Advertisement

Human Activity Recognition Using Smartphone Sensors

  • Marcin D. BugdolEmail author
  • Andrzej W. Mitas
  • Marcin Grzegorzek
  • Robert Meyer
  • Christoph Wilhelm
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 472)

Abstract

In the paper a human activity recognition system has been presented based on the data gathered with the smartphone sensors. The acceleration, magnetic field and sound have been registered and four different activities of daily living has been recognized i.e. riding a bike, driving in a car, walking and sitting. Two version of Support Vector Machine (SVM) classifier have been employed and the obtained results are promising.

Keywords

Human activity recognition Smartphones Support vector machine 

References

  1. 1.
    Aggarwal, J., Xia, L.: Human activity recognition from 3D data: a review. Pattern Recogn. Lett. 48, 70–80 (2014)CrossRefGoogle Scholar
  2. 2.
    Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.: Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: Bravo, J., Hervás, R., Rodríguez, M. (eds.) Ambient Assisted Living and Home Care. Lecture Notes in Computer Science, vol. 7657, pp. 216–223. Springer, Berlin (2012)CrossRefGoogle Scholar
  3. 3.
    Badura, P., Pietka, E., Franiel, S.: Acceleration trajectory analysis in remote gait monitoring. In: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp. 4615–4618 (2014)Google Scholar
  4. 4.
    Choujaa, D., Dulay, N.: Tracme: Temporal activity recognition using mobile phone data. In: IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, 2008. EUC ’08, vol. 1, pp. 119–126 (2008)Google Scholar
  5. 5.
    Costa, Â., Castillo, J.C., Novais, P., Fernández-Caballero, A., Simoes, R.: Sensor-driven agenda for intelligent home care of the elderly. Expert Syst. Appl. 39(15), 12192–12204 (2012)CrossRefGoogle Scholar
  6. 6.
    Guiry, J.J., van de Ven, P., Nelson, J., Warmerdam, L., Riper, H.: Activity recognition with smartphone support. Med. Eng. Phys. 36(6), 670–675 (2014)CrossRefGoogle Scholar
  7. 7.
    Jalal, A., Uddin, M., Kim, J., Kim, T.S.: Daily human activity recognition using depth silhouettes and R transformation for smart home. In: Abdulrazak, B., Giroux, S., Bouchard, B., Pigot, H., Mokhtari, M. (eds.) Toward Useful Services for Elderly and People with Disabilities. Lecture Notes in Computer Science, vol. 6719, pp. 25–32. Springer, Berlin (2011)CrossRefGoogle Scholar
  8. 8.
    Jatoba, L.C., Grossmann, U., Kunze, C., Ottenbacher, J., Stork, W.: Context-aware mobile health monitoring: Evaluation of different pattern recognition methods for classification of physical activity. In: Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pp. 5250–5253 (2008)Google Scholar
  9. 9.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. SIGKDD Explor. Newsl. 12(2), 74–82 (2011). MarchCrossRefGoogle Scholar
  10. 10.
    Kwon, Y., Kang, K., Bae, C.: Unsupervised learning for human activity recognition using smartphone sensors. Expert Syst. Appl. 41(14), 6067–6074 (2014)CrossRefGoogle Scholar
  11. 11.
    Lara, O., Labrador, M.: A survey on human activity recognition using wearable sensors. Commun. Surv. Tutorials IEEE 15(3) 1192–1209 (2013)Google Scholar
  12. 12.
    Mitas, A.W., Rudzki, M., Skotnicka, M., Lubina, P.: Activity monitoring of the elderly for telecare systems—review. In: Pietka, E., Kawa, J., Wieclawek, W. (eds.) Information Technologies in Biomedicine, Volume 4. Advances in Intelligent Systems and Computing, vol. 284, pp. 125–138. Springer (2014)Google Scholar
  13. 13.
    Mitas, A., Rudzki, M., Wieclawek, W., Zarychta, P., Piwowarski, S.: Wearable system for activity monitoring of the elderly. In: Pietka, E., Kawa, J., Wieclawek, W. (eds.) Information Technologies in Biomedicine, Volume 4. Advances in Intelligent Systems and Computing. Springer International Publishing, vol. 284, pp. 147–160 (2014)Google Scholar
  14. 14.
    Ni, B., Wang, G., Moulin, P.: Rgbd-hudaact: a color-depth video database for human daily activity recognition. 1147–1153 (2011)Google Scholar
  15. 15.
    Parkka, J., Ermes, M., Korpipaa, P., Mantyjarvi, J., Peltola, J., Korhonen, I.: Activity classification using realistic data from wearable sensors. IEEE Trans. Inf. Technol. Biomed. 10(1), 119–128 (2006). JanCrossRefGoogle Scholar
  16. 16.
    Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)Google Scholar
  17. 17.
    Urtasun, R., Fua, P.: 3d tracking for gait characterization and recognition. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings, pp. 17–22 (2004)Google Scholar
  18. 18.
    Varkey, J., Pompili, D., Walls, T.: Human motion recognition using a wireless sensor-based wearable system. Pers. Ubiquit. Comput. 16(7), 897–910 (2012)CrossRefGoogle Scholar
  19. 19.
    Wang, J., Liu, Z., Wu, Y., Yuan, J.: Mining actionlet ensemble for action recognition with depth cameras. 1290–1297 (2012)Google Scholar
  20. 20.
    Yang, C.C., Hsu, Y.L.: Remote monitoring and assessment of daily activities in the home environment. J. Clin. Gerontol. Geriatr. 3(3), 97–104 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Marcin D. Bugdol
    • 1
    Email author
  • Andrzej W. Mitas
    • 1
  • Marcin Grzegorzek
    • 2
  • Robert Meyer
    • 3
  • Christoph Wilhelm
    • 2
  1. 1.Faculty of Biomedical EngineeringSilesian University of TechnologyZabrzePoland
  2. 2.Pattern Recognition GroupUniversity of SiegenSiegenGermany
  3. 3.Neural Information Processing GroupTechnische Universität BerlinBerlinGermany

Personalised recommendations