Skip to main content
SpringerLink
Log in

Impedance control of a small treadmill with sonar sensors for automatic speed adaptation

  • Regular Papers
  • Robotics and Automation
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

Automatic speed adaptation in treadmill training plays an important role in gait rehabilitation and virtual reality (VR) environments, where the user can adjust his/her speed for improved motivation and an enhanced sense of reality during walking interactions. To implement automatic speed adaptation of a treadmill belt, we have developed a novel impedance control scheme that accommodates natural movements without mechanical attachments to the user, and can estimate user-treadmill interactive forces to directly detect user intention, while simultaneously maintaining the user’s position on the treadmill platform. The proposed impedance control is realized via user interaction with a fixed virtual spring-damper component, allowing direct acceleration control of the treadmill belt in proportion to user displacement. The technique was applied to a small commercial treadmill (with a belt length of 1.2 m and a width of 0.5 m), which is easily installed and economical to operate, and is widely used in homes and health centers. Inexpensive sonar sensors with a Kalman filter algorithm were employed to measure user motions. To identify the characteristics of the proposed control scheme, a set of experiments was conducted and preliminary user studies with VR interactions were performed. The results of these experiments indicate that our impedance control scheme can provide a non-intrusive, intuitive method for implementing user-selected speed on a small treadmill. The proposed technique is cost-effective, and could potentially be applied to any type of locomotion interface or gait rehabilitation system, without the use of expensive, sophisticated sensors or special treadmills.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. J. Yoon, J. Ryu, and J. Park, “A symmetric planar walking algorithm for foot-platform locomotion interface,” International Journal of Robotics Research, vol. 29, no. 1, pp. 39–59, 2010.

    Article  Google Scholar 

  2. J. Yoon and J. Ryu, “A novel locomotion interface with two 6-DOF parallel manipulators for human walking on various virtual terrains,” International Journal of Robotics Research, vol. 27, no. 07, pp. 689–708, 2006.

    Article  Google Scholar 

  3. S. Hesse, C. Bertelt, A. Schaffrin, and K. H. Mauritz, “Restoration of gait in non-ambulatory hemiparetic patients by treadmill training with partial body weight support,” Arch. Phys. Med. Rehabil., vol. 75, no. 10, pp. 87–1093, October 1994.

    Article  Google Scholar 

  4. J. Yoon, B. Novandy, C. Yoon, and K. Park, “A 6-DOF gait rehabilitation robot with upper and lower-limb connections that allows walking velocity updates on various terrains,” IEEE/ASME Trans. on Mechatronics, vol. 15, no. 2, pp. 201–215, 2010.

    Article  Google Scholar 

  5. R. R. Christensen, J. M. Hollerbach, Y. Xu, and S. G. Meek, “Inertial force feedback for the treadport locomotion interface,” Presence: Teleoperators and Virtual Environments, vol. 9, pp. 1–14, 2000.

    Article  Google Scholar 

  6. J. von Zitzewitz, M. Bernhardt, and R. Riener, “A novel methods for automatic treadmill speed adaptation,” IEEE Trans. Neural Syst. and Rehabil. Eng., vol. 15, no. 3, pp. 401–409, 2007.

    Article  Google Scholar 

  7. A. Koenig, C. Binder, J. von Zitzewitz, X. Omlin, M. Bolliger, and R. Riener, “Voluntary gait speed adaptation for robot-assisted treadmill training,” Proc. of IEEE 11th Int. Conf. on Rehabilitation Robotics, Kyoto, Japan, pp. 419–424, 2009.

    Google Scholar 

  8. D. Checcacci, J. M. Hollerbach, R. Hayward, and M. Bergamasco, “Design and analysis of a harness for torso force application in locomotion interfaces,” Proc. of IEEE EuroHaptics Conference, Los Alamitos, CA, 2003, pp. 53–67.

    Google Scholar 

  9. J. Feasel, M. C. Whitton, L. P. Kassler, F. P. Brooks Jr, and M. D. Lewek, “The integrated virtual environment rehabilitation treadmill system,” IEEE Trans. Neural. Syst. Rehabil. Eng., vol. 19, no. 3, pp. 290–297, 2011.

    Article  Google Scholar 

  10. A. E. Minetti, L. Boldrini, L. Brusamolin, P. Zamparo, and T. McKee, “A feedback-controlled treadmill (treadmill-on-demand) and the spontaneous speed of walking and running in humans,” J. Appl. Physiol., vol. 95, pp. 838–843, 2003.

