Jump to ContentJump to Main Navigation
Show Summary Details
More options …

at - Automatisierungstechnik

Methoden und Anwendungen der Steuerungs-, Regelungs- und Informationstechnik

[AT - Automation Technology: Methods and Applications of Control, Regulation, and Information Technology
]

Editor-in-Chief: Jumar, Ulrich


IMPACT FACTOR 2017: 0.503

CiteScore 2017: 0.47

SCImago Journal Rank (SJR) 2017: 0.212
Source Normalized Impact per Paper (SNIP) 2017: 0.546

Online
ISSN
2196-677X
See all formats and pricing
More options …
Volume 66, Issue 12

Issues

Automatic control of grasping strength for functional electrical stimulation in forearm movements via electrode arrays

Automatische Regelung der Griffstärke mit funktioneller Elektrostimulation über Elektrodenarrays bei Unterarmbewegungen

Christina Salchow-Hömmen
  • Corresponding author
  • Control Systems Group, Technische Universität Berlin, Sekretariat EN 11, Einsteinufer 17, 10587 Berlin, Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Till Thomas / Markus Valtin / Thomas Schauer
Published Online: 2018-11-29 | DOI: https://doi.org/10.1515/auto-2018-0068

Abstract

The generation of precise hand movements with functional electrical stimulation (FES) via surface electrodes on the forearm faces several challenges. Besides the biomechanical complexity and the required selectivity, the rotation of the forearm during reach-and-grasp tasks leads to a relative change between the skin and underlying tissue, resulting in a varying FES response. We present a new method for automatic adaptation of virtual electrodes (size, position) and stimulation intensity in an electrode array to guarantee a secure grasp during forearm movements. The method involves motion tracking of arm and hand with inertial sensors. This enables the estimation of grasping strength when using elastic objects. Experiments in healthy volunteers revealed that our method allows generating a strong, stable grasp force regardless of the rotational state of the forearm.

Zusammenfassung

Die Erzeugung präziser Handbewegungen mit funktioneller Elektrostimulation über Elektroden am Unterarm ist eine Herausforderung. Neben der biomechanischen Komplexität und der erforderlichen Selektivität führt die Drehung des Unterarms zu einer relativen Verschiebung von Haut und darunter liegendem Gewebe, was zu einer veränderten Reaktion auf die FES führt. Wir stellen eine neue Methode zur automatischen Anpassung einer virtuellen Elektrode (Größe, Position) und der Stimulationsintensität in einem Elektrodenarray vor, mit der ein sicherer Griff während Unterarmbewegungen gewährleistet wird. Über Inertialsensoren werden Arm- und Handbewegungen erfasst. Zusätzlich kann die Griffstärke bei komprimierbaren Objekten geschätzt werden. In Experimenten an gesunden Probanden konnte eine starke, stabile Greifkraft unabhängig vom Rotationszustand des Unterarms erzeugt werden.

Keywords: functional electrical stimulation; automatic control; virtual electrode; electrode array; hand neuroprosthesis

Schlagwörter: Funktionelle Elektrostimulation; Regelung; virtuelle Elektrode; Elektrodenarray; Handneuroprothese

References

  • 1.

    M. R. Popovic, D. B. Popovic and T. Keller, “Neuroprostheses for grasping,” Neurological research, vol. 24, no. 5, pp. 443–452, 2002.CrossrefGoogle Scholar

  • 2.

    A. Crema, N. Malešević, I. Furfaro, F. Raschellà, A. Pedrocchi and S. Micera, “A wearable multi-site system for NMES-based hand function restoration,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, pp. 428–440, 2018.Web of ScienceCrossrefGoogle Scholar

  • 3.

    G. Bijelić, A. Popović-Bijelić, N. Jorgovanović, D. Bojanić and D. B. Popović, “E actitrode: The new selective stimulation interface for functional movements in hemiplegics patients,” Serbian Journal of Electrical Engineering, vol. 1, no. 3, pp. 21–28, 2004.CrossrefGoogle Scholar

  • 4.

