Advertisement

Design of an Autonomous Mobile Wheel Chair for Disabled Using Electrooculogram (EOG) Signals

  • Mohammad Rokonuzzaman
  • S. M. Ferdous
  • Rashedul Amin Tuhin
  • Sabbir Ibn Arman
  • Tasnim Manzar
  • Md. Nayeemul Hasan

Abstract

This paper discusses the implementation of a simple, effective and low cost design of a microcontroller based wheelchair using the EOG signal collected from muscles those are responsible for the movement of the human eye. This was an exploratory research that was carried out to allow a disabled person to control the wheelchair by using only the movement of his eyes. Electrooculography (EOG) is a technique for sensing and recording the activation signal of the muscles and was used to collect and evaluate the myoelectric signal generated by the eye muscles during different movements. The main purpose of the work is to design a cost-effective, easily affordable and accessible wheel chair for the disabled general masses where advanced attachments like on board computer, digital cameras, sophisticated sensors etc. are not being used, rather concentration has been paid on designing a more simple, practical but effective system using an electronically controlled differential drive structure with only two wheels. A low cost microcontroller (ATMEGA 32) serves as the brain of the system for all types of control purposes.

Keywords

Human Machine Interface Myoelectric Signal Wheel Chair Extra Ocular Muscle Wheelchair Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lowery, M.M., Stoykov, N.S., Taflove, A., Kuiken, T.A.: A Multiple-Layer Finite-Element Model of the Surface EMG Signal. IEEE Trans. on Biomed. Eng. 49(5), 446–454 (2002)CrossRefGoogle Scholar
  2. 2.
    Alemu, M., Kumar, D.K., Bredley, A.: Time- Frequency Analysis of SEMG- with special Consideration to the Interelectrode spacing. IEEE Trans. on Neural System and Rehabilitations Eng. 11(4), 341–345 (2003)CrossRefGoogle Scholar
  3. 3.
    Bonato, P., Member IEEE, Boissy, P., Croce, U.D., Roy, S.H.: Changes in the EMG Signal and the Biomechanics of motion During a repetive Lifting Task. IEEE Transaction on Neural System and Rehabilitation Eng. 10(1), 38–47 (2002)CrossRefGoogle Scholar
  4. 4.
    Bogey, R.A., Perry, J., Gitter, A.: An EMG-to-Force Processing Approach for determining Ankle Muscle Forces During Normal Human Gait. IEEE Trans. on Neural System and Rehabilitations Eng 13(3), 302–310 (2005)CrossRefGoogle Scholar
  5. 5.
    Gao, Z., Lei, J., Song, Q., Yu, Y., Ge, Y.: Research on the Surface EMG Signal for Human Body Motion Recognizing Based on Arm Wrestling Robot. In: Proceedings of the 2006 IEEE International Conference on Information Acquisition, Weihai, Shandong, China, August 20-23 (2006)Google Scholar
  6. 6.
    Moon, I., Lee, M., Chu, J., Mun, M.: Wearable EMG-based HCI for electric-powered wheelchair users with motor disabilities. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 2649–2654 (2005)Google Scholar
  7. 7.
    Barea, R., Boquete, L., Mazo, M., Lopez, E.: System for assisted mobility using eye movements based on electrooculography. IEEE Transactions on Neural Systems and Rehabilitation Engineering 10, 209–218 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mohammad Rokonuzzaman
    • 2
  • S. M. Ferdous
    • 1
  • Rashedul Amin Tuhin
    • 1
  • Sabbir Ibn Arman
    • 1
  • Tasnim Manzar
    • 1
  • Md. Nayeemul Hasan
    • 1
  1. 1.Islamic University of TechnologyGazipurBangladesh
  2. 2.The University of Asia PacificDhakaBangladesh

Personalised recommendations