Advertisement

Recognition and Modeling of Atypical Children Behavior

  • Aleksandra PostawkaEmail author
  • Przemysław Śliwiński
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9119)

Abstract

According to reports from medical community the number of autistic children’s birth is more and more alarming. Early diagnosis and regular rehabilitation are crucial. The problem with verbal and emotional communication is very common. In a form of short survey, a few similar issues and their solutions have been examined in terms of input data type, feature selection, pattern recognition and formal mathematical modeling. Then we propose a system for autistic children rehabilitation, surveillance and emotions translation. These new solutions have been compared with those reported in the literature. The preliminary experiments provide rather satisfactory results.

Keywords

Markov model Hidden Markov Model (HMM) Action recognition Skeleton Kinect-type sensors 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ricks, D., Wing, L.: Language, Communication, and the Use of Symbols in Normal and Autistic Children. Journal of Autism and Childhood Schizophrenia 5, 191–221 (1975)CrossRefGoogle Scholar
  2. 2.
    Landowska, A., Kołakowska, A., Anzulewicz, A., Jarmołkowicz, P., Rewera, J.: E-technologies in diagnosis and progress measurement in autistic children therapy in Poland (in Polish). E-mentor 4 (2014)Google Scholar
  3. 3.
    Snoek, J., Hoey, J., Stewart, L., Zemel, R.: Automated Detection of Unusual Events on Stairs. Image and Vision Computing 27, 153–166 (2009)CrossRefGoogle Scholar
  4. 4.
    Yin, J., Yang, Q., Junfen Pan, J.: Sensor-Based Abnormal Human-Activity Detection. IEEE Transactions on Knowledge and Data Engineering 20, 1082–1090 (2008)CrossRefGoogle Scholar
  5. 5.
    Dubois, A., Charpillet, F.: Human Activities Recognition with RGB-Depth Camera using HMM. In: 35th Annual International Conference on the IEEE EMBS, pp. 4666–4669 (2013)Google Scholar
  6. 6.
    Jiang, M., Chen, Y., Zhao, Y., Cai, A.: A Real-Time Fall Detection System Based on HMM and RVM. In: Visual Communications and Image Processing, pp. 1–6 (2013)Google Scholar
  7. 7.
    Patsadu, O., Nukoolkit, C., Watanapa, B.: Human Gesture Recognition Using Kinect Camera. In: Ninth International Joint Conference on Computer Science and Software Engineering, pp. 28–32 (2012)Google Scholar
  8. 8.
    Jalal, A., Kamal, S., Kim, D.: A Depth Video Sensor-Based Life-Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments. Sensors 14, 11735–11759 (2014)CrossRefGoogle Scholar
  9. 9.
    Raptis, M., Kirovski, D., Hoppe, H.: Real-Time Classification of Dance Gestures from Skeleton Animation. In: Symposium on Computer Animation, pp. 147–156 (2011)Google Scholar
  10. 10.
    Yamato, J., Ohya, J., Ishii, K.: Recognizing Human Action in Time-Sequential Images using Hidden Markov Model. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 379–385 (1992)Google Scholar
  11. 11.
    Starner, T.: Visual Recognition of American Sign Language Using Hidden Markov Models. Massachusetts Institute of Technology (1995)Google Scholar
  12. 12.
    Liu, T., Song, Y., Gu, Y., Li, A.: Human Action Recognition Based on Depth Images from Microsoft Kinect. In: Fourth Global Congress on Intelligent Systems, pp. 200–204 (2013)Google Scholar
  13. 13.
    Lai, K., Konrad, J., Ishwar, P.: A gesture-driven computer interface using Kinect. In: IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 185–188 (2012)Google Scholar
  14. 14.
    Vemulapalli, R., Arrate, F., Chellappa, R.: Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 588–595 (2014)Google Scholar
  15. 15.
    Uddin, Z., Thang, D.N., Kim, T.-S.: Human Activity Recognition via 3-D Joint Angle Features and Hidden Markov Models. In: Proceedings of 2010 IEEE 17th International Conference on Image Processing, pp. 713–716 (2010)Google Scholar
  16. 16.
    Ji, X., Wang, C., Li, Y., Wu, Q.: Hidden Markov Model-based Human Action Recognition Using Mixed Features. Journal of Computational Information Systems, 3659–3666 (2013)Google Scholar
  17. 17.
    Forney, D.: The Viterbi Algorithm. Proceedings of the IEEE 61 (1973)Google Scholar
  18. 18.
    Kajastila, R., Hamalainen, P.: Augmented Climbing: Interacting With Projected Graphics on a Climbing Wall. In: CHI Extended Abstracts 2014, pp. 1279–1284 (2014)Google Scholar
  19. 19.
    Juditsky, A., Nemirovski, A.: Functional aggregation for nonparametric regression. Annals of Statistics 28(3), 681–712 (2000)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Wachel, P., Śliwiński, P.: Aggregative modelling of nonlinear systems. IEEE Signal Processing Letters 33(9), 1482–1486 (2015)CrossRefGoogle Scholar
  21. 21.
    Tsybakov, A.B.: Optimal rates of aggregation. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS (LNAI), vol. 2777, pp. 303–313. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of ElectronicsWroclaw University of TechnologyWroclawPoland

Personalised recommendations