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Journal of Healthcare Engineering
Volume 2017 (2017), Article ID 8932938, 11 pages
https://doi.org/10.1155/2017/8932938
Research Article

Hierarchical Shared Control of Cane-Type Walking-Aid Robot

1Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, The National Research Centre for Rehabilitation Technical Aids, Beijing 100176, China
2Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China
3The 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 210007, China

Correspondence should be addressed to Qingyang Yan

Received 5 January 2017; Revised 28 May 2017; Accepted 27 June 2017; Published 13 August 2017

Academic Editor: Chengzhi Hu

Copyright © 2017 Chunjing Tao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. K. A. Kline and D. M. Bowdish, “Infection in an aging population,” Current Opinion in Microbiology, vol. 29, pp. 63–67, 2016. View at Publisher · View at Google Scholar · View at Scopus
  2. C. Hu, F. Aeschlimann, G. Chatzipirpiridis et al., “Spatiotemporally controlled electrodeposition of magnetically driven micromachines based on the inverse opal architecture,” Electrochemistry Communications, vol. 81, 2017. View at Publisher · View at Google Scholar
  3. C. Hu, H. Vogler, M. Aellen et al., “High precision, localized proton gradients and fluxes generated by a microelectrode device induce differential growth behaviors of pollen tubes,” Lab on a Chip, vol. 17, 2017. View at Publisher · View at Google Scholar
  4. J. Huang, X. Tu, and J. He, “Design and evaluation of the RUPERT wearable upper extremity exoskeleton robot for clinical and in-home therapies,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 7, pp. 926–935, 2016. View at Publisher · View at Google Scholar · View at Scopus
  5. H. Kawamoto, K. Kamibayashi, Y. Nakata et al., “Pilot study of locomotion improvement using hybrid assistive limb in chronic stroke patients,” BMC Neurology, vol. 13, no. 1, pp. 93–98, 2013. View at Google Scholar
  6. S. Tanabe, E. Saitoh, S. Hirano et al., “Design of the Wearable Power-Assist Locomotor (WPAL) for paraplegic gait reconstruction,” Disability and Rehabilitation: Assistive Technology, vol. 8, no. 1, pp. 84–91, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. T. Kikuchi, T. Tanaka, K. Anzai, S. Kawakami, M. Hosaka, and K. Niino, “Evaluation of line-tracing controller of intelligently controllable walker,” Advanced Robotics, vol. 27, no. 7, pp. 493–502, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Huang, Z. H. Guan, T. Matsuno, T. Fukuda, and K. Sekiyama, “Sliding-mode velocity control of mobile-wheeled inverted-pendulum systems,” IEEE Transactions on Robotics, vol. 26, no. 4, pp. 750–758, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Hirata, A. Hara, and K. Kosuge, “Motion control of passive intelligent walker using servo brakes,” IEEE Transactions on Robotics, vol. 23, no. 5, pp. 981–990, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. K. Wakita, J. Huang, P. Di, K. Sekiyama, and T. Fukuda, “Human-walking-intention-based motion control of an omnidirectional-type cane robot,” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 1, pp. 285–296, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Khatib and R. Chatila, “An extended potential field approach for mobile robot sensor-based motions,” in International Conference on Intelligent Autonomous Systems, pp. 490–496, 1995.
  12. K. Iwatsuka, K. Yamamoto, and K. Kato, “Development of a guide dog system for the blind with character recognition ability,” in First Canadian Conference on Computer and Robot Vision, 2004. Proceedings, pp. 401–405, London, ON, Canada, 2004, IEEE Computer Society.
  13. T. Gonnot and J. Saniie, “Integrated machine vision and communication system for blind navigation and guidance,” in 2016 IEEE International Conference on Electro Information Technology (EIT), pp. 0187–0191, Grand Forks, ND, 2016.
  14. E. B. Kaiser and M. Lawo, “Wearable navigation system for the visually impaired and blind people,” in IEEE Computer Society, 2012 IEEE/ACIS 11th International Conference on Computer and Information Science, pp. 230–233, Shanghai, China, 2012.
  15. C. J. Kim and D. Chwa, “Obstacle avoidance method for wheeled mobile robots using interval type-2 fuzzy neural network,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 3, pp. 677–687, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. F. Fabrizio and A. D. Luca, “Real-time computation of distance to dynamic obstacles with multiple depth sensors,” IEEE Robotics and Automation Letters, vol. 2, no. 1, pp. 56–63, 2017. View at Publisher · View at Google Scholar
