactive Multimedia and Artificial Intelligence, 1(5), pp. 38–45, Jun 2012.10.9781/ijimai.2012.155
  8. [8] Khan, A. M., Lee, Y.-K., Lee, S. Y., Kim, T.-S., “Human Activity Recognition via An Accelerometer-Enabled-Smartphone Using Kernel Discriminant Analysis”. Presented at Future Information Technology, 2010 5th International Conference, May. 2010.10.1109/FUTURETECH.2010.5482729
  9. [9] Tomlein, M., Bielik, P., Krátky, P., Mitrík, S., Barla, M., Bieliková, M., “Advanced Pedometer for Smartphone-based Activity Tracking”, Presented at Proceedings of the International Conference on Health Informatics, 2012.
  10. [10] Shin, J., Shin, D., Shin, D., Her, S., Kim, S., Lee, M., “Human Movement Detection Algorithm Using 3-Axis Accelerometer Sensor Based on Low-Power Management Scheme for Mobile Health Care System”, Presented at GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing, May. 2010.10.1007/978-3-642-13067-0_12
  11. [11] Kwapisz, J. R., Weiss, G. M., Moore, S. A., “Activity Recognition using Cell Phone Accelerometers”, ACM SIGKDD Explorations Newsletter, Dec. 2010.10.1145/1964897.1964918
  12. [12] Dernbach, S., Das, B., Krishnan, N. C., “Simple and Complex Activity Recognition Through Smart Phones”, Presented at Intelligent Environments (IE), 2012 8th International Conference, Jun. 2012.10.1109/IE.2012.39
  13. [13] Yang, C.C., Hsu, Y.L., “Development of a wearable motion detector for telemonitoring and real-time identification of physical activity”, Telemed. J. E. Health, no. 15, 2009, pp. 62–72.10.1089/tmj.2008.006019199849
  14. [14] Karantonis, D.M., Narayanan, M.R., Mathie, M., Lovell, N.H., Celler, B.G., “Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring”, IEEE. Trans. Inf. Technol. Biomed. no. 10, 2006, pp. 156–167.10.1109/TITB.2005.85686416445260
  15. [15] Ozdemir, A. T. and Barshan, B., “Detecting Falls with Wearable Sensors Using Machine Learning Techniques” Sensors, vol. 14, MDPI, pp. 10691–10708, 2014.
Language: English
Page range: 29 - 41
Submitted on: Feb 15, 2016
|
Published on: Sep 9, 2017
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2017 Zsófia Sándor, Gergely Kis, published by Sapientia Hungarian University of Transylvania
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.