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Conference on Innovations in Biomedical Engineering

IBE 2017: Innovations in Biomedical Engineering pp 185–192Cite as

The Face Tracking System for Rehabilitation Robotics Applications

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 623 )

Abstract

The paper presents the working model of the face tracking system. The proposed solution may be used as one of the parts of the rehabilitation or assistive robotic system and serve as the robotic vision subsystem or as the module controlling robotic arm. It is a low-cost design, it is based on open source hardware and software components. As a hardware base the Raspberry Pi computer was used. The machine vision software is based on Python programming language and OpenCV computer vision library.

Keywords

  • machine vision
  • rehabilitation robotics
  • assistive robotics
  • human computer interaction
  • face recognition
  • object tracking
  • OpenCV
  • Raspberry Pi

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  • DOI: 10.1007/978-3-319-70063-2_20
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References

  1. H. Zhou, H. Hu: Human motion tracking for rehabilitation - A survey, Biomedical Signal Processing and Control, Volume 3, Issue 1, pp. 1âĂŞ18, January 2008.

    Google Scholar 

  2. C. Rougier, J. Meunier, A. St-Arnaud, J. Rousseau: Monocular 3D Head Tracking to Detect Falls of Elderly People. Proceedings of the 28th IEEE EMBS Annual International Conference New York City, USA, Aug 30-Sept 3, 2006.

    Google Scholar 

  3. L. Boccanfuso, J. M. O’Kane: Adaptive Robot Design with Hand and Face Tracking for Use in Autism Therapy. Social Robotics, Second International Conference on Social Robotics, ICSR 2010, Singapore, November 23-24, 2010, pp. 265–274, DOI: 10.1007/978-3-642-17248-9_28.

  4. T. Fong, I. Nourbakhsh, K. Dautenhahn: A survey of socially interactive robots. Robotics and Autonomous Systems, Volume 42, Issues 3âĂŞ4, pp. 143âĂŞ166, March 2003.

    Google Scholar 

  5. P. Jia, H. H. Hu, T. Lu, K. Yuan: Head gesture recognition for hands free control of an intelligent wheelchair. Industrial Robot: An International Journal, Vol. 34 Iss: 1, pp.60 âĂŞ 68, 2007.

    Google Scholar 

  6. P. Raif, J.A. Starzyk: Motivated learning in autonomous systems. The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 603–610, 2011.

    Google Scholar 

  7. J.A. Starzyk, J.T. Graham, P. Raif, A.H. Tan: Motivated Learning for Autonomous Robots Development. Cognitive Science Research, 14, 1, 2011.

    Google Scholar 

  8. RaspberryPi, https://www.raspberrypi.org/

  9. Raspbian, http://www.raspbian.org/

  10. Python, http://www.python.org/

  11. J.E. Solem: Programming Computer Vision with Python: Tools and algorithms for analyzing images. O’Reilly Media 2012.

    Google Scholar 

  12. OpenCV, http://opencv.org/

  13. SimpleCV, http://simplecv.sourceforge.net/, http://simplecv.org/

  14. K. Demaagd, A. Oliver, N. Oostendorp, K. Scott: Practical Computer Vision with SimpleCV: The Simple Way to Make Technology See. O’Reilly Media 2012.

    Google Scholar 

  15. NumPy, http://numpy.scipy.org/

  16. SciPy, http://www.scipy.org/

  17. Pygame, http://pygame.org/

  18. ServoBlaster, https://github.com/richardghirst/PiBits/tree/master/ServoBlaster

  19. J. Howse: OpenCV Computer Vision with Python. CreateSpace Independent Publishing Platform 2015.

    Google Scholar 

  20. G. Bradski, A. Kaehler: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media 2008.

    Google Scholar 

  21. P. Viola, M. Jones: Rapid object detection using a boosted cascade of simple features. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. vol. 1, pp. 511–518, 2001.

    Google Scholar 

  22. P. Viola, M. Jones: Robust Real-time Object Detection. International Journal of Computer Vision 57(2), pp. 137–154, 2004.

    Google Scholar 

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Author information

Affiliations

  1. Department of Biosensors and Processing of Biomedical Signals, Biomedical Engineering Faculty at the Silesian University of Technology, Zabrze, Poland

    Paweł Raif & Ewaryst Tkacz

Authors
  1. Paweł Raif
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  2. Ewaryst Tkacz
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Raif, P., Tkacz, E. (2018). The Face Tracking System for Rehabilitation Robotics Applications. In: Gzik, M., Tkacz, E., Paszenda, Z., Piętka, E. (eds) Innovations in Biomedical Engineering . IBE 2017. Advances in Intelligent Systems and Computing, vol 623 . Springer, Cham. https://doi.org/10.1007/978-3-319-70063-2_20

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  • DOI: https://doi.org/10.1007/978-3-319-70063-2_20

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  • Publisher Name: Springer, Cham

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