Skip to main content
Log in

Optimization-based motor control of a Paralympic wheelchair athlete

  • Original Article
  • Published:
Sports Engineering Aims and scope Submit manuscript

Abstract

The following research approximated how the central nervous system of Paralympic wheelchair athletes resolve kinematic redundancies during upper-limb movements. A multibody biomechanical model of a tetraplegic Paralympic athlete was developed using subject-specific body segment parameters. The angular joint kinematics throughout a specified Paralympic sport movement (i.e., wheelchair curling) were experimentally measured using inertial measurement units. The motor control system of the Paralympian was mathematically modelled and simulated using forward dynamics optimization. The predicted kinematics from different optimization objective functions (i.e., minimizing resultant joint moments, mechanical joint power, and angular joint velocities and accelerations) were compared with those experimentally measured throughout the wheelchair curling movement. Of the optimization objective functions under consideration, minimizing angular joint accelerations produced the most accurate predictions of the kinematic trajectories (i.e., characterized with the lowest overall root mean square deviations) and the shortest optimization computation time. The implications of these control findings are discussed with regards to optimal wheelchair design through predictive dynamic simulations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Zatsiorsky VM (1998) Kinematics of human motion. Human Kinetics, Champaign

    Google Scholar 

  2. Bernstein N (1967) The co-ordination and regulation of movements. Pergamon Press, New York

    Google Scholar 

  3. Kirshblum SC et al (2011) International standards for neurological classification of spinal cord injury (revised 2011). J Spinal Cord Med 34:535–546. https://doi.org/10.1179/204577211X13207446293695

    Article  Google Scholar 

  4. Prilutsky B et al (2011) Motor control and motor redundancy in the upper extremity: implications for neurorehabilitation. Top Spinal Cord Injury Rehabil 17:7–15. https://doi.org/10.1310/sci1701-07

    Article  Google Scholar 

  5. Mehrabi N, Razavian RS, McPhee J (2015) A physics-based musculoskeletal driver model to study steering tasks. ASME J Comput Nonlinear Dyn. https://doi.org/10.1115/1.4027333

    Google Scholar 

  6. Popovic D, Oguztoreli MN, Stein RB (1995) Optimal control for an above-knee prosthesis with two degrees of freedom. J Biomech 28:89–98. https://doi.org/10.1016/0021-9290(95)80010-7

    Article  Google Scholar 

  7. Sha D, Thomas JS (2015) An optimisation-based model for full-body upright reaching movements. Comput Methods Biomech Biomed Eng 18:847–860. https://doi.org/10.1080/10255842.2013.850675

    Article  Google Scholar 

  8. Shourijeh MS, McPhee J (2014) Forward dynamic optimization of human gait simulations: a global parameterization approach. ASME J Comput Nonlinear Dyn. https://doi.org/10.1115/1.4026266

    Google Scholar 

  9. Shourijeh MS, Mehrabi N, McPhee J (2017) Forward static optimization in dynamic simulation of human musculoskeletal systems: a proof-of-concept study. ASME J Comput Nonlinear Dyn. https://doi.org/10.1115/1.4036195

    Google Scholar 

  10. Shoushtari AL (2013) What strategy central nervous system uses to perform a movement balanced? Biomechatronical simulation of human lifting. Appl Bionics Biomech 10:113–124. https://doi.org/10.3233/ABB-130075

    Article  Google Scholar 

  11. De Groote F, Kinney AL, Rao AV, Fregly BJ (2016) Evaluation of direct collocation optimal control problem formulations for solving the muscle redundancy problem. Ann Biomed Eng. https://doi.org/10.1007/s10439-016-1591-9

    Google Scholar 

  12. Lin YC, Pandy MG (2017) Three-dimensional data-tracking dynamic optimization simulations of human locomotion generated by direct collocation. J Biomech. https://doi.org/10.1016/j.jbiomech.2017.04.038

    Google Scholar 

  13. Porsa S, Lin YC, Pandy MG (2016) Direct methods for predicting movement biomechanics based upon optimal control theory with implementation in OpenSim. Ann Biomed Eng 44:2542–2557. https://doi.org/10.1007/s10439-015-1538-6

    Article  Google Scholar 

  14. Razavian RS, Mehrabi N, McPhee J (2015) A model-based approach to predict muscle synergies using optimization: application to feedback control. Front Comput Neurosci. https://doi.org/10.3389/fncom.2015.00121

    Google Scholar 

  15. Mehrabi N, Razavian RS, Ghannadi B, McPhee J (2016) Predictive simulation of reaching moving targets using nonlinear model predictive control. Front Comput Neurosci. https://doi.org/10.3389/fncom.2016.00143

    Google Scholar 

  16. Roetenberg D (2006) Inertial and magnetic sensing of human motion. PhD Dissertation, University of Twente

  17. Cloete T, Scheffer C (2010) Repeatability of an off-the-shelf, full body inertial motion capture system during clinical gait analysis. Annu Int Conf IEEE Eng Med Biol Soc. https://doi.org/10.1109/IEMBS.2010.5626196

