Skip to main content
Log in

A review of simulation methods for human movement dynamics with emphasis on gait

  • Published:
Multibody System Dynamics Aims and scope Submit manuscript

Abstract

Human gait analysis is a complex problem in biomechanics because of highly nonlinear human motion equations, muscle dynamics, and foot-ground contact.

Despite a large number of studies in human gait analysis, predictive human gait simulation is still challenging researchers to increase the accuracy and computational efficiency for evaluative studies (e.g., model-based assistive device controllers, surgical intervention planning, athletic training, and prosthesis and orthosis design).

To assist researchers in this area, this review article classifies recent predictive simulation methods for human gait analysis according to three categories: (1) the human models used (i.e., skeletal, musculoskeletal and neuromusculoskeletal models), (2) problem formulation, and (3) simulation solvers.

Human dynamic models are classified based on whether muscle activation and/or contraction dynamics or joint torques (instead of muscle dynamics) are employed in the analysis. Different formulations use integration and/or differentiation or implicit-declaration of the dynamic equations. A variety of simulation solvers (i.e., semi- and fully-predictive simulation methods) are studied. Finally, the pros and cons of the different formulations and simulation solvers are discussed.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Singh, J.A.: Epidemiology of knee and hip arthroplasty: a systematic review. Open Orthop. J. 5, 80–85 (2011). https://doi.org/10.2174/1874325001105010080

    Article  Google Scholar 

  2. McLawhorn, A.S., Sculco, P.K., Weeks, K.D., Nam, D., Mayman, D.J.: Targeting a new safe zone: a step in the development of patient-specific component positioning in hip arthroplasty. Orthop. Proc. 96-B, 43 (2014). https://doi.org/10.1302/1358-992X.96BSUPP_16.CAOS2014-043

    Article  Google Scholar 

  3. Abdel, M.P., von Roth, P., Jennings, M.T., Hanssen, A.D., Pagnano, M.W.: What safe zone? The vast majority of dislocated THAs are within the Lewinnek safe zone for acetabular component position. Clin. Orthop. Relat. Res. 474, 386–391 (2016). https://doi.org/10.1007/s11999-015-4432-5

    Article  Google Scholar 

  4. Esposito, C.I., Carroll, K.M., Sculco, P.K., Padgett, D.E., Jerabek, S.A., Mayman, D.J.: Total hip arthroplasty patients with fixed spinopelvic alignment are at higher risk of hip dislocation. J. Arthroplast. (2017). https://doi.org/10.1016/j.arth.2017.12.005

    Article  Google Scholar 

  5. Esposito, C.I., Gladnick, B.P., Lee, Y., Lyman, S., Wright, T.M., Mayman, D.J., Padgett, D.E.: Cup position alone does not predict risk of dislocation after hip arthroplasty. J. Arthroplast. 30, 109–113 (2015). https://doi.org/10.1016/j.arth.2014.07.009

    Article  Google Scholar 

  6. Abujaber, S.B., Marmon, A.R., Pozzi, F., Rubano, J.J., Zeni, J.A. Jr.: Sit-to-stand biomechanics before and after total hip arthroplasty. J. Arthroplast. 30, 2027–2033 (2015). https://doi.org/10.1016/j.arth.2015.05.024

    Article  Google Scholar 

  7. Sasaki, K., Hongo, M., Miyakoshi, N., Matsunaga, T., Yamada, S., Kijima, H., Shimada, Y.: Evaluation of sagittal spine-pelvis-lower limb alignment in elderly women with pelvic retroversion while standing and walking using a three-dimensional musculoskeletal model. Asian Spine J. 11, 562–569 (2017). https://doi.org/10.4184/asj.2017.11.4.562

    Article  Google Scholar 

  8. Handford, M.L., Srinivasan, M.: Robotic lower limb prosthesis design through simultaneous computer optimizations of human and prosthesis costs. Sci. Rep. 6, 19983 (2016). https://doi.org/10.1038/srep19983

    Article  Google Scholar 

  9. Geng, Y., Yang, P., Xu, X., Chen, L.: Design and simulation of active transfemoral prosthesis. In: 2012 24th Chinese Control and Decision Conference (CCDC), pp. 3724–3728. IEEE, Taiyuan (2012)

