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Development of electric cart for improving walking ability — application of control theory to assistive technology

  • Jinhua SheEmail author
  • Yasuhiro OhyamaEmail author
  • Min WuEmail author
  • Hiroshi HashimotoEmail author
Moop

Abstract

This paper explains the development of an electric cart that helps the elderly maintain or improve their physical strength. Unlike commercially available ones, it has a pedal unit that provides some exercise for a user in training his lower limbs. An impedance model describes the feeling of pushing the pedals. The largest pedal load is determined based on a pedaling experiment. An H controller is designed for each of the largest pedal load and virtually no load. A control law, which is based on the concept of dynamic parallel distributed compensation, is designed using the rating of perceived exertion of a driver as a criterion to choose a pedal load between the largest and almost zero. Five university students and twelve elderly people participated experiments to verify the system design and the validity of the system.

Keywords

aging Borg’s scale dynamic parallel distributed compensation electrical cart H control Karvonen formula lower limbs pedaling rating of perceived exertion 

Notes

Acknowledgements

This work was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI (Grant Nos. 18560259, 26350673), and partially by JSPS KAKENHI (Grant No. 16H02883). This work was also supported by National Natural Science Foundation of China (Grant Nos. 61473313, 61210011), Hubei Provincial Natural Science Foundation of China (Grant No. 2015CFA010), and the 111 Project of China (Grant No. B17040).

Supplementary material

11432_2017_9261_MOESM1_ESM.pdf (2.5 mb)
Development of Electric Cart for Improving Walking Ability

Supplementary material, approximately 27.1 MB.

Supplementary material, approximately 304 MB.

References

  1. 1.
    Okamura Y. Mainstreaming gender and aging in the SDGs. Ambassador and Deputy Representative of Japan to the United Nations at a Side Event to the High Level Political Forum, 2016. http://www.un.emb-japan.go.jp/jp/ statements/okamura071316.htmlGoogle Scholar
  2. 2.
    Cabinet Office, Goverment of Japan. Annual Report on the Aging Society: 2016 (Summary) (in Japanese). 2017. http://www8.cao.go.jp/kourei/whitepaper/w-2013/zenbun/25pdf index.htmlGoogle Scholar
  3. 3.
    She J, Ohyama Y, Kobayashi H. Master-slave electric cart control system for maintaining/improving physical strength. IEEE Trans Robotics, 2006, 22: 481–490CrossRefGoogle Scholar
  4. 4.
    She J, Yokota S, Du E Y. Automatic heart-rate-based selection of pedal load and control system for electric cart. Mechatronics, 2013, 23: 279–288CrossRefGoogle Scholar
  5. 5.
    She J, Wu F, Mita T, et al. Design of a new lower-limb rehabilitation machine. J Adv Comput Intel Intel Inform, 2017, 23: 409–416CrossRefGoogle Scholar
  6. 6.
    Satoh M. Ningen Kougaku Kizyun Suuchi Suusiki Binran (Handbook of Ergonomic Standards, Statistics, and Numerical Formulae) (in Japanese). Tokyo: Gihodo Shuppan Company Limited, 1994Google Scholar
  7. 7.
    Hill D C, Ethans K D, Macleod D A, et al. Exercise stress testing in subacute stroke patients using a combined upperand lower-limb ergometer. Arch Phys Med Rehabil, 2005, 86: 1860–1866CrossRefGoogle Scholar
  8. 8.
    Borg G. Borg’s Perceived Exertion and Pain Scales. Champaigne: Human Kinetics, 1998Google Scholar
  9. 9.
    Salvendy G. Handbook of Human Factors and Ergonomics. 2nd ed. New York: Willy, 1997Google Scholar
  10. 10.
    Japan International Cooperation Agency (JICA). JICA Report: Final Report on Information Collection and Confirmation Investigation of Aging Problem in China. 2014. http://openjicareport.jica.go.jp/211/211/21110512153276.htmlGoogle Scholar
  11. 11.
    Peng L, Hou Z G, Peng L, et al. Robot assisted rehabilitation of the arm after stroke: prototype eesign and clinical evaluation. Sci China Inf Sci, 2017, 60: 073201CrossRefGoogle Scholar
  12. 12.
    Hou Z, Zhao X, Cheng L, et al. Robot recent advances in rehabilitation robots and intelligent assistance systems (in Chinese). Acta Autom Sin, 2016, 42: 1765–1779Google Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of EngineeringTokyo University of TechnologyTokyoJapan
  2. 2.School of AutomationChina University of GeosciencesWuhanChina
  3. 3.Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsWuhanChina
  4. 4.Master Program of Innovation for Design & EngineeringAdvanced Institute of Industrial TechnologyTokyoJapan

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