JRM Vol.25 No.6 pp. 1070-1077
doi: 10.20965/jrm.2013.p1070


Optimized Motion Control of an Intelligent Cane Robot for Easing Muscular Fatigue in the Elderly During Walking

Pei Di*1, Jian Huang*2, Shotaro Nakagawa*1,
Kosuke Sekiyama*1, Qiang Huang*3, and Toshio Fukuda*1,*4

*1Department of Micro-Nano Systems Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8603, Japan

*2Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430-074, China

*3School of Mechatronic Engineering, Beijing Institute of Technology, Beijing 100-081, China

*4Faculty of Science and Technology, Meijo University, Aichi 468-8502, Japan

May 24, 2013
November 3, 2013
December 20, 2013
cane robot, easing fatigue, human walking intention, on-shoe load sensor
In previous works, an intelligent cane robot was proposed to assist the elderly or persons with conditions that slightly restrict their motion ability. The cane robot can help the elderly walk in both indoor and outdoor environments because of its miniaturized design and mobility. In the intentional direction (ITD) concept the user’s walking intention is estimated by analyzing signals from a six-axis force/torque sensor. An admittance control method controls the motion of the cane robot. In some cases, however the elderly can not walk uniformly because one leg suffers from muscular weakness. When the affected leg is in the support phase, the cane robot should stop to absorb more strain than the affected leg. When the healthy leg is in the support phase, the cane robot should move forward according to ITD. In contrast to ITD, the motion of the cane robot should be controlled considering the walking pattern characteristics of the elderly to ensure safety and effectiveness. In this paper, an optimizedmotion control of the cane robot is proposed that is based on the characteristics gait pattern (CGP). An on-shoe load sensor was used to evaluate the reduction in muscular fatigue for the user’s affected leg. The effectiveness of the proposed method was verified through experiments.
Cite this article as:
P. Di, J. Huang, S. Nakagawa, K. Sekiyama, Q. Huang, and T. Fukuda, “Optimized Motion Control of an Intelligent Cane Robot for Easing Muscular Fatigue in the Elderly During Walking,” J. Robot. Mechatron., Vol.25 No.6, pp. 1070-1077, 2013.
Data files:
  1. [1] K. Kiguchi, K. Iwami, M. Yasuda, K. Watanabe, and T. Fukuda, “An exoskeletal robot for human shoulder joint motion assist,” IEEE/ASME Trans. on Mechatronics, Vol.8, No.1, pp. 125-135, 2003.
  2. [2] K. Kiguchi, T. Tanaka, and T. Fukuda, “Neuro-fuzzy control of a robotic exoskeleton with emg signals,” IEEE Trans. on Fuzzy Systems, Vol.12, No.4, pp. 481-490, 2004.
  3. [3] K. Xing, J. Huang, J. He, Y. Wang, Q. Xu, and J. Wu, “Sliding mode tracking for actuators comprising pneumatic muscle and torsion spring,” Trans. of the Institute of Measurement and Control, Vol.34, No.2-3, pp. 255-277, 2012.
  4. [4] W. Huo, J. Huang, Y. Wang, and J. Wu, “Control of a rehabilitation robotic exoskeleton based on intentional reaching direction,” pp. 357-362, 2010.
  5. [5] J. Wu, J. Huang, Y. Wang, and K. Xing, “Rls-esn based pid control for rehabilitation robotic arms driven by pm-ts actuators,” pp. 511-516, 2010.
  6. [6] K. Kong and D. Jeon, “Design and control of an exoskeleton for the elderly and patients,” IEEE/ASME Trans. on Mechatronics, Vol.11, No.4, pp. 428-432, 2006.