    Chapter  Google Scholar 

  10. Font-Llagunes, J.M., Pàmies-Vilà, R., Alonso, J., Lugrís, U.: Simulation and design of an active orthosis for an incomplete spinal cord injured subject. Proc. IUTAM 2, 68–81 (2011). https://doi.org/10.1016/j.piutam.2011.04.007

    Article  Google Scholar 

  11. Rosenberg, M., Steele, K.M.: Simulated impacts of ankle foot orthoses on muscle demand and recruitment in typically-developing children and children with cerebral palsy and crouch gait. PLoS ONE 12, e0180219 (2017). https://doi.org/10.1371/journal.pone.0180219

    Article  Google Scholar 

  12. Lochner, S.J., Huissoon, J.P., Bedi, S.S.: Simulation methods in the foot orthosis development process. Comput-Aided Des. Appl. 11, 608–616 (2014). https://doi.org/10.1080/16864360.2014.914375

    Article  Google Scholar 

  13. Wyss, U.P., McBride, I., Murphy, L., Cooke, T.D., Olney, S.J.: Joint reaction forces at the first MTP joint in a normal elderly population. J. Biomech. 23, 977–984 (1990)

    Article  Google Scholar 

  14. Neumann, D.A.: Biomechanical analysis of selected principles of hip joint protection. Arthritis Care Res. 2, 146–155 (1989)

    Article  Google Scholar 

  15. Kirkwood, R.N., Gomes, H. de A., Sampaio, R.F., Culham, E., Costigan, P.: Análise biomecânica das articulações do quadril e joelho durante a marcha em participantes idosos. Acta Ortop. Bras. 15, 267–271 (2007). https://doi.org/10.1590/S1413-78522007000500007

    Article  Google Scholar 

  16. Chuanjie, Z., Zhengwei, F.: Biomechanical analysis of knee joint mechanism of the national women’s epee fencing lunge movement. Biomed. Res. 0, 104–110 (2017)

    Google Scholar 

  17. Lenhart, R.L., Thelen, D.G., Wille, C.M., Chumanov, E.S., Heiderscheit, B.C.: Increasing running step rate reduces patellofemoral joint forces. Med. Sci. Sports Exerc. 46, 557–564 (2014). https://doi.org/10.1249/MSS.0b013e3182a78c3a

    Article  Google Scholar 

  18. Winter, D.: Human balance and posture control during standing and walking. Gait Posture 3, 193–214 (1995). https://doi.org/10.1016/0966-6362(96)82849-9

    Article  Google Scholar 

  19. Meyer, G., Ayalon, M.: Biomechanical aspects of dynamic stability. Eur. Rev. Aging Phys. Act. 3, 29–33 (2006). https://doi.org/10.1007/s11556-006-0006-6

    Article  Google Scholar 

  20. Prakash, C., Kumar, R., Mittal, N.: Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges. Artif. Intell. Rev. 49, 1–40 (2018). https://doi.org/10.1007/s10462-016-9514-6

    Article  Google Scholar 

  21. Rockenfeller, R., Günther, M., Schmitt, S., Götz, T.: Comparative sensitivity analysis of muscle activation dynamics. Comput. Math. Methods Med. 2015, 1–16 (2015). https://doi.org/10.1155/2015/585409

    Article  MATH  Google Scholar 

  22. Romero, F., Alonso, F.J.: A comparison among different Hill-type contraction dynamics formulations for muscle force estimation. Mech. Sci. 7, 19–29 (2016). https://doi.org/10.5194/ms-7-19-2016

    Article  Google Scholar 

  23. Amis, A.A., Dowson, D., Wright, V.: Muscle strengths and musculoskeletal geometry of the upper limb. Eng. Med. 8, 41–48 (1979). https://doi.org/10.1243/EMED_JOUR_1979_008_010_02

    Article  Google Scholar 

  24. Schiehlen, W.: On the historical development of human walking dynamics. In: Stein, E. (ed.) The History of Theoretical, Material and Computational Mechanics—Mathematics Meets Mechanics and Engineering, pp. 101–116. Springer, Berlin (2014)

