Skip to main content
SpringerLink
Log in

Design and analysis of plantar hydraulic control device for body weight support treadmill training

  • Original Article
  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

The increase in the number of people with walking disabilities caused by aging, accidental injuries and diseases is an important social problem. Therefore, based on the center of gravity control theory, a plantar hydraulic weight loss walking training device with flexible elements is proposed, which helps patients achieve walking rehabilitation training through a cooperative weight loss method combining passive suspension and active center of gravity control. Firstly, the structure of the device is designed and the dynamic model is established. Then, the constitutive model of the flexible element is determined by uniaxial tensile test. Then, based on the hyper-elastic finite element simulation results of the plantar flexible element, combined with the constant load pressure test, the correctness of the experimental data and the feasibility of the plantar hydraulic control device scheme are verified. Finally, the landing buffer and weight loss gait experiments were carried out. The results show that the plantar flexible element has a buffering effect at the moment of heel touching the ground, and the unloading force fluctuation of the two gaits under the single support phase of the human body is weakened.

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. G. Kwakkel et al., Effects of robot-assisted therapy on upper limb recovery after stroke: A systematic review, Neurorehabilitation and Neural Repair, 22(2) (2008) 111–121.

    Article  PubMed  Google Scholar 

  2. E. Hoyer, R. Jshnsen and J. K. Stanghelle, Body weight supported treadmill training versus traditional training in patients dependent on walking assistance after stroke: a randomized controlled trial, Disability and Rehabilitation, 34(3) (2012) 210–219.

    Article  PubMed  Google Scholar 

  3. R. C. H. Kwakman et al., Steps to recovery: body weight-supported treadmill training for critically ill patients: a randomized controlled trial, Trials, 21(1) (2020) 409.

    Article  PubMed  PubMed Central  Google Scholar 

  4. R. Yamamoto et al., Effect of exoskeleton-assisted body weight-supported treadmill training on gait function for patients with chronic stroke: A scoping review, Journal of Neuroengineering and Rehabilitation, 19(1) (2022) 143.

    Article  PubMed  PubMed Central  Google Scholar 

  5. L. M. Wier, M. S. Hatcher and E. W. Triche, Effect of robotassisted versus conventional body-weight-supported treadmill training on quality of life for people with multiple sclerosis, Journal of Rehabilitation Research and Development, 48(4) (2011) 483–492.

    Article  PubMed  Google Scholar 

  6. M. Bernhardt, M. Frey and G. Colombo, Hybrid force-position control yields cooperative behaviour of the rehabilitation robot LOKOMAT, 2005 IEEE International Conf. on Rehabilitation Robotics (ICORR), Chicago, USA (2005) 536–539.

  7. M. Frey, G. Colombo and M. Vaglio, A novel mechatronic body weight support system, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(3) (2006) 311–321.

    Article  PubMed  Google Scholar 

  8. C. Wei et al., Surplus force control strategy of an active body-weight support training system, 14th International Conf. on Intelligent Robotics and Applications (ICIRA), Yantai, China (2021) 153–162.

  9. B. J. Guo, J. H. Han, X. P. Li and L. Yan, Human-robot interactive control based on reinforcement learning for gait rehabilitation training robot, International Journal of Advanced Robotic Systems, 16(2) (2019) 1–16.

    ADS  Google Scholar 

  10. T. P. Luu et al., Hardware development and locomotion control strategy for an over-ground gait trainer: nature-gaits, IEEE Journal of Translational Engineering in Health and Medicine, 2 (2014) 1–9.

    Article  ADS  Google Scholar 

  11. C. Chen et al., Disturbance observer-based patient-cooperative control of a lower extremity rehabilitation exoskeleton, International Journal of Precision Engineering and Manufacturing, 21(5) (2020) 957–968.

    Article  Google Scholar 

  12. J. Yoon et al., A 6-DOF gait rehabilitation robot with upper and lower limb connections that allows walking velocity updates on various terrains, IEEE-ASME Transactions on Mechatronics, 15(2) (2010) 201–215.