  7. [7] H. Kawamoto, S. Taal, H. Niniss, T. Hayashi, K. Kamibayashi, K. Eguchi, and Y. Sankai, “Voluntary motion support control of robot suit hal triggered by bioelectrical signal for hemiplegia,” in Engineering in Medicine and Biology Society (EMBC), 2010 Annual Int. Conf. of the IEEE, pp. 462-466, 2010.
  8. [8] K. Xing, J. Huang, Y. Wang, J. Wu, Q. Xu, and J. He, “Tracking control of pneumatic artificial muscle actuators based on sliding mode and non-linear disturbance observer,” IET control theory & applications, Vol.4, No.10, pp. 2058-2070, 2010.
  9. [9] T. Ohnuma, G. Lee, and N. Y. Chong, “Particle filter based feedback control of jaist active robotic walker,” in IEEE RO-MAN 2011, pp. 264-269, 2011.
  10. [10] L. Ran, S. Helal, and S. Moore, “Drishti: An integrated indoor/outdoor blind navigation system and service,” in Proc. of the Second IEEE Annual Conf. on Pervasive Computing and Communications 2004 (PerCom 2004), pp. 23-30, 2004.
  11. [11] Y. Nemoto, S. Egawa, A. Koseki, S. Hattori, T. Ishii, and M. Fujie, “Power-assisted walking support system for elderly,” in Engineering inMedicine and Biology Society 1998, Proc. of the 20th Annual Int. Conf. of the IEEE, Vol.5, pp. 2693-2695, 1998.
  12. [12] M. Giuliani, M. Scopelliti, and F. Fornara, “Elderly people at home: technological help in everyday activities,” in IEEE Int. Workshop on Robot and Human Interactive Communication 2005 (ROMAN 2005), pp. 365-370, 2005.
  13. [13] S. Dubowsky, F. Genot, S. Godding, H. Kozono, A. Skwersky, H. Yu, and L. S. Yu, “Pamm-a robotic aid to the elderly for mobility assistance and monitoring: a “helping-hand” for the elderly,” in IEEE Int. Conf. on Robotics and Automation 2000 (ICRA’00) Proc., Vol.1, pp. 570-576, 2000.
  14. [14] S. Taghvaei, Y. Hirata, and K. Kosuge, “Control of a passive walker using a depth sensor for user state estimation,” in IEEE Int. Conf. on Robotics and Biomimetics (ROBIO 2011), pp. 1639-1645, 2011.
  15. [15] W. Zhou, L. Xu, and J. Yang, “An intent-based control approach for an intelligent mobility aid,” in 2nd Int. Asia Conf. on Informatics in Control, Automation and Robotics (CAR 2010), Vol.2, pp. 54-57, 2010.
  16. [16] I. Ulrich and J. Borenstein, “The guidecane-applying mobile robot technologies to assist the visually impaired,” IEEE Tran. on Systems, Man and Cybernetics, Part A: Systems and Humans, Vol.31, No.2, pp. 131-136, 2001.
  17. [17] I. Shim and J. Yoon, “A human robot interaction system “roji”,” in IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics 2003 (AIM 2003), Proc., Vol.2, pp. 723-728, 2003.
  18. [18] Y. Hirata, A. Hara, and K. Kosuge, “Motion control of passivetype walking support system based on environment information,” in Proc. of IEEE Int. Conf. on Robotics and Automation 2005 (ICRA 2005), pp. 2921-2926, 2005.
  19. [19] J. Huang, P. Di, T. Fukuda, and T. Matsuno, “Motion control of omni-directional type cane robot based on human intention,” in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems 2008 (IROS 2008), pp. 273-278, 2008.
  20. [20] P. Di, J. Huang, K. Sekiyama, and T. Fukuda, “Motion control of intelligent cane robot under normal and abnormal walking condition,” in IEEE RO-MAN 2011, pp. 497-502, 2011.
  21. [21] P. Di, J. Huang, K. Sekiyama, S. He, S. Nakagawa, F. Chen, and T. Fukuda, “Optimal posture control for stability of intelligent cane robot,” IEEE RO-MAN 2012, pp. 725-730, 2012.
  22. [22]
    Supporting Online Materials:[a] [Accessed May 15, 2013]
  23. [23] [b] Cane-Correctly [Accessed May 15, 2013]

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