Logical Activity Recognition Model for Smart Home Environment

  • Choi, Jung-In (Dept. of Computer Science and Engineering, Ewha Woman's University) ;
  • Lim, Sung-Ju (Dept. of Computer Science and Engineering, Ewha Woman's University) ;
  • Yong, Hwan-Seung (Dept. of Computer Science and Engineering, Ewha Woman's University)
  • Received : 2015.05.07
  • Accepted : 2015.08.18
  • Published : 2015.09.30


Recently, studies that interact with human and things through motion recognition are increasing due to the expansion of IoT(Internet of Things). This paper proposed the system that recognizes the user's logical activity in home environment by attaching some sensors to various objects. We employ Arduino sensors and appreciate the logical activity by using the physical activitymodel that we processed in the previous researches. In this System, we can cognize the activities such as watching TV, listening music, talking, eating, cooking, sleeping and using computer. After we produce experimental data through setting virtual scenario, then the average result of recognition rate was 95% but depending on experiment sensor situation and physical activity errors the consequence could be changed. To provide the recognized results to user, we visualized diverse graphs.


Supported by : National Research Foundation of Korea(NRF)


  1. Jung-in C, Hwan-Seung Y. Activity data modeling and visualization method for human life activity recognition. Journal of Korea Multimedia 2012; 15(8): 1059-1066 (in Korean).
  2. H.K. Yang, H.S. Yong, "Real-Time Physical Activity Recognition Using Tri-axis Accelerometer of Smart Phone", Journal of Korea Multimedia, Vol. 17, No. 4, pp. 506-513, 2014. (in Korean)
  3. M. Blum, A. Pentland, and G. Troster, "InSense: Interest-based life logging," IEEE MultiMedia, Vol. 13, pp. 40-48, 2006.
  4. S. Reedy, M. Mun, J.Burke, D. Estrin, and M. Hansen, "Using mobile phones to determine transportation modes," ACM Transactions on Sensor Networks, Vol.6, No.2, Article 13, 2010.
  5. L. Stenneth, O. Wolfson, P. S. Yu, and B. Xu, "Transportation mode detection using mobile phones and GIS information," In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems(GIS 2011), pp. 54-63, ACM, 2011.
  6. S.S. Lee, H.S. Yong, "Logical Activity Recognition using Common Sense for Cause of Activity and Activity Related Object", Proc. of the KIISE Korea Computer Congress 2013, pp. 262-264, 2013. (in Korean)
  7. C.M. Jung, J.H. Bang, K.H. Han, H.N. Yeom, and S.Y. Lee, "Smartphone based activity recognition for real environment," Proc. of the KIISE Korea Computer Congress 2013, pp. 460-462, 2013. (in Korean)
  8. S. Amendola, R. Lodato, S. Manzari, C. Occhiuzzi and G. Marrocco, "RFID Technology for IoT-based Personal Healthcare in Smart Spaces," 2014.
  9. L. Chen, C. D. Nugent and H. Wang, "A knowledge-driven approach to activity recognition in smart homes," Knowledge and Data Engineering on IEEE Transactions, vol. 24, no. 6, pp. 961-974, 2014.
  10. M. Fahim, I. Fatima, S. Y. Lee, Y. K. Lee, "Daily life activity tracking application for smart homes using android smartphone," Advanced Communication Technology (ICACT), 2012 14th International Conference on IEEE, 2012.
  11. I. Duque, K. Dautenhahm, K. L. Koay, I. Willcock and B. Christianson, "Knowledge-driven user activity recognition for a Smart House. Development and validation of a generic and low-cost, resource-efficient system," ACHI 2013, The Sixth International Conference on Advances in Computer-Human Interactions, 2013.
  12. T. Gu, L. Wang, Z. Wu, X, Tao and J. Lu, "A pattern mining approach to sensor-based human activity recognition," Knowledge and Data Engineering on IEEE Transactions, vol. 23, no. 9, pp. 1359-1372, 2011.
  13. Arduino menual,, 2014.
  14. Seo Jin-su, "R Programming" Korean -Press, slow thinking, pp.21, 2014.