Towards Assistive Robotics for Home Rehabilitation

Elsa Andrea Kirchner, Jan Christian Albiez, Anett Seeland, Mathias Jordan, Frank Kirchner

2013

Abstract

In this paper, we want to point out the possibilities that arise from the latest advances in robotic exoskeleton design and control. We show that approaches of artificial intelligence research and robotics that integrate psychophysiological data analysis offer the possibility to assist disabled people in their everyday lives. Thus, continuous long term rehabilitation training and daily support can be provided in the future to help them to regain motor functions. We outline a possible scenario for fully embedded home rehabilitation and its components. The presented work further investigates two challenges of the application of such a system in more detail: (i) improvement of the interaction between the patient and the supporting interface and (ii) enhancement of reliability of predictions made about the patients intention. In the experimental part we demonstrate that the exoskeleton control can compensate for gravitational loads, imposed by the device itself. Further, we present results that show that movement onset prediction can be made based on different psychophysiological measures, and can be improved with respect to their reliability.

References

  1. Arvetti, M., Gini, G., and Folgheraiter, M. (2007). Classification of EMG signals through wavelet analysis and neural networks for controlling an active hand prosthesis. In Proc. 2007 IEEE 10th Intern. Conf. on Rehabilitation Robotics, pages 531-536, Noordwijk.
  2. Beer, R., Naujokas, C., Bachrach, B., and Mayhew, D. (2008). Development and evaluation of a gravity compensated training environment for robotic rehabilitation of post-stroke reaching. In Conf. on Biomed. Robotics and Biomechatronics (BioRob-2008), pages 205-210, Scottsdale.
  3. Blankertz, B., Dornhege, G., Lemm, S., Krauledat, M., Curio, G., and Müller, K.-R. (2006). The Berlin BrainComputer Interface: Machine learning based detection of user specific brain states. J. of Universal Computer Science, 12(6):581-607.
  4. Brodersen, K., Ong, C., Stephan, K., and Buhmann, J. M. (2010). The balanced accuracy and its posterior distribution. Proc. 20th Intern. Conf. on Pattern Recognition, pages 3121-3124.
  5. Clark, M. and Smith, D. (1999). Psychological correlates of outcome following rehabilitation from stroke. Clinical Rehabilitation, 13(2):129-140.
  6. Folgheraiter, M., Jordan, M., Straube, S., Seeland, A., Kim, S. K., and Kirchner, E. A. (2012). Measuring the Improvement of the Interaction Comfort of a Wearable Exoskeleton. Intern. J. of Social Robotics, 4(3):285- 302.
  7. Folgheraiter, M., Kirchner, E. A., Seeland, A., Kim, S. K., Jordan, M., Wöhrle, H., Bongardt, B., Schmidt, S., Albiez, J., and Kirchner, F. (2011). A multimodal brain-arm interface for operation of complex robotic systems and upper limb motor recovery. In Proc. 4th Int. Conf. Biomed. Electronics and Devices, pages 150-162, Rome.
  8. Gancet, J., Ilzkovitz, M., Cheron, G., Ivanenko, Y., van der Kooij, H., van der Helm, F., Zanow, F., and Thorsteinsson, F. (2011). MINDWALKER: A Brain Controlled Lower Limbs Exoskeleton for Rehabilitation. Potential Applications To Space. In 11th Symp. on Adv. Space Techn. in Robotics and Automation, pages 12-14.
  9. Guidali, M., Duschau-Wicke, A., Broggi, S., KlamrothMarganska, V., Nef, T., and Riener, R. (2011). A robotic system to train activities of daily living in a virtual environment. Med. and Biol. Engineering and Computing, 49(10):1213-1223.
  10. Hesse, S., Werner, C., and Brocke, J. (2009). Maschinenund Robotereinsatz in der Neurorehabilitation. Orthopädie-Technik, 2:74-77.
  11. Hogan, N., Krebs, H., Charnnarong, J., Srikrishna, P., and Sharon, A. (1992). MIT-MANUS: a workstation for manual therapy and training I. In Proc. Intern. Works. on Robot and Human Comm., pages 161-165.
  12. Jordan, M., Benitez, L. M. V., Schmidt, S., Folgheraiter, M., and Albiez, J. (2012). Model-Based Control and Design of a Low-Pressure Fluid Actuation System for Haptic Devices. In Proc. 13th Intern. Conf. on New Actuators (Actuator-12), pages 295-298, Bremen.