    Article  MathSciNet  Google Scholar 

  13. Z. Mu et al., Development of an improved rotational orthosis for walking with arm swing and active ankle control, Frontiers in Neurorobotics, 14 (2020) 1–14.

    Article  Google Scholar 

  14. Z. Mu et al., Admittance control of the ankle mechanism in a rotational orthosis for walking with arm swing, 2019 IEEE International Conf. on Rehabilitation Robotics (2019) 709–714.

  15. S. Hussein, H. Schmidt, S. Hesse and J. Kruger, Effect of different training modes on ground reaction forces during robot assisted floor walking and stair climbing, 2009 IEEE International Conf. on Rehabilitation Robotics, Kyoto, Japan (2009) 845–850.

  16. T. Qin et al., Dynamics analysis of the human-machine system of the assistive gait training robot, H. Yu, J. Liu, L. Liu, Z. Ju, Y. Liu and D. Zhou (eds.), Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science, Springer, Cham, 11745 (2019) 273–282.

    Google Scholar 

  17. T. Qin et al., Motion planning and experimental validation of a novel robotic device for assistive gait training, Y. Huang, H. Wu, H. Liu and Z. Yin (eds.), Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science, Springer, Cham, 10462 (2017) 290–300.

    Chapter  Google Scholar 

  18. J. Meuleman et al., Novel actuation design of a gait trainer with shadow leg approach, 13th IEEE International Conf. on Rehabilitation Robotics, Seattle, USA (2013) 1–8.

  19. J. Meuleman et al., LOPES II-design and evaluation of an admittance controlled gait training robot with shadow-leg approach, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(3) (2016) 352–363.

    Article  PubMed  Google Scholar 

  20. H. Vallery et al., Multidirectional transparent support for overground gait training, 2013 IEEE International Conf. on Rehabilitation Robotics, Seattle, USA (2013) 1–7.

  21. P. Sabetian et al., A 3 wire body weight support system for a large treadmill, 2017 IEEE International Conf. on Robotics and Automation, Singapore (2017) 498–503.

  22. J. Kim et al., A robotic treadmill system to mimic overground walking training with body weight support, Frontiers in Neurorobotics, 17 (2023) 1–11.

    Article  ADS  Google Scholar 

  23. N. Kataoka et al., Effects of partial body-weight support and functional electrical stimulation on gait characteristics during treadmill locomotion: pros and cons of saddle-seat-type body-weight support, 2017 IEEE International Conf. on Rehabilitation Robotics (2017) 381–386.

  24. L. W. Lee, I. H. Li and T. W. Liang, A proof of concept study for the design, manufacturing, and control of a mobile overground gait-training system, International Journal of Fuzzy Systems, 23(8) (2021) 2396–2416.

    Article  Google Scholar 

  25. M. R. Haghjoo et al., Mech-walker: A novel single-DOF linkage device with movable frame for gait rehabilitation, IEEE-Asme Transactions on Mechatronics, 26(1) (2021) 13–23.

    Article  Google Scholar 

  26. S. Noroozi et al., Therapeutic effects of an anti-gravity treadmill (AlterG) training on neuromuscular abnormalities associated with spasticity in children with cerebral palsy, 2020 IEEE International Conf. of the Engineering in Medicine and Biology Society (2020) 3856–3859.

  27. C. G. Lim, Effect of underwater treadmill gait training with water-jet resistance on balance and gait ability in patients with chronic stroke: A randomized controlled pilot trial, Frontiers in Neurology, 10 (2020) 1246.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Y. Stauffer et al., Pelvic motion implementation on the walktrainer, 2007 IEEE International Conf. on Robotics and Biomimetics, Sanya, China (2007) 133–138.

  29. F. Massaad et al., Reducing the energy cost of hemiparetic gait using center of mass feedback: A pilot study, Neurorehabilitation and Neural Repair, 24(4) (2009) 338–347.

