Averaged Hidden Markov Models in Kinect-Based Rehabilitation System

  • Aleksandra PostawkaEmail author
  • Przemysław Śliwiński
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)


In this paper the Averaged Hidden Markov Models (AHMMs) are examined for the upper limb rehabilitation purposes. For the data acquisition the Microsoft Kinect 2.0 sensor is used. The system is intended for low-functioning autistic children whose rehabilitation is often based on sequences of images presenting the subsequent gestures. The number of such training sets is limited and the preparation of a new one is not available for everyone, whereas each child requires the individual therapy. The advantage of the presented system is that new activities models could be easily added.

The conducted experiments provide satisfactory results, especially in the case of single hand rehabilitation and both hands rehabilitation based on asymmetric gestures.


Autistic children Rehabilitation Hidden Markov Models Averaged Hidden Markov Models Microsoft Kinect 2.0 Depth sensor 



This work was supported by the statutory funds of the Faculty of Electronics 0401/0159/17, Wroclaw University of Science and Technology, Wroclaw, Poland.


  1. 1.
    Seach, D., Lloyd, M., Preston, M.: Supporting Children with Autism in Mainstreem Schools. The Questions Publishing Company Ltd., Birmingham (2003). ISBN 83-60215-17-0Google Scholar
  2. 2.
    Barry, A.: Some people think that every person with autism is like Rain Man, or a wizard at maths. Thejournal (2017).
  3. 3.
    Autism Awareness - Frequently Asked Questions About Autism. Staffordshire Adults Autistic Society.
  4. 4.
    Regenbrecht, H., Hoermann, S., McGregor, G., Dixon, B., Franz, E., Ott, C., Hale, L., Schubert, T., Hoermann, J.: Visual manipulations for motor rehabilitation. Comput. Graph. (Pergamon) 36(7), 819–834 (2012)CrossRefGoogle Scholar
  5. 5.
    Kuttuva, M., Boian, R., Merians, A., Burdea, G., Bouzit, M., Lewis, J., Fensterheim, D.: The rutgers arm, a rehabilitation system in virtual reality: a pilot study. CyberPsychol. Behav. 9(2), 148–152 (2006)CrossRefGoogle Scholar
  6. 6.
    Pastor, I., Hayes, H.A., Bamberg, S.J.M.: A feasibility study of an upper limb rehabilitation system using Kinect and computer games. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1286–1289 (2012)Google Scholar
  7. 7.
    Chee, K.L., Chen, I.M., Zhiqiang, L., Yeo, S.H.: A low cost wearable wireless sensing system for upper limb home rehabilitation. In: 2010 IEEE Conference on Robotics, Automation and Mechatronics, pp. 1–8 (2010)Google Scholar
  8. 8.
    Clark, R.A., Pua, Y.H., Fortin, K., Ritchie, C., Webster, K.E., Denehy, L., Bryant, A.L.: Validity of the Microsoft Kinect for assessment of postural control. Gait Posture 36(3), 372–377 (2012)CrossRefGoogle Scholar
  9. 9.
    Scherer, M., Unterbrunner, A., Riess, B., Kafka, P.: Development of a system for supervised training at home with Kinect V2. Procedia Eng. 147, 466–471 (2016)CrossRefGoogle Scholar
  10. 10.
    Chang, Y.J., Chen, S.F., Huang, J.D.: A Kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities. Res. Dev. Disabil. 32(6), 2566–2570 (2011)CrossRefGoogle Scholar
  11. 11.
    Kusaka, J., Obo, T., Botzheim, J., Kubota, N.: Joint angle estimation system for rehabilitation evaluation support. In: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1456–1462 (2014)Google Scholar
  12. 12.
    Su, Ch.J., Chiang, Ch.Y., Huang, J.Y.: Kinect-enabled home-based rehabilitation system using dynamic time warping and fuzzy logic. Appl. Soft Comput. 22, 652–666 (2014). Elsevier B.VCrossRefGoogle Scholar
  13. 13.
    González-Ortega, D., Díaz-Pernas, F.J., Martínez-Zarzuela, M., Antón-Rodríguez, M.: A Kinect-based system for cognitive rehabilitation exercises monitoring. Comput. Methods Programs Biomed. 113, 620–631 (2014)CrossRefGoogle Scholar
  14. 14.
    Rabiner, L., Juang, B.: An introduction to hidden Markov models. IEEE ASSP Mag. 3, 4–16 (1986)CrossRefGoogle Scholar
  15. 15.
    Postawka, A.: Exercise recognition using averaged hidden Markov models. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017 Part II. LNCS (LNAI), vol. 10246, pp. 137–147. Springer, Cham (2017). Scholar
  16. 16.
    Postawka, A.: Real-time monitoring system for potentially dangerous activities detection. In: Proceedings of the 22nd International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 1005–1008. IEEE Xplore Digital Library (2017)Google Scholar
  17. 17.
    Postawka, A., Śliwiński, P.: A Kinect-based support system for children with autism spectrum disorder. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016 Part II. LNCS (LNAI), vol. 9693, pp. 189–199. Springer, Cham (2016). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of ElectronicsWroclaw University of Science and TechnologyWrocławPoland

Personalised recommendations