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Design and modeling of a cable-driven articulated robot intended to conduct lower limb recovery training

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Abstract

The proposed cable-driven articulated robot (CDAR) is composed mainly of an articulated-multibody system which is connected to three cables, and can perform sitting type rehabilitation therapies for lower extremity. Adding torsion springs to the rotary joints of CDAR ensures that all cables are always in tension. The arrangement of kinematics in the sagittal plane is presented for motion control and the corresponding optimization problems with strongly nonlinear characteristics are developed. After, the particle swarm optimization method is used to determine the dimensions of the mechanism and the optimum attachment position of the cable. Further, the system dynamics is derived based on Newton-Euler method to help analyze the force profiles and the stiffness of the robot is described when considering compliant impacts of the structural joints. Finally, experimental results demonstrated that the designed robot can fulfill lower limb rehabilitation with advantages such as being lightweight, low-cost, and having simple transmission.

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References

  1. E. E. Kaiser and D. W. Franklin, Large animal ischemic stroke models: replicating human stroke pathophysiology, Neural Regeneration Research, 15 (8) (2020) 1377–1387.

    Article  Google Scholar 

  2. G. J. Hankey, Stroke, Lancet, 389 (10069) (2017) 641–654.

    Article  Google Scholar 

  3. S. L. James et al., Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990–2016: a systematic analysis for the global burden of disease study 2016, The Lancet Neurology, 18 (1) (2019) 56–87.

    Article  Google Scholar 

  4. M. Pohl et al., Repetitive locomotor training and physiotherapy improve walking and basic activities of daily living after stroke: a single-blind, randomized multicentre trial (DEutsche GAngtrainerStudie, DEGAS), Clinical Rehabilitation, 21 (1) (2007) 17–27.

    Article  MathSciNet  Google Scholar 

  5. E. Akdoan and M. A. Adli, The design and control of a therapeutic exercise robot for lower limb rehabilitation: physiotherabot, Mechatronics, 21 (3) (2011) 509–522.

    Article  Google Scholar 

  6. F. M. Lim, R. Foong and H. Yu, A supine gait training device for stroke rehabilitation, ASME. J. Med. Devices, 8 (2) (2014) 020926.

    Article  Google Scholar 

  7. V. Monaco et al., Design and evaluation of neurobike: a neurorehabilitative platform for bedridden post-stroke patients, IEEE Transactions on Neural System and Rehabilitation Engineer, 20 (6) (2012) 845–852.

    Article  Google Scholar 

  8. M. Bouri, B. L. Gall and R. Clavel, A new concept of parallel robot for rehabilitation and fitness: the lambda, IEEE International Conference on Robotics and Biomimetics, Bangkok, Thailand (2009) 2503–2508.

  9. C. Schmitt et al., The motion maker™: a rehabilitation system combining an orthosis with closed-loop electrical muscle stimulation, International Workshop on Functional Electrical Stimulation, Vienna, Austria (2004) 117–120.

  10. G. Colombo et al., Treadmill training of paraplegic patients using a robotic orthosis, Journal of Rehabilitation Research and Development, 37 (6) (2000) 693–700.

    Google Scholar 

  11. S. Freivogel et al., Gait training with the newly developed ‘lokohelp’-system is feasible for non-ambulatory patients after stroke, spinal cord and brain injury, A feasibility study, Brain Injury, 22 (7–8) (2008) 625–632.

    Article  Google Scholar 

  12. J. Skidmore and P. Artemiadis, Leg muscle activation evoked by floor stiffness perturbations: a novel approach to robot-assisted gait rehabilitation, IEEE International Conference on Robotics and Automation, Seattle, USA (2015) 6463–6468.

  13. M. Pietrusinski et al., Gait rehabilitation therapy using robot generated force fields applied at the pelvis, Proc. IEEE Haptics Symposium, Waltham, USA (2010) 401–407.

  14. G. Puyuelo-Quintana et al., A new lower limb portable exoskeleton for gait assistance in neurological patients: a proof of concept study, Journal of NeuroEngineering and Rehabilitaion, 17 (1) (2020) 1–16.

    Google Scholar 

  15. A. Roy et al., Robot-aided neurorehabilitation: a novel robot for ankle rehabilitation, IEEE Transactions on Robotics, 25 (3) (2009) 569–582.

    Article  Google Scholar 

  16. W. Q. W et al., A novel leg orthosis for lower limb rehabilitation robots of the sitting/lying type, Mechanism and Machine Theory, 74 (2014) 337–353.

    Article  Google Scholar 

  17. S. K. Banala, A. Kulpe and S. K. Agrawal, A powered leg orthosis for gait rehabilitation of motor-impaired patients, IEEE International Conference on Robotics and Automation, Rome (2007) 4140–4145.

  18. J. K. Mohanta et al., Kinematic analysis of a passive sitting/lying type lower limb rehabilitation robot, International Conference on Machines and Mechanisms, Kanpur, India (2015) 1–12.

  19. M. Vasanthakumar et al., Design and robust motion control of a planar 1P-2P RP hybrid manipulator for lower limb rehabilitation applications, Journal of Intelligent and Robotic System, 96 (1) (2019) 17–30.

    Article  Google Scholar 

  20. R. Rupp et al., Motion Therapy@Home-a robotic device for automated locomotion therapy at home, IEEE International Conference on Rehabilitation Robotics, Kyoto, Japan (2009) 395–400.

  21. A. M. Barbosa, J. C. M. Carvalho and R. S. Goncalves, Cable-driven lower limb rehabilitation robot, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40 (5) (2018) 1–11.

