Advertisement

Uncovering Neural Commands from Noisy Dermal Signals—an Experimental Verification of a Minimalistic Robotic Exoskeletal Hand Design for Medical Rehabilitation

  • Nathan Reimus
  • Alan Barhorst
  • Mary Baker
Original Article
  • 357 Downloads

Abstract

Exoskeletal medical rehabilitation devices require the interpretation of neural movement signals via surface electromyographic sensors. In many cases, the signals are imbedded in noise (due to underlying pathologies of the rehab patient) and must be processed such that movement commands can be processed instantly. This paper will report on a prototype design of a device that addresses many of the issues found with previous devices, including the size as well as providing insufficient assistance force, and associated signal processing logic. The prototype addresses these issues by using a tendon drive mechanism actuated by a power screw, which will have the ability to restore mobility to each finger of the user while at the same time providing a low profile and low operating power. The following features were integrated into the system: motor position control and characterization, a LabVIEW controller interface, and an EMG control scheme. The prototype was tested on five healthy participants and a cerebral palsy patient. Conducting computer simulations and testing of a physical prototype reveal that the exoskeleton design is viable. Furthermore, a viable controller was developed for the device and validated experimentally through multiple EMG filtering methods by using discrete wavelet transforms and bandpass filtering. This study also discovered that by using discrete wavelet transforms to filter EMG signals, it is possible for an individual with cerebral palsy to control the device. This paper is an application of data-enabled discovery tools in the field of medical orthotics. It provides guidance to readers relative to the use of signal processing techniques as applied to problems in injury rehabilitation.

Keywords

Hand exoskeleton EMG control Tendon drive Robotics Discrete wavelet transforms 

Notes

Acknowledgements

The authors would like to thank Dr. Desirae Mckee and Dr. Renato Gonik at the Texas Tech Health Science Center in Lubbock for finding volunteers for this research and volunteering their time and resources. Additionally, the authors would like to thank all of the participants in the EMG studies conducted for this project.

References

  1. 1.
    S. Balasubramanian, J. Klein, E. Burdet, Robot-assisted rehabilitation of hand function. Curr. Opin. Neurol. 23(6), 661 (2010)CrossRefGoogle Scholar
  2. 2.
    A.A. Barhorst, Symbolic equation processing utilizing vector/dyad notation. J. Sound Vib. 208(5), 823–839 (1998)CrossRefGoogle Scholar
  3. 3.
    C. De Luca, Surface electromyography: Detection and recording. DelSys Incorporated. 10, 2011 (2002)Google Scholar
  4. 4.
    M. DiCicco, L. Lucas, Y. Matsuoka, in Comparison of control strategies for an EMG controlled orthotic exoskeleton for the hand. 2004 IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA’04. vol. 2, pp. 1622–1627. IEEE (2004)Google Scholar
  5. 5.
    G.F. Franklin, J.D. Powell, A. Emami-Naeini, Feedback control of dynamic systems, sixth edn Pearson (2010)Google Scholar
  6. 6.
    Y. Hasegawa, Y. Mikami, K. Watanabe, Y. Sankai, in Five-fingered assistive hand with mechanical compliance of human finger. IEEE International Conference on Robotics and Automation, 2008. ICRA 2008. pp. 718–724. IEEE (2008)Google Scholar
  7. 7.
    G.T.R. Institute, Dexterity and mobility impairment fact sheet. http://accessibility.gtri.gatech.edu/assistant/acc_info/factsheet_dexterity_mobility.php (1997)
  8. 8.
    J. Ochoa, D. Kamper, M. Listenberger, S. Lee, in Use of an electromyographically driven hand orthosis for training after stroke. 2011 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 1–5. IEEE (2011)Google Scholar
  9. 9.
    A. Perotto, E. Delagi, Anatomical guide for the electromyographer: the limbs and trunk. Charles C Thomas Pub Ltd (2011)Google Scholar
  10. 10.
    N. Reimus, Innovative design of a robotic exoskeletal hand for medical rehabilitation. Master’s thesis, Texas Tech University (2012)Google Scholar
  11. 11.
    A. Wege, G. Hommel, in Development and control of a hand exoskeleton for rehabilitation of hand injuries. 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005.(IROS 2005). pp. 3046–3051. IEEE (2005)Google Scholar
  12. 12.
    A. Wege, A. Zimmermann, in Electromyography sensor based control for a hand exoskeleton. IEEE International Conference on Robotics and Biomimetics, 2007. ROBIO 2007. pp. 1470–1475. IEEE (2007)Google Scholar
  13. 13.
    H. Yamaura, K. Matsushita, R. Kato, H. Yokoi, in Development of hand rehabilitation system using wire-driven link mechanism for paralysis patients. 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 209–214. IEEE (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringTexas Tech UniversityLubbockUSA
  2. 2.Department of Electrical EngineeringTexas Tech UniversityLubbockUSA

Personalised recommendations