Methodology for a Low-Cost Vision-Based Rehabilitation System for Stroke Patients

  • Arpita Ray SarkarEmail author
  • Goutam Sanyal
  • Somajyoti Majumder
Conference paper
Part of the Lecture Notes in Bioengineering book series (LNBE)


Stroke is a life threatening phenomenon throughout the world caused due to the blockage (by clots) or bursting of arteries. As a result permanent or semi-permanent neurological damage may occur that requires proper rehabilitation to overcome the deficiency of communication or communication disorder of the stroke patient with the outer world. This causes delay in overall recovery and affects the general hygiene of the patient. Computer vision based interaction using gazes may be helpful for such cases. In all those methodologies as a mandatory step eye tracking has to be performed. Present work uses a low cost web camera for eye tracking using Haar feature-based cascade function in comparison with the costlier eye tracking systems available in the market. This method easily detects the eye balls from the video online with less computational load. Several experiments have been carried out to evaluate the performance in different background, lighting conditions and quality of images.


Computer vision Object detection Face recognition Patient rehabilitation 



Authors are grateful to the Head and other faculty members of Department of CSE, NIT-Durgapur and SR Lab, CSIR—CMERI Durgapur for their continuous help and tired less support. Authors also thank Dr. D.N. Ray for his continuous suggestions and advices, without which this work would not have completed.


  1. 1.
    Taylor FC, Suresh Kumar K (2012) Stroke in India fact sheet (updated 2012)Google Scholar
  2. 2.
    National Stroke Association (2013) Explaining stroke. Accessed 19 July 2013
  3. 3.
    Dalal P, Bhattacharjee M, Vairale J, Bhat P (2007) UN millennium development goals: can we halt the stroke epidemic in India? Ann Indian Acad Neurol 10(3):130–136CrossRefGoogle Scholar
  4. 4.
    Jack D, Boian R, Merians AS, Tremaine M, Burdea GC, Adamovich SV, Recce M, Poizner H (2001) Virtual reality-enhanced stroke rehabilitation. IEEE Trans Neural Syst Rehabil Eng 9(3):308–318CrossRefGoogle Scholar
  5. 5.
    Ushaw G, Ziogas E, Eyre J, Morgan G (2013) An efficient application of gesture recognition from a 2D camera for rehabilitation of patients with impaired dexterity. School of Computing Science Technical Report Series. Accessed 22 Jan 2013
  6. 6.
    Sucar L, Luis R, Leder R, Hernandez J, Sanchez I (2010) Gesture therapy: a vision-based system for upper extremity stroke rehabilitation. In: Proceedings of IEEE engineering medical biology society, 2010, pp 107–111Google Scholar
  7. 7.
    Reinkensmeyer DJ, Pang CT, Nessler JA, Painter CC (2002) Web-based tele rehabilitation for the upper extremity after stroke. IEEE Trans Neural Syst Rehabil Eng 10(2):102–108CrossRefGoogle Scholar
  8. 8.
    Fasoli SE, Krebs HI, Stein J, Frontera WR, Hogan N (2003) Effects of robotic therapy on motor impairment and recovery in chronic stroke. Arch Phys Med Rehabil 84:477–482CrossRefGoogle Scholar
  9. 9.
    Huq R, Wang R, Lu E, Lacheray H, Mihailidis A (2013) Development of a fuzzy logic based intelligent system for autonomous guidance of poststroke rehabilitation exercise. In: Proceedings of 13th international conference on rehabilitation robotics, 24–26 June 2013, WAGoogle Scholar
  10. 10.
    Huq R, Lu E, Wang R, Mihailidis A (2012) Development of a portable robot and graphical user interface for haptic rehabilitation exercise. In: Proceedings of 4th IEEE/RAS-EMBS international conference on biomedical robotics and biomechatronics, June 2012, ItalyGoogle Scholar
  11. 11.
    Block M, Mercado L (2013) Talking tech: technology expands communication opportunities for people with aphasia, everyday survival. Springer, Heidelberg, pp 18–19Google Scholar
  12. 12.
    Roke Manor Research Ltd (2013) Microsoft kinect gesture recognition software for stroke patients, Inside Technology, Issue 9. Accessed 07 June 2013
  13. 13.
    Arrington Research (2013) Eye tracker prices. Accessed 20 Dec 2013
  14. 14.
    Mirametrix, S2 Eye Tracker (2014) Accessed 25 Feb 2014
  15. 15.
    iMotions A/S Denmark, Quotation no. 884836000000801041, 21 February 2014Google Scholar
  16. 16.
    Gazepoint Products (2014) Accessed 15 Feb 2014
  17. 17.
    EyeGuide Mobile Tracking Price (2014) Accessed 13 June 2014
  18. 18.
    Aerobe Medicare Pvt. Ltd., New Delhi, Quotation no. nil, 07 April 2014Google Scholar
  19. 19.
  20. 20.
    Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE conference computer vision and pattern recognition, 2001Google Scholar
  21. 21.
    Product—Frontech (2012) E-brochure. Accessed 25 May 2012
  22. 22.
    Logitech HD Webcam C525 (2013) Accessed 12 Oct 2013
  23. 23.
    Sarkar AR, Sanyal G, Majumder S (2013) Hand gesture recognition systems: a survey. Int J Comput Appl 71(15):25–37Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Arpita Ray Sarkar
    • 1
    Email author
  • Goutam Sanyal
    • 1
  • Somajyoti Majumder
    • 1
    • 2
  1. 1.Department of Computer Science and EngineeringNational Institute of TechnologyDurgapurIndia
  2. 2.Surface Robotics LabCSIR—Central Mechanical Engineering Research InstituteDurgapurIndia

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