    Article  Google Scholar 

  22. A. Alamdari and V. Krovi, Design and analysis of a cable-driven articulated rehabilitation system for gait training, Journal of Mechanisms and Robotics, 8 (5) (2016) 051018.

    Article  Google Scholar 

  23. A. Alamdari, R. Haghighi and V. Krovi, Stiffness modulation in an elastic articulated-cable leg-orthosis emulator: theory and experiment, IEEE Transactions on Robotics, 34 (5) (2018) 1266–1279.

    Article  Google Scholar 

  24. L. W. Tang and P. S. Shi, Design and analysis of a gait rehabilitation cable robot with pairwise cable arrangement, Journal of Mechanical Science and Technology, 35 (7) (2021) 3161–3170.

    Article  Google Scholar 

  25. H. R. Chu, S. J. Chiou and I. H. Li, Design, development, and control of a novel upper-limb power-assist exoskeleton system driven by pneumatic muscle actuators, Actuators, 11 (8) (2022) 231.

    Article  Google Scholar 

  26. M. J. Johnson and H. Schmidt, Robot assisted neurological rehabilitation at home: motivational aspects and concepts for tele-rehabilitation, Public Health Forum, 17 (4) (2009) 8–10.

    Article  Google Scholar 

  27. G. L. Yang et al., Kinematic design of an anthropomimetic 7-DOF cable-driven robotic arm, Frontiers of Mechanical Engineering, 6 (1) (2011) 45–60.

    Google Scholar 

  28. A. Macor and A. Rossetti, Optimization of hydro-mechanical power split transmissions, Mechanism and Machine Theory, 46 (12) (2011) 1901–1919.

    Article  Google Scholar 

  29. Y. C. Lin, M. J. Wang and E. M. Wang, The comparisons of anthropometric characteristics among four peoples in East Asia, Applied Ergonomics, 35 (2) (2004) 173–178.

    Article  Google Scholar 

  30. S. K. S. Fan, Y. C. Liang and E. A. Zahara, A genetic algorithm and a particle swarm optimizer hybridized with Nelder-Mead simplex search, Computers and Industrial Engineering, 50 (4) (2006) 401–425.

    Article  Google Scholar 

  31. J. Kennedy and R. Eberhart, Particle swarm optimization, IEEE International Conference on Neural Network, Perth, Australia (1995) 1942–1948.

  32. R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, Proc. International Symposium on Micro Machine and Human Science, Nagoya, Japan (1995) 39–43.

  33. C. T. Lee and C. C. Lee, On a hybrid particle swarm optimization method and its application in mechanism design, Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, 228 (15) (2014) 2844–2857.

    Article  Google Scholar 

  34. Y. Zhao et al., Optimum selection of mechanism type for heavy manipulators based on particle swarm optimization method, Chinese Journal of Mechanical Engineering, 27 (4) (2013) 139–146.

    Google Scholar 

  35. Z. Liu, H. Li and P. Zhu, Diversity enhanced particle swarm optimization algorithm and its application in vehicle lightweight design, Journal of Mechanical Science and Technology, 33 (2) (2019) 695–709.

    Article  Google Scholar 

  36. R. McDougall and S. Nokleby, Synthesis of grashof four-bar mechanisms using particle swarm optimization, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Brooklyn, New York (2008) 1–5.

  37. V. Savsani, R. V. Rao and D. P. Vakharia, Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms, Mechanism and Machine Theory, 45 (3) (2010) 531–541.

    Article  MATH  Google Scholar 

  38. G. E. O. Giacaglia and W. de Queiroz Lamas, Notes on newton–euler formulation of robotic manipulators, Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, 226 (1) (2012) 61–71.

    Google Scholar 

  39. X. X. Qu et al., A novel design of torsion spring-connected nonlinear stiffness actuator based on cam mechanism, Journal of Mechanical Design, 144 (8) (2022) 083303.

    Article  Google Scholar 

  40. S. Plagenhoef, F. G. Evans and T. Abdelnour, Anatomical data for analyzing human motion, Research Quarterly for Exercise and Sport, 54 (2) (1983) 169–178.

    Article  Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation (NSF) of China (51875167) as well as the NSF of Hebei Province (E2018202114).

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Authors and Affiliations

  1. School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China

    Dongxing Cao, Xiangxu Qu & Chunlei Wang

Authors
  1. Dongxing Cao
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  2. Xiangxu Qu
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  3. Chunlei Wang
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Corresponding author

Correspondence to Dongxing Cao.

Additional information

Dongxing Cao received the Ph.D. degree from Hebei University of Technology. He is currently a Professor in Hebei University of Technology. His research interests include stair climbing wheelchairs, rehabilitation robots and parallel robot.

Xiangxu Qu is currently studying for a doctoral degree. He received his B.S. and M.S. degrees from North China University of Science and Technology in 2013 and 2016, respectively. His research interests include nonlinear stiffness actuators and cable-driven rehabilitation robots.

Chunlei Wang is currently a Ph.D. candidate at the Department of Mechanical Engineering, Hebei University. He received his B.S. and M.S. degrees from Hebei University of Technology. His research interests include nonlinear stiffness actuators and cable-driven rehabilitation robots.

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Cao, D., Qu, X. & Wang, C. Design and modeling of a cable-driven articulated robot intended to conduct lower limb recovery training. J Mech Sci Technol 37, 2581–2592 (2023). https://doi.org/10.1007/s12206-023-0433-6

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  • DOI: https://doi.org/10.1007/s12206-023-0433-6

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