Advertisement

Human Activity Recognition on Smartphones with Awareness of Basic Activities and Postural Transitions

  • Jorge-Luis Reyes-Ortiz
  • Luca Oneto
  • Alessandro Ghio
  • Albert Samá
  • Davide Anguita
  • Xavier Parra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)

Abstract

Postural Transitions (PTs) are transitory movements that describe the change of state from one static posture to another. In several Human Activity Recognition (HAR) systems, these transitions cannot be disregarded due to their noticeable incidence with respect to the duration of other Basic Activities (BAs). In this work, we propose an online smartphone-based HAR system which deals with the occurrence of postural transitions. If treated properly, the system accuracy improves by avoiding fluctuations in the classifier. The method consists of concurrently exploiting Support Vector Machines (SVMs) and temporal filters of activity probability estimations within a limited time window. We present the benefits of this approach through experiments over a HAR dataset which has been updated with PTs and made publicly available. We also show the new approach performs better than a previous baseline system, where PTs were not taken into account.

Keywords

Human Activity Recognition Smartphones Postural Transitions Support Vector Machines Temporal Filtering 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: European Symposium on Artificial Neural Networks (2013)Google Scholar
  2. 2.
    Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic. Journal of Universal Computer Science 19, 1295–1314 (2013)Google Scholar
  3. 3.
    Bache, K., Lichman, M.: UCI machine learning repository (2013), http://archive.ics.uci.edu/ml
  4. 4.
    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
  5. 5.
    Dernbach, S., Das, B., Krishnan, N., Thomas, B., Cook, D.: Simple and complex activity recognition through smart phones. In: International Conference on Intelligent Environments (2012)Google Scholar
  6. 6.
    Hsu, C.-W., Lin, C.-J.: A comparison of methods for multiclass support vector machines. EEE Transactions on Neural Networks 13, 415–425 (2002)Google Scholar
  7. 7.
    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 Transactions on Information Technology in Biomedicine 10, 156–167 (2006)CrossRefGoogle Scholar
  8. 8.
    Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K.: Improvements to platt’s smo algorithm for svm classifier design. Neural Computation 13, 637–649 (2001)CrossRefzbMATHGoogle Scholar
  9. 9.
    Lara, O., Labrador, M.: A survey on human activity recognition using wearable sensors. IEEE Communications Surveys Tutorials 1, 1–18 (2012)Google Scholar
  10. 10.
    Najafi, B., Aminian, K., Loew, F., Blanc, Y., Robert, P.A.: Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly. IEEE Transactions on Biomedical Engineering 49, 843–851 (2002)CrossRefGoogle Scholar
  11. 11.
    Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers (1999)Google Scholar
  12. 12.
    Rifkin, R., Klautau, A.: In defense of one-vs-all classification. Journal of Machine Learning Research 5, 101–141 (2004)zbMATHMathSciNetGoogle Scholar
  13. 13.
    Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., Förster, K., Tröster, G., Lukowicz, P., Bannach, D., Pirkl, G., Ferscha, A.: Collecting complex activity data sets in highly rich networked sensor environments. In: International Conference on Networked Sensing Systems 2010 (2010)Google Scholar
  14. 14.
    Tapia, E.M., Intille, S.S., Lopez, L., Larson, K.: The design of a portable kit of wireless sensors for naturalistic data collection. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 117–134. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jorge-Luis Reyes-Ortiz
    • 1
    • 2
  • Luca Oneto
    • 1
  • Alessandro Ghio
    • 1
  • Albert Samá
    • 2
  • Davide Anguita
    • 1
  • Xavier Parra
    • 2
  1. 1.DITENUniversity of GenoaGenovaItaly
  2. 2.CETpDUniversitat Politècnica de CatalunyaSpain

Personalised recommendations