    Google Scholar 

  11. L. Lichtenstein, J. Barabas, R. L. Woods, and E. Peli, “A feedback control instrument for treadmill locomotion in virtual environments,” ACM Trans. Appl. Percept., vol. 4, no. 1, January 2007.

    Google Scholar 

  12. J. Fung, C. L. Richards, F. Malouin, B. J. McFadyen, and A. Lamontagne, “A treadmill and motion coupled virtual reality system for gait training poststroke,” Cyberpsychol. Behav, vol. 9, pp. 157–162, 2006.

    Article  Google Scholar 

  13. A. De Luca, R. Mattone, P. R. Giordano, and H. H. Bulthoff, “Control design of the 2D CyberWalk platform,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, St. Louis, MO, pp. 5051–5058, October 2009.

    Google Scholar 

  14. J. L. Souman, P. R. Giordano, I. Frissen, A. D. Luca, and M. O. Ernst, “Making virtual walking real: perceptual evaluation of a new treadmill control algorithm,” ACM Trans. Applied Perception, vol. 7, no. 2, article 11, 2010.

    Google Scholar 

  15. H. Noma and T. Miyasato, “Design for locomotion interface in a large scale virtual environment ATLAS: ATR locomotion interface for active self motion,” Proc. of the IEEE 7th Annual Symposium on Haptic Interface for Virtual Environments and Teleoperated Systems, Los Alamitos CA, 1998, pp. 111–118.

    Google Scholar 

  16. J. Yoon, H. Park, and D. L. Damiano, “A novel walking speed estimation scheme and its application to treadmill control for gait rehabilitation,” Journal of Neuro Engineering and Rehabilitation, vol. 9, no. 62, 2012.

    Google Scholar 

  17. N. Hogan, “On the stability of manipulators performing contact tasks,” IEEE J. Robotics and Automation, vol. 4, pp. 677–686, 1988.

    Article  Google Scholar 

  18. K. L. Shi, T. F. Chan, Y. K. Wong, and S. L. Ho, “Speed estimation of an induction motor drive using an optimized extended Kalman filter,” IEEE Trans. Industrial Electronics, vol. 49, no. 1, pp. 124–133, February 2002.

    Article  Google Scholar 

  19. P. R. Belanger, “Estimation of angular velocity and acceleration from shaft encoder measurement,” Proc. of IEEE Int. Conf. on Robotics and Automation, Nice, France, pp. 585–592, 1992.

    Google Scholar 

  20. L. Drolet, F. Michaud, and J. Cote, “Adaptable sensor fusion using multiple Kalman filter,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robot and System, Takamatsu, Japan, pp. 1434–1439, 2000.

    Google Scholar 

  21. K. K. Busawon and P. Kabore, “On the design of integral and proportional integral observers,” Proc. of American Control Conf., Chicago, USA, pp. 3725–3729, 2000.

    Google Scholar 

  22. H. Lee and V. I. Utkin, “Chattering suppression methods in sliding mode control systems,” Annual Reviews in Control, vol. 31, pp. 179–188, 2007.

    Article  Google Scholar 

  23. D. A. Winter, Biomechanics and Motor Control of Human Movement, 2nd edition, Wiley, New York, 1990.

    Google Scholar 

  24. A. Albu-Schaffer, O. Eiberger, M. Grebenstein, S. Haddadin, C. Ott, F. Petit, T. Wimbock, S. Wolf, and G. Hirzinger, “Soft robotics: from torque feedback controlled lightweight robots to intrinsically compliant systems,” Proc. of Int. Conf. on Control, Automation and Systems, Korea, 2010.

    Google Scholar 

  25. J. E. Colgate, P. E. Grafing, M. C. Stanley, and G. Schenkel, “Implementation of stiff virtual walls in force-reflecting interfaces,” Proc. of Virtual Reality Annu. Int. Symposium, Seattle, WA, pp. 202–208, September 1993.

    Chapter  Google Scholar 

  26. T. Wimboeck, C. Ott, and G. Hirzinger, “Passivity-based object level impedance control for multifingered hand,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Beijing, China, pp. 4621–4627, 2006.

    Google Scholar 

  27. G. Niemeyer and J.-J. E. Slotine, “Stable adaptive teleoperation,” IEEE Journal of Oceanic Engineering, vol. 16, no. 1, pp. 152–162, 1991.

    Article  Google Scholar 

  28. M. M. Moghaddam and M. Buehler, “Control of virtual motion systems,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robot and System, Yokohama, Japan, pp. 63–67, 1993.

