Advertisement

Indoor Actions Classification Through Long Short Term Memory Neural Networks

  • Emanuele CipollaEmail author
  • Ignazio Infantino
  • Umberto Maniscalco
  • Giovanni Pilato
  • Filippo Vella
Conference paper
First Online:
  • 1.8k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10484)

Abstract

This work presents a system based on a recurrent deep neural network to classify actions performed in an indoor environment. RGBD and infrared sensors positioned in the rooms are used as data source. The smart environment the user lives in can be adapted to his/her needs.

Keywords

Deep learning Human actions LSTM Indoor activities 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Augello, A., Ortolani, M., Re, G.L., Gaglio, S.: Sensor mining for user behavior profiling in intelligent environments. In: Pallotta, V., Soro, A., Vargiu, E. (eds.) Advances in Distributed Agent-Based Retrieval Tools, pp. 143–158. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-21384-7_10 CrossRefGoogle Scholar
  2. 2.
    Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., Baskurt, A.: Sequential deep learning for human action recognition. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 29–39. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-25446-8_4 Google Scholar
  3. 3.
    Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)CrossRefGoogle Scholar
  4. 4.
    Castillo, J.C., Carneiro, D., Serrano-Cuerda, J., Novais, P., Fernández-Caballero, A., Neves, J.: A multi-modal approach for activity classification and fall detection. Int. J. Syst. Sci. 45(4), 810–824 (2014)CrossRefGoogle Scholar
  5. 5.
    Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: technologies, applications, and opportunities. Pervasive Mob. Comput. 5(4), 277–298 (2009)CrossRefGoogle Scholar
  6. 6.
    Donahue, J., Hendricks, L.A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description. CoRR abs/1411.4389 (2014). http://arxiv.org/abs/1411.4389
  7. 7.
    Filippo, V., Agnese, A., Umberto, M., Vincenzo, B., Salvatore, G.: Classification of indoor actions through deep neural networks. In: 2016 International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). IEEE (2016)Google Scholar
  8. 8.
    Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence, vol. 385. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-24797-2 zbMATHGoogle Scholar
  9. 9.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  10. 10.
    Krishnan, K., Prabhu, N., Babu, R.V.: ARRNET: action recognition through recurrent neural networks. In: 2016 International Conference on Signal Processing and Communications (SPCOM), pp. 1–5, June 2016Google Scholar
  11. 11.
    Krishnan, N.C., Cook, D.J.: Activity recognition on streaming sensor data. Pervasive Mob. Comput. Part B 10, 138–154 (2014)CrossRefGoogle Scholar
  12. 12.
    Kyriazakos, S., Mihaylov, M., Anggorojati, B., Mihovska, A., Craciunescu, R., Fratu, O., Prasad, R.: eWALL: an intelligent caring home environment offering personalized context-aware applications based on advanced sensing. Wirel. Pers. Commun. 87(3), 1093–1111 (2016)CrossRefGoogle Scholar
  13. 13.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  14. 14.
    Lima, W.S., Souto, E., Rocha, T., Pazzi, R.W., Pramudianto, F.: User activity recognition for energy saving in smart home environment. In: 2015 IEEE Symposium on Computers and Communication (ISCC), pp. 751–757. IEEE (2015)Google Scholar
  15. 15.
    Lowe, S.A., ÓLaighin, G.: Monitoring human health behaviour in one’s living environment: a technological review. Med. Eng. Phys. 36(2), 147–168 (2014)CrossRefGoogle Scholar
  16. 16.
    Pascanu, R., Gülçehre, Ç., Cho, K., Bengio, Y.: How to construct deep recurrent neural networks. CoRR abs/1312.6026 (2013). http://arxiv.org/abs/1312.6026
  17. 17.
    Remagnino, P., Foresti, G.L.: Ambient intelligence: a new multidisciplinary paradigm. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 35(1), 1–6 (2005)CrossRefGoogle Scholar
  18. 18.
    Twomey, N., Diethe, T., Kull, M., Song, H., Camplani, M., Hannuna, S., Fafoutis, X., Zhu, N., Woznowski, P., Flach, P., Craddock, I.: The SPHERE challenge: activity recognition with multimodal sensor data. arXiv preprint arXiv:1603.00797 (2016)
  19. 19.
    Maniscalco, U., Pilato, G., Vella, F.: Detection of indoor actions through probabilistic induction model. In: De Pietro, G., Gallo, L., Howlett, R.J., Jain, L.C. (eds.) KES-IIMSS 2017. SIST, vol. 76, pp. 129–138. Springer, Cham (2018). doi: 10.1007/978-3-319-59480-4_14 CrossRefGoogle Scholar
  20. 20.
    Vella, F., Infantino, I., Scardino, G.: Person identification through entropy oriented mean shift clustering of human gaze patterns. Multimedia Tools Appl. 76(2), 1–25 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Emanuele Cipolla
    • 1
    Email author
  • Ignazio Infantino
    • 1
  • Umberto Maniscalco
    • 1
  • Giovanni Pilato
    • 1
  • Filippo Vella
    • 1
  1. 1.ICAR, National Research Council of ItalyPalermoItaly

Personalised recommendations

Cite paper

Buy options