    Google Scholar 

  18. Zhang JT, Novak AC, Brouwer B, Li Q (2013) Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics. Physiol Meas 34:63–69. https://doi.org/10.1088/0967-3334/34/8/N63

    Article  Google Scholar 

  19. Laschowski B, McPhee J (2016) Body segment parameters of Paralympic athletes from dual-energy X-ray absorptiometry. Sports Eng 19:155–162. https://doi.org/10.1007/s12283-016-0200-3

    Article  Google Scholar 

  20. Laschowski B, McPhee J (2016) Quantifying body segment parameters using dual-energy X-ray absorptiometry: a Paralympic wheelchair curler case report. Procedia Eng 147:163–167. https://doi.org/10.1016/j.proeng.2016.06.207

    Article  Google Scholar 

  21. Maeno N (2014) Dynamics and curl ratio of a curling stone. Sports Eng 17:33–41. https://doi.org/10.1007/s12283-013-0129-8

    Article  Google Scholar 

  22. Lebiedowska MK (2006) Dynamic properties of human limb segments. In: Karwowski W (ed) International encyclopaedia of ergonomics and human factors, 2nd edn. CRC Press, Boca Raton, pp 315–319

    Google Scholar 

  23. Rapoport S, Mizrahi J, Kimmel E, Verbitsky O, Isakov E (2003) Constant and variable stiffness and damping of the leg joints in human hopping. ASME J Biomech Eng 125:507–514. https://doi.org/10.1115/1.1590358

    Article  Google Scholar 

  24. Laschowski B, Mehrabi N, McPhee J (2016) Inverse dynamics modelling of Paralympic wheelchair curling. J Appl Biomech 33:294–299. https://doi.org/10.1123/jab.2016-0143

    Article  Google Scholar 

  25. Patterson MA, Rao AV (2012) GPOPS II: a MATLAB software for solving multiple-phase optimal control problems using hp-adaptive Gaussian quadrature collocation methods and sparse nonlinear programming. ACM Trans Math Softw 41:1–37. https://doi.org/10.1145/2558904

    Article  MathSciNet  Google Scholar 

  26. Ackermann M, Van Den Bogert AJ (2010) Optimality principles for model-based prediction of human gait. J Biomech 43:1055–1060. https://doi.org/10.1016/j.jbiomech.2009.12.012

    Article  Google Scholar 

  27. Miller RH, Hamill J (2015) Optimal footfall patterns for cost minimization in running. J Biomech 48:2858–2864. https://doi.org/10.1016/j.jbiomech.2015.04.019

    Article  Google Scholar 

  28. Hollerbach JM, Suh KC (1987) Redundancy resolution of manipulators through torque optimization. IEEE J Robotics Autom RA-3:308–316. https://doi.org/10.1109/jra.1987.1087111

    Article  Google Scholar 

  29. Parnianpour A, Wang JL, Shirazi-Adl A, Khayatian B, Lafferriere G (1999) Computation method for simulation of trunk motion: towards a theoretical based quantitative assessment of trunk performance. Biomed Eng Appl Basis Commun 11:27–39

    Google Scholar 

  30. Biegler LT, Zavala VM (2008) Large-scale nonlinear programming using IPOPT: an integrating framework for enterprise-wide optimization. Comput Chem Eng 33:575–582. https://doi.org/10.1016/j.compchemeng.2008.08.006

    Article  Google Scholar 

  31. Xiang Y, Arora JS, Abdel-Malek K (2010) Physics-based modeling and simulation of human walking: a review of optimization-based and other approaches. Struct Multidiscip Optim 42:1–23. https://doi.org/10.1007/s00158-010-0496-8

    Article  MathSciNet  MATH  Google Scholar 

  32. Haydon DS, Pinder RA, Grimshaw PN, Robertson WSP (2016) Elite wheelchair rugby: a quantitative analysis of chair configuration in Australia. Sports Eng 19:177–184. https://doi.org/10.1007/s12283-016-0203-0

    Article  Google Scholar 

  33. Astier M, Weissland T, Vallier JM, Pradon D, Watelain E, Faupin A (2017) Effects of synchronous versus asynchronous push modes on performance and biomechanical parameters in elite wheelchair basketball. Sports Eng. https://doi.org/10.1007/s12283-017-0245-y

    Google Scholar 

Download references

Acknowledgements

The authors thank the Paralympian for participating in this research. The authors also recognize the Canadian Sport Institute Ontario and Curling Canada for their support. This research was funded by Dr. John McPhee’s Tier I Canada Research Chair in Biomechatronic System Dynamics.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Brock Laschowski.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Laschowski, B., Mehrabi, N. & McPhee, J. Optimization-based motor control of a Paralympic wheelchair athlete. Sports Eng 21, 207–215 (2018). https://doi.org/10.1007/s12283-018-0265-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12283-018-0265-2

Keywords

Navigation