    Google Scholar 

  29. T. McGeer, “Passive dynamic walking,” International Journal of Robotics Research, vol. 9, no. 2, pp. 62–82, 1990.

    Article  Google Scholar 

  30. D. A. Lawrence, “Impedance control stability properties in common implementations,” Proc. of Int. Conf. on Robotics and Automation, Denver, CO, 1988.

    Google Scholar 

  31. Y. X. Shu, D. Sun, and B. Y. Duan, “Design of an enhanced nonlinear PID controller,” Mechatronics, vol. 15, pp. 1005–1024, 2005.

    Article  Google Scholar 

  32. A. Manurung, J. Yoon, and H. Park, “Speed adaptation control of a small-sized treadmill with state feedback controller,” Proc. of 3rd IEEE RAS/EMBS Int. Conf. on Biomedical Robotics and Biomechatronics, Tokyo, Japan, pp. 15–20, 2010.

    Google Scholar 

  33. Ogre 3D, Available at http://www.ogre3d.org.

  34. H. Dong, T. Oshiumi, A. Nagano, and Z. Luo, “Development of a 3D interactive virtual market system with adaptive treadmill control,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robot and System, Taipei, Taiwan, pp. 5238–5244, 2010.

    Google Scholar 

  35. J. Yoon, J. Ryu, and K. Lim, “A novel reconfigurable ankle rehabilitation robot for various exercise modes,” Journal of Robotic Systems, vol. 22. no. s1, pp. s15–s33, 2006.

    Article  Google Scholar 

  36. R. W. Bohannon and A. Williams, “Normal walking speed: a descriptive meta-analysis,” Physiotherapy, vol. 97, no. 3, pp. 182–9, 2011.

    Article  Google Scholar 

  37. R. Seifabadi, S. M. Rezaei, S. S. Ghidary, and M. Zareinejad, “A teleoperation system for micro positioning with haptic feedback,” International Journal of Control, Automation, and Systems, vol. 11, no. 4, pp. 768–775, 2013.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical and Aerospace Engineering and ReCAPT, Gyeongsang National University, Jinju, Korea

    Jungwon Yoon & Auralius Manurung

  2. Department of Control and Measurement Engineering, Gyeongsang National University, Jinju, Korea

    Gap-Soon Kim

Authors
  1. Jungwon Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Auralius Manurung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gap-Soon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jungwon Yoon.

Additional information

Recommended by Associate Editor Soohee Han under the direction of Editor Myotaeg Lim.

This work was supported by the National Research Foundation Korea (NRF) funded by the Ministry of Education, Science and Technology (2012R1A2A2A01047344) and supported by Dual Use Technology Program of Civil and Military.

Jungwon Yoon received his Ph.D. degree from the Department of Mechatronics in 2005 from Gwangju Institute of Science and Technology (GIST), Kwangju, Korea, After the Ph.D. degree. He worked as a senior researcher in Electronics Tele-communication Research Institute (ETRI), Daejeon, Korea. From 2001 to 2002, he worked as a visiting researcher at Virtual Reality Lab, the Rutgers University, U.S.A, and as a visiting fellow at Functional & Applied Biomechanics Section, Rehabilitation Medicine of Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA, from 2010 to 2011. In 2005, he joined the School of Mechanical & Aerospace Engineering, Gyeongsang National University, Jinju, Korea, where he is currently an associate professor. His research interests include virtual reality haptic devices & locomotion interfaces, and rehabilitation robots. He has published more than 40 peer reviewed journal articles and patents.

Auralius Manurung received his Master’s degree from the School of Mechanical and Aerospace Engineering, Gyeongsang National University, Jinju, Korea in 2011. After the Master’s degree, He joined the Rehabilitation Engineering Lab in September 2011 to pursue a Ph.D. degree from ETH Zürich, Swiss. His research interests include intelligent surgical robots and rehabilitation robots, their control algorithms.

Gap-Soon Kim received his M.S. and Ph.D. degrees in Precision Mechanical Engineering from Hanyang University, in 1990 and 1999, respectively. He was previously employed as a Senior Researcher in the Division of Mechanical Metrology, KRISS (Korea Research Institute of Standards Science), Taejon, Korea. Since 2000, he has been a Professor at Gyeongsang National University. His research interests include the design of multi-axis force/moment sensor, intelligent system and control, service robots and humanoid robots.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yoon, J., Manurung, A. & Kim, GS. Impedance control of a small treadmill with sonar sensors for automatic speed adaptation. Int. J. Control Autom. Syst. 12, 1323–1335 (2014). https://doi.org/10.1007/s12555-013-0241-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-013-0241-3

Keywords

Search

Navigation