Where am I? Using Mobile Sensor Data to Predict a User’s Semantic Place with a Random Forest Algorithm

  • Elisabeth Lex
  • Oliver Pimas
  • Jörg Simon
  • Viktoria Pammer-Schindler
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 120)

Abstract

We use mobile sensor data to predict a mobile phone user’s semantic place, e.g. at home, at work, in a restaurant etc. Such information can be used to feed context-aware systems, that adapt for instance mobile phone settings like energy saving, connection to Internet, volume of ringtones etc. We consider the task of semantic place prediction as classification problem. In this paper we exploit five feature groups: (i) daily patterns, (ii) weekly patterns, (iii) WLAN information, (iv) battery charging state and (v) accelerometer data. We compare the performance of a Random Forest algorithm and two Support Vector Machines, one with an RBF kernel and one with a Pearson VII function based kernel, on a labelled dataset, and analyse the separate performances of the feature groups as well as promising combinations of feature groups. The winning combination of feature groups achieves an accuracy of 0.871 using a Random Forest algorithm on daily patterns and accelerometer data.

A detailed analysis reveals that daily patterns are the most discriminative feature group for the given semantic place labels. Combining daily patterns with WLAN information, battery charging state or accelerometer data further improves the performance. The classifiers using these selected combinations perform better than the classifiers using all feature groups. This is especially encouraging for mobile computing, as fewer features mean that less computational power is required for classification.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)CrossRefMATHGoogle Scholar
  2. 2.
    Caruana, R., Karampatziakis, N., Yessenalina, A.: An empirical evaluation of supervised learning in high dimensions. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 96–103. ACM (2008)Google Scholar
  3. 3.
    Chen, C., Chen, J., Barry, J.: Diurnal pattern of transit ridership: a case study of the new york city subway system. Journal of Transport Geography 17(3), 176–186 (2009)CrossRefGoogle Scholar
  4. 4.
    Grieco, M., Urry, J.: Mobilities: new perspectives on transport and society. Ashgate (January 2012)Google Scholar
  5. 5.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  6. 6.
    Hall, M.A.: Correlation-based Feature Subset Selection for Machine Learning. PhD thesis, University of Waikato, Hamilton, New Zealand (1998)Google Scholar
  7. 7.
    Huang, C.-M., Ying, J.J.-C., Tseng, V.S.: Mining Users Behaviors and Environments for Semantic Place Prediction. In: Proceedings of the Mobile Data Challenge by Nokia Workshop, Co-Located with Pervasive 2012 (2012)Google Scholar
  8. 8.
    Kim, B., Ha, J.-Y., Lee, S., Kang, S., Lee, Y., Rhee, Y., Nachman, L., Song, J.: Adnext: a visit-pattern-aware mobile advertising system for urban commercial complexes. In: Proceedings of the 12th Workshop on Mobile Computing Systems and Applications, HotMobile 2011, pp. 7–12. ACM, New York (2011)Google Scholar
  9. 9.
    Kiukkonen, N., Blom, J., Dousse, O., Gatica-Perez, D., Laurila, J.: Towards rich mobile phone datasets: Lausanne data collection campaign. In: Proceedings of the ACM International Conference on Pervasive Services, ICPS (2010)Google Scholar
  10. 10.
    Laurila, J.K., Gatica-Perez, D., Aad, I., Bornet, B.J.O., Do, T.-M.-T., Dousse, O., Eberle, J., Miettinen, M.: The mobile data challenge: Big data for mobile computing research. In: Pervasive Computing (2012)Google Scholar
  11. 11.
    Montoliu, R., Martnez-Uso, A., Martnez Sotoca, J.: Semantic place prediction by combining smart binary classifiers. In: Proceedings of the Mobile Data Challenge by Nokia Workshop, Co-Located with Pervasive 2012 (2012)Google Scholar
  12. 12.
    Oliver, E.: A survey of platforms for mobile networks research. SIGMOBILE Mob. Comput. Commun. Rev. 12(4), 56–63 (2009)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Pal, M.: Kernel methods in remote sensing: a review. ISH. J. Hydraulic Eng. (Special Issue), 194–215 (2009)Google Scholar
  14. 14.
    Saffari, A., Leistner, C., Santner, J., Godec, M., Bischof, H.: On-line random forests. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), September 27-October 4, pp. 1393–1400 (2009)Google Scholar
  15. 15.
    Üstün, B., Melssen, W.J., Buydens, L.M.C.: Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel. Chemometrics and Intelligent Laboratory Systems 81(1), 29–40 (2006)CrossRefGoogle Scholar
  16. 16.
    Wang, S., Chen, C., Ma, J.: Accelerometer Based Transportation Mode Recognition on Mobile Phones. In: Proceedings of the 2010 Asia-Pacific Conference on Wearable Computing Systems, APWCS 2010, pp. 44–46. IEEE Computer Society, Washington, DC (2010)CrossRefGoogle Scholar
  17. 17.
    Yang, J.: Toward physical activity diary: Motion recognition using simple acceleration features with mobile phones. Data Processing, 1–9 (2009)Google Scholar
  18. 18.
    Zhu, Y., Zhong, E., Lu, Z., Yang, Q.: Feature Engineering for Place Category Classification. In: Proceedings of the Mobile Data Challenge by Nokia Workshop, Co-Located with Pervasive 2012 (2012)Google Scholar

Copyright information

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

Authors and Affiliations

  • Elisabeth Lex
    • 1
  • Oliver Pimas
    • 1
  • Jörg Simon
    • 1
  • Viktoria Pammer-Schindler
    • 1
  1. 1.Know-CenterGrazAustria

Personalised recommendations