    M. Lawrence, T. Kirstein and T. Keller, “Electrical simulation of the finger flexors using ‘virtual electrodes’,” in Proceedings of the 8th International Workshop on Functional Electrical Stimulation, M. Bijak, Ed., Vienna, Austria, 10–13 September 2004, pp. 191–194.Google Scholar

  • 5.

    O. Schill, R. Rupp, C. Pylatiuk, S. Schulz and M. Reischl, “Automatic adaptation of a self-adhesive multi-electrode array for active wrist joint stabilization in tetraplegic SCI individuals,” in Proc. of the IEEE TIC-STH International Conference, Toronto, ON, Canada, 26–27 September 2009, pp. 708–713.Google Scholar

  • 6.

    L. Popović-Maneski, M. Kostić, G. Bijelić, T. Keller, S. Mitrović, L. Konstantinović and D. B. Popović, “Multi-pad electrode for effective grasping: design,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 4, pp. 648–654, 2013.CrossrefWeb of ScienceGoogle Scholar

  • 7.

    T. Keller and A. Kuhn, “Electrodes for transcutaneous (surface) electrical stimulation,” Journal of Automatic Control, vol. 18, pp. 35–45, 2008.CrossrefGoogle Scholar

  • 8.

    A. J. Westerveld, A. C. Schouten, P. H. Veltink and H. van der Kooij, “Selectivity and resolution of surface electrical stimulation for grasp and release,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 20, no. 1, pp. 94–101, 2012.Web of ScienceCrossrefGoogle Scholar

  • 9.

    P. E. Crago, R. J. Nakai and H. J. Chizeck, “Feedback regulation of hand grasp opening and contact force during stimulation of paralyzed muscle,” IEEE Transactions on Biomedical Engineering, vol. 38, no. 1, pp. 17–28, 1991.CrossrefGoogle Scholar

  • 10.

    A. J. Westerveld, A. Kuck, A. C. Schouten, P. H. Veltink and H. van der Kooij, “Grasp and release with surface functional electrical stimulation using a model predictive control approach,” in Proc. of the IEEE 34th Annual International Conference on Engineering in Medicine and Biology Society (EMBS), San Diego, CA, USA, 28 August–1 September 2012, pp. 333–336.Google Scholar

  • 11.

    C. T. Freeman, “Iterative learning control of FES applied to the upper extremity for rehabilitation,” Control Engineering Practice, vol. 23, pp. 32–43, 2014.Web of ScienceGoogle Scholar

  • 12.

    M. Kutlu, C. Freeman, A. M. Hughes and M. Spraggs, “A home-based FES system for upper-limb stroke rehabilitation with iterative learning control,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 12089–12094, 2017, 20th IFAC World Congress.CrossrefGoogle Scholar

  • 13.

    T. Schauer, “Sensing motion and muscle activity for feedback control of functional electrical stimulation: Ten years of experience in Berlin,” Annual Reviews in Control, vol. 44, pp. 355–374, 2017.CrossrefWeb of ScienceGoogle Scholar

  • 14.

    C. Salchow, M. Valtin, T. Seel and T. Schauer, “A new semi-automatic approach to find suitable virtual electrodes in arrays using an interpolation strategy,” European Journal of Tranlational Myology, vol. 26, no. 2, 2016.Google Scholar

  • 15.

    T. Thomas, C. Salchow, M. Valtin and T. Schauer, “Automatic real-time adaptation of electrode positions for grasping with FES during forearm movements,” in AUTOMED Workshop, Villingen-Schwenningen, Germany, 15–16 March 2018.Google Scholar

  • 16.

    M. Valtin, C. Salchow, T. Seel, D. Laidig and T. Schauer, “Modular finger and hand motion capturing system based on inertial and magnetic sensors,” Current Directions in Biomedical Engineering, vol. 3, no. 1, pp. 19–23, 2017.Google Scholar

  • 17.

    M. Valtin, K. Kociemba, C. Behling, B. Kuberski, S. Becker and T. Schauer, “RehaMovePro: A versatile mobile stimulation system for transcutaneous FES applications,” European Journal of Tranlational Myology, vol. 26, no. 3, 2016.Google Scholar

  • 18.