  13. Kelly, R. (1997). PD Control with Desired Gravity Compensation of Robotic Manipulators: A Review. The Intern. J. of Robotics Research, 16(5):660-672.
  14. Kornhuber, H. H. and Deecke, L. (1965). Hirnpotentialänderungen bei Willk ürbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale. Pflüger's Archiv für die ges. Phys. des Menschen und der Tiere, 284(1):1-17.
  15. Leeb, R., Keinrath, C., Friedman, D., Guger, C., Scherer, R., Neuper, C., Garau, M., Antley, A., Steed, A., and Slater, M. (2006). Walking by thinking: the brainwaves are crucial, not the muscles! Presence: Teleoperators and Virtual Environments, 15(5):500-514.
  16. Lenzi, T., De Rossi, S., Vitiello, N., and Carrozza, M. (2012). Intention-based EMG Control for Powered Exoskeletons. IEEE Transa. on Biomed. Engineering, 59(8):2180-2190.
  17. Meyer-Bäse, U. (2007). Digital Signal Processing with Field Programmable Gate Arrays. Springer-Verlag, Berlin, Heidelberg, 3rd edition.
  18. Mihelj, M., Nef, T., and Riener, R. (2007). ARMin II - 7 DoF rehabilitation robot: mechanics and kinematics. In 2007 Intern. Conf. on Robotics and Automation, pages 4120-4125.
  19. Nocedal, J. and Wright, S. (1999). Numerical Optimization. Springer Series in Operations Research. SpringerVerlag, New York.
  20. Otsuka, T., Kawaguchi, K., Kawamoto, H., and Sankai, Y. (2011). Development of Upper-limb type HAL and Reaching Movement for Meal-Assistance. In Proc. 2011 IEEE Intern. Conf. on Robotics and Biomimetics (ROBIO-11), pages 883-888.
  21. Platz, T. and Roschka, S. (2009). Rehabilitative Therapie bei Armparese nach Schlaganfall. Neurol. Rehabil., 15(2):81-106.
  22. Santucci, E. and Balconi, M. (2009). The multicomponential nature of movement-related cortical potentials: functional generators and psychological factors. Neuropsychological Trends, (5):59-84.
  23. Shyu, K.-K., Lee, P.-L., Lee, M.-H., Lin, M.-H., Lai, R.-J., and Chiu, Y.-J. (2010). Development of a Low-Cost FPGA-Based SSVEP BCI Multimedia Control System. IEEE Transa. on Biomed. Circuits and Systems, 4(2):125-132.
  24. Tabie, M. and Kirchner, E. A. (2013). EMG Onset Detection-Comparison of different methods for a movement prediction task based on EMG. In BIOSIGNALS. SciTePress. Accepted.
  25. Takahashi, C. D., Der-Yeghiaian, L., Le, V., Motiwala, R. R., and Cramer, S. C. (2008). Robot-based hand motor therapy after stroke. Brain, 131(2):425-437.
  26. Villiger, M., Hepp-Reymond, M.-C., Pyk, P., Kiper, D., Eng, K., Spillman, J., Meilick, B., Estevez, N., Kollias, S. S., Curt, A., and Hotz-Boendermaker, S. (2011). Virtual reality rehabilitation system for neuropathic pain and motor dysfunction in spinal cord injury patients. In 2011 Intern. Conf. on Virt. Rehab. (ICVR), pages 1-4.
  27. Volpe, B., Krebs, H., Hogan, N., Edelstein, O., Diels, C., and Aisen, M. (2000). A novel approach to stroke rehabilitation: robot-aided sensorimotor stimulation. Neurology, 54(10):1938-1944.
  28. Zander, T. O., Gaertner, M., Kothe, C., and Vilimek, R. (2010). Combining eye gaze input with a braincomputer interface for touchless human-computer interaction. Intern. J. of Human-Computer Interaction, 27(1):38-51.
Download


Paper Citation


in Harvard Style

Andrea Kirchner E., Christian Albiez J., Seeland A., Jordan M. and Kirchner F. (2013). Towards Assistive Robotics for Home Rehabilitation . In Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2013) ISBN 978-989-8565-34-1, pages 168-177. DOI: 10.5220/0004248501680177


in Bibtex Style

@conference{biodevices13,
author={Elsa Andrea Kirchner and Jan Christian Albiez and Anett Seeland and Mathias Jordan and Frank Kirchner},
title={Towards Assistive Robotics for Home Rehabilitation},
booktitle={Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2013)},
year={2013},
pages={168-177},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004248501680177},
isbn={978-989-8565-34-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: BIODEVICES, (BIOSTEC 2013)
TI - Towards Assistive Robotics for Home Rehabilitation
SN - 978-989-8565-34-1
AU - Andrea Kirchner E.
AU - Christian Albiez J.
AU - Seeland A.
AU - Jordan M.
AU - Kirchner F.
PY - 2013
SP - 168
EP - 177
DO - 10.5220/0004248501680177