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Gait pattern generator for control of a lower limb exoskeleton

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of the study was to propose a relatively simple central pattern generator (CPG) model, which can be used to control a lower limb exoskeleton. The mentioned generator and the simulation model of the human gait were developed based on experimental observations of the healthy volunteer's gait recorded using a motion tracking system. In order to reproduce the correct movements of the exoskeleton segments, time series of angles in the joints corresponding to the hip and knee joints were calculated based on tracing the trajectories generated by the CPG and the inverse kinematic relations. The proposed model can be implemented to control the lower limb (extremity) exoskeleton and assist various types of gait abnormality in patients with different motor dysfunction by means of changing the parameters of the control system. The presented experimental data, the developed gait simulation model, and the results of numerical simulations can be treated as guidelines for further improvement of the proposed model and its application in the exoskeleton control system. Although the study is mainly focused on rehabilitation applications, the proposed model is general and can be used also for other purposes such as control of bipedal and multi-legged robots.
Rocznik
Tom
Strony
art. no. 2018007
Opis fizyczny
Bibliogr. 16 poz., il. kolor., 1 rys., wykr.
Twórcy
autor
  • Lodz University of Technology, Department of Automation, Biomechanics and Mechatronics, 1/15 Stefanowskiego Str., 90-924 Lodz, Poland
  • Lodz University of Technology, Department of Automation, Biomechanics and Mechatronics, 1/15 Stefanowskiego Str., 90-924 Lodz, Poland
  • Lodz University of Technology, Department of Automation, Biomechanics and Mechatronics, 1/15 Stefanowskiego Str., 90-924 Lodz, Poland
Bibliografia
  • 1. K. Gregorczyk, A. Adams, P. O’Donovan, J. Schiffman, C. Bensel, Biomechanical and metabolic implications of wearing a powered exoskeleton to carry a backpack load, American Society of Biomechanics 2012 Proceedings, August 15-18, 2012, Gainesville (Florida) 2 pages.
  • 2. S. Banala, S. Agrawal, S. Scholz, Active leg exoskeleton (ALEX) for gait rehabilitation of motor-impaired patients, Proceedings of the 10th International Conference on Rehabilitation Robotics, June 12-15, 2007, Noordwijk, The Netherlands, 401 - 407.
  • 3. S. Agrawal, S. Banala, A. Fattah, V. Sangwan, V. Krishnamoorthy, J. Scholz, H. Wei-Li, Assessment of motion of a swing leg and gait rehabilitation with a gravity balancing exoskeleton, IEEE T. Neur. Sys. Reh., 15 (2007) 410 - 420.
  • 4. M. Petrarca, F. Patanè, S. Rossi, S. Carniel, P. Cappa, E. Castelli, A new robotic exoskeleton for gait recovery, Gait Posture, 40 (2014) 26 - 27.
  • 5. J. F. Veneman, R. Kruidhof, E.E.G. Hekman, R. Ekkelenkamp, E.H.F. Van Asseldonk, H. Van Der Kooij, Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation, IEEE T. Neur. Sys. Reh., 15 (2007) 379 - 386.
  • 6. A. Ferrari, M. Benedetti, E. Pavan, C. Frigo, D. Bettinelli, M. Rabuffetti, P. Crenna, A. Leardini, Quantitative comparison of five current protocols in gait analysis, Gait Posture, 28 (2008) 207 - 216.
  • 7. L. Donga, F. Zhu, X. Jin, M. Suresh, B. Jianga, G. Sevagan, Y. Cai, G. Lia, K. Yang, Blast effect on the lower extremities and its mitigation: A computational study, J. Mech. Behav. Biomed., 28 (2013) 111 - 124.
  • 8. T. Clark, D. Hawkins, Are fixed limb inertial models valid for dynamic simulations of human movement?, J. Biomech., 43 (2010) 2695 - 2701.
  • 9. L. Zach, S. Konvickova, P. Ruzicka, Investigation of in-vivo hinge knee behaviour using a dynamic finite element model of the lower limb, Comput. Method. Biomec., 15 (2012) 326 - 327.
  • 10. T. Zielinska, C-M. Chew, P. Kryczka, T. Jargilo, Robot gait synthesis using the scheme of human motions skills development, Mech. Mach. Theory, 44 (2009) 541 - 558.
  • 11. H. Kazerooni, R. Steger, L. Huang, Hybrid control of the berkeley lower extremity exoskeleton (BLEEX), Int. J. Robot. Res., 25 (2006) 561 - 573.
  • 12. T. P. Luu, K. H. Low, X. Qu, H. B. Lim, K. H. Hoon, An individual-specific gait pattern prediction model based on generalized regression neural networks, Gait Posture, 39 (2014) 443 - 448.
  • 13. D. Pan, F. Gao, Y. Miao, R. Cao, Co-simulation research of a novel exoskeleton-human robot system on humanoid gaits with fuzzy-PID/PID algorithms, Adv. Eng. Softw., 79 (2015) 36 - 46.
  • 14. A. Cohen, P. Holmes, R. Rand, The nature of the coupling between segmental oscillators of the lamprey spinal generator or locomotion: a mathematic model, J. Math. Biol., 13 (1982) 345 - 369.
  • 15. D. Grzelczyk, P. Biesiacki, J. Mrozowski, J. Awrejcewicz, Dynamic simulation of a novel "broomstick" human forward fall model and finite element analysis of the radius under the impact force during fall, J. Theor. Appl. Mech., 56 (2018) 239 - 253.
  • 16. D. Grzelczyk, B. Stańczyk, J. Awrejcewicz, Prototype, control system architecture and controlling of the hexapod legs with nonlinear stick-slip vibrations, Mechatronics, 37 (2016) 63 - 78.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-52c4ba8f-0109-4f25-975d-91010c26fbf9
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