    E. Imatz Ojanguren, “Neuro-fuzzy modeling of multi-field surface neuroprostheses for hand grasp,” Ph.D. dissertation, University of the Basque Country, 2016.Google Scholar

  • 19.

    L. Popović-Maneski, N. M. Malešević, A. M. Savić, T. Keller and D. B. Popović, “Surface-distributed low-frequency asynchronous stimulation delays fatigue of stimulated muscles,” Muscle & nerve, vol. 48, no. 6, pp. 930–937, 2013.Web of ScienceCrossrefGoogle Scholar

  • 20.

    R. Shalaby, “Development of an electromyography detection system for the control of functional electrical stimulation in neurological rehabilitation,” Ph.D. dissertation, Technische Universität Berlin, Control Systems Group, 2011.

  • 21.

    M. Sojka and P. Píša, “Usable simulink embedded coder target for linux,” in 16th Real Time Linux Workshop, Düsseldorf, Germany, 12–13 October 2014.Google Scholar

  • 22.

    K. Åström and T. Hägglund, PID controllers: theory, design, and tuning, 2nd ed. USA: Instrument Society of America, 1995.Google Scholar

  • 23.

    D. Laidig, T. Schauer and T. Seel, “Exploiting kinematic constraints to compensate magnetic disturbances when calculating joint angles of approximate hinge joints from orientation estimates of inertial sensors,” in Proc. of 15th IEEE Conference on Rehabilitation Robotics (ICORR), London, UK, 17–21 July 2017, pp. 971–976.Google Scholar

About the article

Christina Salchow-Hömmen

Christina Salchow-Hömmen received her Bachelor’s (2012) and Master’s degree (2014) in Biomedical Engineering from the Technische Universität Ilmenau, Germany. During her studies, she stayed at the Washington University in St. Louis, USA, for a seven-month research internship. Since 2014, she is a Ph.D. candidate at the Control Systems Group at the Technische Universität Berlin, Germany. Her research focuses on rehabilitation engineering and neuroscience.

Till Thomas

Till Thomas received his Bachelor’s degree (2015) in Biomedical Engineering from the Ernst-Abbe-Hochschule Jena – University of Applied Sciences, Germany. During his studies towards his Master’s degree at the Technische Universität Berlin (Germany), he worked as a student assistant at the Control Systems Group. In 2018, he spoke at the AUTOMED workshop in Villingen-Schwenningen.

Markus Valtin

Markus Valtin received his Diploma (2012) in Electrical Engineering at the Technische Universität Berlin, Germany. Since 2012, he is a Ph.D. candidate at the Control Systems Group. His research focuses on Rehabilitation Engineering with the main topics functional electrical stimulation via electrode arrays and inertial sensor applications.

Thomas Schauer

Thomas Schauer studied Electrical Engineering at the University Magdeburg in Germany from 1992 to 1997. He received his Ph.D. degree in Mechanical Engineering from the University of Glasgow in Scotland. From December 2001 until April 2006 he has been working as research assistant and project leader at the Max Planck Institute for Dynamics of Complex Technical Systems (Magdeburg, Germany) in the Systems and Control Theory Group. Since 2006 he holds a position as senior researcher in the Control Systems Group at the Technische Universität Berlin and manages the research topic “Rehabilitation Engineering and Assistive Technology”.


Received: 2018-05-14

Accepted: 2018-09-04

Published Online: 2018-11-29

Published in Print: 2018-12-19


Funding Source: Bundesministerium für Bildung und Forschung

Award identifier / Grant number: FKZ16SV7069K

The presented work was partly conducted within the research project BeMobil, supported by the German Federal Ministry of Education and Research (FKZ16SV7069K).


Citation Information: at - Automatisierungstechnik, Volume 66, Issue 12, Pages 1027–1036, ISSN (Online) 2196-677X, ISSN (Print) 0178-2312, DOI: https://doi.org/10.1515/auto-2018-0068.

Export Citation

© 2018 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in