A mobile phone is getting smarter by employing a sensor and awareness of various contexts about a user and the terminal itself. In this paper, we deal with 9 storing positions of a smartphone on the body as a context of a device itself and a user: 1) around the neck (hanging), 2) chest pocket, 3) jacket pocket (side), 4) front pocket of trousers, 5) back pocket of trousers, 6) backpack, 7) handbag, 8) messenger bag, and 9) shoulder bag. We propose a method of recognizing the 9 positions by machine learning algorithms with 60 features that characterize specific movements of a terminal at the position during walking. The result of offline experiment showed that an overall accuracy was 74.6% in a strict condition of Leave-One-Subject-Out (LOSO) test, where a support vector machine (SVM) classifier was trained with dataset from other subjects.


Support Vector Machine Mobile Phone Window Size Activity Recognition Wearable Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Blanke, U., Schiele, B.: Sensing Location in the Pocket. In: Adjunct Proceedings of the 10th International Conference on Ubiquitous Computing (Ubicomp 2008), pp. 2–3 (September 2008)Google Scholar
  3. 3.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011)Google Scholar
  4. 4.
    Cho, S.-J., et al.: Two-stage Recognition of Raw Acceleration Signals for 3-D Gesture-Understanding Cell Phones. In: Proc. of the Tenth International Workshop on Frontiers in Handwriting Recognition (2006)Google Scholar
  5. 5.
    Cui, Y., Chipchase, J., Ichikawa, F.: A Cross Culture Study on Phone Carrying and Physical Personalization. In: Aykin, N. (ed.) HCII 2007. LNCS, vol. 4559, pp. 483–492. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Fujinami, K., Jin, C., Kouchi, S.: Tracking on-body location of a mobile phone. In: Proc. of the 14th Annual IEEE International Symposium on Wearable Computers, ISWC 2010, pp. 190–197 (2010)Google Scholar
  7. 7.
    Gellersen, H., Schmidt, A., Beigl, M.: Multi-Sensor Context-Awareness in Mobile Devices and Smart Artifacts. Journal on Mobile Networks and Applications (MONET) 7(5), 341–351 (2002)CrossRefzbMATHGoogle Scholar
  8. 8.
    Harrison, C., Hudson, S.E.: Lightweight material detection for placement-aware mobile computing. In: Proc. of the 21st Annual ACM Symposium on User Interface Software and Technology, UIST 2008, pp. 279–282. ACM, New York (2008)Google Scholar
  9. 9.
    Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)CrossRefGoogle Scholar
  10. 10.
    Kawahara, Y., Kurasawa, H., Morikawa, H.: Recognizing user context using mobile handsets with acceleration sensors. In: IEEE International Conference on Portable Information Devices (Portable 2007), pp. 1–5 (2007)Google Scholar
  11. 11.
    Kunze, K.S., Lukowicz, P., Junker, H., Tröster, G.: Where am I: Recognizing On-body Positions of Wearable Sensors. In: Strang, T., Linnhoff-Popien, C. (eds.) LoCA 2005. LNCS, vol. 3479, pp. 264–275. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. SIGKDD Explor. Newsl. 12(2), 74–82 (2011)CrossRefGoogle Scholar
  13. 13.
    Machine Learning Group at University of Waikato. Weka 3 - Data Mining with Open Source Machine Learning Software in Java,
  14. 14.
    Marsan, R.J.: Weka for Android,
  15. 15.
    Miluzzo, E., et al.: Pocket, Bag, Hand, etc.-Automatically Detecting Phone Context through Discovery. In: Proc. of the First International Workshop on Sensing for App. Phones, PhoneSense 2010 (2010)Google Scholar
  16. 16.
    Murao, K., Terada, T.: A motion recognition method by constancy-decision. In: Proceedings of the 14th International Symposium on Wearable Computers, ISWC 2010, pp. 69–72 (October 2010)Google Scholar
  17. 17.
    Okumura, F., et al.: A Study on Biometric Authentication based on Arm Sweep Action with Acceleration Sensor. In: Proc. of International Symposium on Intelligent Signal Processing and Communications (ISPACS 2006), pp. 219–222 (2006)Google Scholar
  18. 18.
    Pirttikangas, S., Fujinami, K., Nakajima, T.: Feature Selection and Activity Recognition from Wearable Sensors. In: Youn, H.Y., Kim, M., Morikawa, H. (eds.) UCS 2006. LNCS, vol. 4239, pp. 516–527. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  19. 19.
    Shi, Y., Shi, Y., Liu, J.: A Rotation based Method for Detecting On-body Positions of Mobile Devices. In: Proc. of the 13th International Conference on Ubiquitous Computing, UbiComp 2011, pp. 559–560. ACM (2011)Google Scholar
  20. 20.
    Stevens, M., D’Hondt, E.: Crowdsourcing of Pollution Data using Smartphones. In: 1st Ubiquitous Crowdsourcing Workshop at UbiComp (2010)Google Scholar
  21. 21.
    Sugimori, D., Iwamoto, T., Matsumoto, M.: A Study about Identification of Pedestrian by Using 3-Axis Accelerometer. In: Proc. of the 17th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2011), pp. 134–137 (2011)Google Scholar
  22. 22.
    Vahdatpour, A., et al.: On-body Device Localization for Health and Medical Monitoring Applications. In: Proc. of the 2011 IEEE International Conference on Pervasive Computing and Communications, PERCOM 2011, pp. 37–44. IEEE Computer Society (2011)Google Scholar
  23. 23.
    Xue, Y.: A Study on Reliable Environmental Sensing and Alerting by an On-Body Placement-Aware Device. Master’s thesis, Departent of Computer and Information Sciences, Tokyo University of Agriculture and Technology (2012) (in Japanese)Google Scholar
  24. 24.
    Xue, Y., et al.: A Trustworthy Heatstroke Risk Alert on a Smartphone. In: Adj. Proc. of the 10th Asia-Pacific Conference on Human-Computer Interaction (APCHI 2012), pp. 621–622 (August 2012)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2013

Authors and Affiliations

  • Kaori Fujinami
    • 1
  • Satoshi Kouchi
    • 1
  1. 1.Department of Computer and Information SciencesTokyo University of Agriculture and TechnologyTokyoJapan

Personalised recommendations