Fine-Grained Activity Recognition of Pedestrians Travelling by Subway

  • Marco Maier
  • Florian Dorfmeister
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 130)

Abstract

With the now widespread usage of increasingly powerful smartphones, pro-active, context-aware, and thereby unobstrusive applications have become possible. A user’s current activity is a primary piece of contextual information, and especially in urban areas, a user’s current mode of transport is an important part of her activity. A lot of research has been conducted on automatically recognizing different means of transport, but up to know, no attempt has been made to perform a fine-grained classification of different activities related to travelling by local public transport.

In this work, we present an approach to recognize 17 different activities related to travelling by subway. We use only the sensor technology available in modern mobile phones and achieve a high classification accuracy of over 90%, without requiring a specific carrying position of the device. We discuss the usefulness of different sensors and computed features, and identify individual characteristics of the considered activities.

Keywords

Mode of Transport Recognition Mobile Phone Context Awareness Activity Recognition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 304–307. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  2. 2.
    Fujinami, K., Kouchi, S.: Recognizing a mobile phone’s storing position as a context of a device and a user. In: Zheng, K., Li, M., Jiang, H. (eds.) MobiQuitous 2012. LNICST, vol. 120, pp. 76–88. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning. PhD thesis, University of Waikato, Hamilton, New Zealand (1998)Google Scholar
  4. 4.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)CrossRefGoogle Scholar
  5. 5.
    Reddy, S., Burke, J., Estrin, D., Hansen, M., Srivastava, M.: Determining transportation mode on mobile phones. In: Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers, ISWC 2008, pp. 25–28. IEEE Computer Society, Washington, DC (2008)Google Scholar
  6. 6.
    Schilit, B.N., Theimer, M.M.: Disseminating active map information to mobile hosts. IEEE Network 8(5), 22–32 (1994)CrossRefGoogle Scholar
  7. 7.
    Stenneth, L., Wolfson, O., Yu, P.S., Xu, B.: 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, New York (2011)Google Scholar
  8. 8.
    Sun, L., Zhang, D., Li, B., Guo, B., Li, S.: Activity recognition on an accelerometer embedded mobile phone with varying positions and orientations. In: Yu, Z., Liscano, R., Chen, G., Zhang, D., Zhou, X. (eds.) UIC 2010. LNCS, vol. 6406, pp. 548–562. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Swan, M.: Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. International Journal of Environmental Research and Public Health 6(2), 492–525 (2009)CrossRefGoogle Scholar
  10. 10.
    Tapia, E.M., Intille, S.S., Haskell, W., Larson, K., Wright, J., King, A., Friedman, R.: Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In: 2007 11th IEEE International Symposium on Wearable Computers, pp. 37–40. IEEE (2007)Google Scholar
  11. 11.
    Wang, S., Chen, C., Ma, J.: Accelerometer based transportation mode recognition on mobile phones. In: 2010 Asia-Pacific Conference on Wearable Computing Systems (APWCS), pp. 44–46. IEEE (2010)Google Scholar
  12. 12.
    Yatani, K., Truong, K.N.: Bodyscope: a wearable acoustic sensor for activity recognition. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, UbiComp 2012, pp. 341–350. ACM, New York (2012)CrossRefGoogle Scholar
  13. 13.
    Zhang, M., Sawchuk, A.A.: A feature selection-based framework for human activity recognition using wearable multimodal sensors. In: Proceedings of the 6th International Conference on Body Area Networks, pp. 92–98. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (2011)Google Scholar
  14. 14.
    Zhang, Z., Poslad, S.: Fine-grained transportation mode recognition using mobile phones and foot force sensors. In: Zheng, K., Li, M., Jiang, H. (eds.) MobiQuitous 2012. LNICST, vol. 120, pp. 103–114. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2014

Authors and Affiliations

  • Marco Maier
    • 1
  • Florian Dorfmeister
    • 1
  1. 1.Mobile and Distributed Systems GroupLudwig-Maximilians-University MunichMunichGermany

Personalised recommendations