    Article  PubMed  Google Scholar 

  30. Q. Lu et al., A new active body weight support system capable of virtually offloading partial body mass, IEEE-ASME Transactions on Mechatronics, 18(1) (2013) 11–20.

    Article  ADS  Google Scholar 

  31. J. H. Kim and G. H. Kim, Effect of rubber content on abrasion resistance and tensile properties of thermoplastic polyurethane (TPU)/rubber blends, Macromolecular Research, 22(5) (2014) 523–527.

    Article  CAS  Google Scholar 

  32. Z. M. Jia, Z. W. Guo and C. Q. Yuan, Effect of material hardness on water lubrication performance of thermoplastic polyurethane under sediment environment, Journal of Materials Engineering and Performance, 30(10) (2021) 7532–7541.

    Article  ADS  CAS  Google Scholar 

  33. B. Kim et al., A comparison among neo-hookean model, mooney-rivlin model, and ogden model for chloroprene rubber, International Journal of Precision Engineering and Manufacturing, 13(5) (2012) 759–764.

    Article  Google Scholar 

  34. S. M. Goh et al., Determination of the constitutive constants of non-linear viscoelastic materials, Mechanics of Time-Dependent Materials, 8(3) (2004) 255–268.

    Article  ADS  Google Scholar 

Download references

Acknowledgments

This research was funded by the National Key Research and Development Program (2019YFB1312500), China.

Author information

Authors and Affiliations

  1. Parallel Robot and Mechatronic System Laboratory of Hebei Province, Yanshan University, Qinhuangdao, 066004, China

    Hui Bian, Zihan Li, YaoYao Lan, Zihao Chen & Yu Zhang

  2. Key Laboratory of Advanced Forging & Stamping Technology and Science of Ministry of Education, Yanshan University, Qinhuangdao, 066004, China

    Hui Bian, Zihan Li, YaoYao Lan, Zihao Chen & Yu Zhang

Authors
  1. Hui Bian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zihan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. YaoYao Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zihao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zihan Li.

Ethics declarations

The experiment involving the participant in this article was reviewed and approved by the School of Mechanical Engineering of Yanshan University. The participant has signed a written informed consent.

Additional information

Hui Bian obtained his B.S. degree in School of Mechanical Engineering, Yanshan University, Qinhuangdao, Hebei Province, China in 2005. In 2011, he received his Ph.D. degree in mechanical engineering from Yanshan University. He is currently an Associate Professor at the School of Mechanical Engineering of Yanshan University. His main research directions are parallel robots, rehabilitation robots, etc.

Zihan Li received his B.S. degree in mechanical engineering from the Yanshan University, Qinhuangdao, China, in 2022, where he is currently pursuing his M.S. degree in mechatronics. His research interests include gait rehabilitation robot, flexible component simulation and principle of automatic control.

YaoYao Lan received his B.S. degree in environmental and chemical engineering from Yanshan University, Qinhuangdao, China, in 2020. In 2023, he received his M.S. degree from the School of Mechanical Engineering of Yanshan University. His research interests include lower limb rehabilitation robot and hyperelastic finite element analysis.

Zihao Chen received his B.S. degree in mechanical engineering from Hebei Agricultural University, Baoding, China, in 2022. He is currently studying for the M.S. degree in School of Mechanical Engineering, Yanshan University. His research interests include visual inspection technology and mechatronics technology.

Yu Zhang received his B.S. degree in mechanical engineering from Qilu University of Technology, Jinan, China, in 2020. He is currently studying for the M.S. degree in School of Mechanical Engineering, Yanshan University. His research interests include rehabilitation robotics and mechatronics technology.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bian, H., Li, Z., Lan, Y. et al. Design and analysis of plantar hydraulic control device for body weight support treadmill training. J Mech Sci Technol 38, 943–955 (2024). https://doi.org/10.1007/s12206-024-0139-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-024-0139-4

Keywords

Search

Navigation