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Designing Bionic Path Robots to Minimize the Metabolic Cost of Human Movement

  • Jing Fang
  • Jianping YuanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11740)

Abstract

Despite great successes in wearable robotics, designing assistive devices that are portable like clothes and could reduce the metabolic cost of human movement remains a substantial challenge. Inspired by the driving mechanism of human body, we proposed a class of bionic path (BP) robots in this paper. The BP robots could assist human limbs along arbitrary paths predesigned on the limb surface, and could be driven by various soft path actuators (the active, quasi-active or passive). Additionally, to minimize the metabolic cost of human movement, a human-in-the-loop optimization method for designing BP robots was also developed in this paper. As practical examples, 18 BP robots with 3 different types of path actuators along 6 different paths were designed to help people reduce their metabolic cost during walking. Each of the 18 robots combined with the human body separately to form a coupled dynamic system. The metabolic power, muscle excitations and optimal control profiles for these coupled systems were analyzed using the simulation-based method. Simulation results showed that, 13 of these 18 BP robots decreased the whole-body metabolic energy consumption, and the maximum reduction was up to 55% relative to the unassisted scenarios.

Keywords

Wearable robotics Human-in-the-loop design Biomechanics Human energetics 

References

  1. 1.
    Rodman, P.S., McHenry, H.M.: Bioenergetics and the origin of hominid bipedalism. Am. J. Phys. Anthropol. 52(1), 103–106 (2016)CrossRefGoogle Scholar
  2. 2.
    Uchida, T.K., Seth, A., Pouya, S., et al.: Simulating ideal assistive devices to reduce the metabolic cost of running. PLoS ONE 11(9), e0163417 (2016)CrossRefGoogle Scholar
  3. 3.
    Dembia, C.L., Silder, A., Uchida, T.K., et al.: Simulating ideal assistive devices to reduce the metabolic cost of walking with heavy loads. PLoS ONE 12(7), e0180320 (2017)CrossRefGoogle Scholar
  4. 4.
    Viteckova, S., Kutilek, P., Jirina, M.: Wearable lower limb robotics: a review. Biocybern. Biomed. Eng. 33(2), 96–105 (2013)CrossRefGoogle Scholar
  5. 5.
    Bogue, R.: Exoskeletons and robotic prosthetics: a review of recent developments. Ind. Robot. 36(5), 421–427 (2009)CrossRefGoogle Scholar
  6. 6.
    Jansen, J., Richardson, B., Pin, F., et al.: Exoskeleton for soldier enhancement systems feasibility study. University of North Texas Libraries, Digital Library (2000)Google Scholar
  7. 7.
    Gopura, R.A.R.C., Kiguchi, K., Bandara, D.S.V.: A brief review on upper extremity robotic exoskeleton systems. In: 2011 6th International Conference on Industrial and Information Systems, Kandy, pp. 346–351 (2011)Google Scholar
  8. 8.
    Singer, J.C.: Lamontagne M, The effect of functional knee brace design and hinge misalignment on lower limb joint mechanics. Clin. Biomech. 23(1), 52–59 (2008)CrossRefGoogle Scholar
  9. 9.
    Browning, R.C., Modica, J.R., Kram, R., et al.: The effects of adding mass to the legs on the energetics and biomechanics of walking. Med. Sci. Sports Exerc. 39(3), 515–525 (2007)CrossRefGoogle Scholar
  10. 10.
    Phillips, B., Zhao, H.X.: Predictors of assistive technology abandonment. Assist. Technol. 5(1), 36–45 (1993)CrossRefGoogle Scholar
  11. 11.
    Park, Y.L., Chen, B.R., Pérez-Arancibia, N.O., et al.: Design and control of a bio-inspired soft wearable robotic device for ankle-foot rehabilitation. Bioinspir. Biomim. 9(1), 016007 (2014)CrossRefGoogle Scholar
  12. 12.
    Kang, B.B., Lee, H., In, H., et al.: Development of a polymer-based tendon-driven wearable robotic hand. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, pp. 3750–3755 (2016)Google Scholar
  13. 13.
    Park, Y., Chen, B., Majidi, C., et al.: Active modular elastomer sleeve for soft wearable assistance robots. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, pp. 1595–1602 (2012)Google Scholar
  14. 14.
    Quinlivan, B.T., Lee, S., Malcolm, P., et al.: Assistance magnitude vs. metabolic cost reductions for a tethered multiarticular soft exosuit. Sci. Robot. 2, eaah4416 (2017)CrossRefGoogle Scholar
  15. 15.
    Ding, Y., Kim, M., Kuindersma, S.: Human-in-the-loop optimization of hip assistance with a soft exosuit during walking, Sci. Robot. 3, eaah5438 (2018)CrossRefGoogle Scholar
  16. 16.
    Awad, L.N., Bae, J., O’Donnell, K., et al.: A soft robotic exosuit improves walking in patients after stroke. Sci. Transl. Med. 9, eaai9084 (2017)CrossRefGoogle Scholar
  17. 17.
    Seth, A., Hicks, J.L., Uchida, T.K., et al.: OpenSim: simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLoS Comput. Biol. 14(7), e1006223 (2018)CrossRefGoogle Scholar
  18. 18.
    Delp, S.L., Anderson, F.C., Arnold, A.S., et al.: OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 55, 1940–1950 (2007)CrossRefGoogle Scholar
  19. 19.
    Umberger, B.R., Gerritsen, K.G.M., Martin, P.E.: A model of human muscle energy expenditure. Comput. Methods Biomech. Biomed. Eng. 6(2), 99–111 (2003)CrossRefGoogle Scholar
  20. 20.
    Thelen, D.G., Anderson, F.C.: Using computed muscle control to generate forward dynamic simulations of human walking from experimental data. J. Biomech. 39(6), 1107–1115 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Science and Technology on Aerospace Flight Dynamics LaboratoryNorthwestern Polytechnical UniversityXi’anChina

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