  17. J. Huang, P. Di, and T. Fukuda, “Motion control of omni-directional type cane robot based on human intention,” in 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2983–2988, Nice, France, 2009.
  18. P. Di, Y. Hasegawa, S. Nakagawa et al., “Fall detection and prevention control using walking-aid cane robot,” IEEE/ASME Transactions on Mechatronics, vol. 21, no. 2, pp. 625–637, 2016. View at Publisher · View at Google Scholar · View at Scopus
  19. Q. Y. Yan, W. X. Xu, J. Huang, and P. C. Su, “Laser and force sensors based human motion intent estimation algorithm for walking-aid robot,” in 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 1858–1863, Shenyang, China, 2015.
  20. W. Wang, Z. G. Hou, L. Cheng et al., “Toward patients’ motion intention recognition: dynamics modeling and identification of iLeg-An LLRR under motion constraints,” IEEE Transactions on Systems Man and Cybernetics Systems, vol. 46, no. 7, pp. 1–13, 2016. View at Google Scholar
  21. K. Khokar, R. Alqasemi, S. Sarkar, K. Reed, and R. Dubey, “A novel telerobotic method for human-in-the-loop assisted grasping based on intention recognition,” in 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 4762–4769, Hong Kong, 2014.
  22. J. H. Han, S. J. Lee, and J. H. Kim, “Behavior hierarchy-based affordance map for recognition of human intention and its application to human’ robot interaction,” IEEE Transactions on Human-Machine Systems, vol. 46, no. 5, pp. 1–15, 2016. View at Google Scholar
  23. J. Huang, W. Huo, W. Xu, S. Mohammed, and Y. Amirat, “Control of upper-limb power-assist exoskeleton using a human-robot interface based on motion intention recognition,” IEEE Transactions on Automation Science and Engineering, vol. 12, no. 4, pp. 1257–1270, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. H. C. Sang, J. M. Lee, S. J. Kim, Y. Hwang, and J. An, “Intention recognition method for sit-to-stand and stand-to-sit from electromyogram signals for overground lower-limb rehabilitation robots,” in 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 418–421, Busan, South Korea, 2015. View at Publisher · View at Google Scholar · View at Scopus
  25. T. B. Sheridan, Telerobotics, Automation and Human Supervisory Control, The MIT Press, Cambridge, 1992. View at Publisher · View at Google Scholar · View at Scopus
  26. S. S. Nudehi, R. Mukherjee, and M. Ghodoussi, “A shared-control approach to haptic interface design for minimally invasive telesurgical training,” IEEE Transactions on Control Systems Technology, vol. 13, no. 4, pp. 588–592, 2005. View at Publisher · View at Google Scholar · View at Scopus
  27. F. Chen, P. Di, J. Huang, H. Sasaki, and T. Fukuda, “Evolutionary artificial potential field method based manipulator path planning,” in 2009 International Symposium on Micro-NanoMechatronics and Human Science, pp. 92–97, Nagoya Japan, 2009. View at Publisher · View at Google Scholar
  28. H. T. Trieu, H. T. Nguyen, and K. Willey, “Shared control strategies for obstacle avoidance tasks in an intelligent wheelchair,” in 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4254–4257, Vancouver, BC, Canada, 2008. View at Publisher · View at Google Scholar
  29. W. G. Huh and S. B. Cho, “Optimal partial filters of EEG signals for shared control of vehicle,” in 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 290–293, Fukuoka, Japan, 2015.
  30. W. X. Xu, J. Huang, Y. J. Wang, C. J. Tao, and L. Cheng, “Reinforcement learning-based shared control for walking-aid robot and its experimental verification,” Advanced Robotics, vol. 29, no. 22, pp. 1463–1481, 2015. View at Publisher · View at Google Scholar · View at Scopus
  31. S. Sathish, K. Nithyakalyani, S. Vinurajkumar, C. Vijayalakshmi, and J. Sivaraman, “Control of robotic wheel chair using EMG signals for paralysed persons,” Indian Journal of Science and Technology, vol. 9, no. 1, 2016. View at Publisher · View at Google Scholar · View at Scopus
  32. A. Accogli, L. Grazi, S. Crea et al., “EMG-based detection of user’s intentions for human-machine shared control of an assistive upper-limb exoskeleton,” in Wearable Robotics: Challenges and Trends, pp. 181–185, Springer International Publishing, Cham, 2017. View at Google Scholar
  33. W. Hong, Y. T. Tian, Z. Dong, and M. Zhou, “Extracting features from local environment for intelligent robot system,” Robot, vol. 25, no. 3, 2003. View at Google Scholar