    Chapter  Google Scholar 

  25. Thelen, D.G., Anderson, F.C.: Using computed muscle control to generate forward dynamic simulations of human walking from experimental data. J. Biomech. 39, 1107–1115 (2006). https://doi.org/10.1016/j.jbiomech.2005.02.010

    Article  Google Scholar 

  26. Lasota, P.A., Shah, J.A.: A multiple-predictor approach to human motion prediction. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 2300–2307. IEEE, Singapore (2017)

    Chapter  Google Scholar 

  27. Pasciuto, I., Ausejo, S., Celigüeta, J.T., Suescun, Á., Cazón, A.: A comparison between optimization-based human motion prediction methods: data-based, knowledge-based and hybrid approaches. Struct. Multidiscip. Optim. 49, 169–183 (2014). https://doi.org/10.1007/s00158-013-0960-3

    Article  Google Scholar 

  28. Chung, H.-J., Xiang, Y., Arora, J.S., Abdel-Malek, K.: Optimization-based dynamic 3D human running prediction: effects of foot location and orientation. Robotica 33, 413–435 (2015). https://doi.org/10.1017/S0263574714000253

    Article  Google Scholar 

  29. Kim, Y., Lee, B., Yoo, J., Choi, S., Kim, J.: Humanoid robot HanSaRam: yawing moment cancellation and ZMP compensation. In: Proceedings of AUS International Symposium on Mechatronics, Sharjah, U.A.E. (2005)

    Google Scholar 

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

    Article  Google Scholar 

  31. Long, L.L., Srinivasan, M.: Walking, running, and resting under time, distance, and average speed constraints: optimality of walk-run-rest mixtures. J. R. Soc. Interface 10, 20120980 (2013). https://doi.org/10.1098/rsif.2012.0980

    Article  Google Scholar 

  32. Srinivasan, M.: Optimal speeds for walking and running, and walking on a moving walkway. Chaos, Interdiscip. J. Nonlinear Sci. 19, 26112 (2009). https://doi.org/10.1063/1.3141428

    Article  MathSciNet  MATH  Google Scholar 

  33. Forner-Cordero, A., Koopman, H.J.F.M., van der Helm, F.C.T.: Inverse dynamics calculations during gait with restricted ground reaction force information from pressure insoles. Gait Posture 23, 189–199 (2006). https://doi.org/10.1016/J.GAITPOST.2005.02.002

    Article  Google Scholar 

  34. Ren, L., Jones, R.K., Howard, D.: Whole body inverse dynamics over a complete gait cycle based only on measured kinematics. J. Biomech. 41, 2750–2759 (2008). https://doi.org/10.1016/J.JBIOMECH.2008.06.001

    Article  Google Scholar 

  35. Riemer, R., Hsiao-Wecksler, E.T., Zhang, X.: Uncertainties in inverse dynamics solutions: a comprehensive analysis and an application to gait. Gait Posture 27, 578–588 (2008). https://doi.org/10.1016/J.GAITPOST.2007.07.012

    Article  Google Scholar 

  36. Silva, M.P.T., Ambrósio, J.A.C.: Kinematic data consistency in the inverse dynamic analysis of biomechanical systems. Multibody Syst. Dyn. 8, 219–239 (2002). https://doi.org/10.1023/A:1019545530737

    Article  MATH  Google Scholar 

  37. Pàmies-Vilà, R., Font-Llagunes, J.M., Cuadrado, J., Alonso, F.J.: Analysis of different uncertainties in the inverse dynamic analysis of human gait. Mech. Mach. Theory 58, 153–164 (2012). https://doi.org/10.1016/J.MECHMACHTHEORY.2012.07.010

    Article  Google Scholar 

  38. Faber, H., van Soest, A.J., Kistemaker, D.A.: Inverse dynamics of mechanical multibody systems: an improved algorithm that ensures consistency between kinematics and external forces. PLoS ONE 13, e0204575 (2018). https://doi.org/10.1371/journal.pone.0204575

    Article  Google Scholar 

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

    Article  Google Scholar 

  40. Lin, Y.-C., Walter, J.P., Pandy, M.G.: Predictive simulations of neuromuscular coordination and joint-contact loading in human gait. Ann. Biomed. Eng. 46, 1216–1227 (2018). https://doi.org/10.1007/s10439-018-2026-6

    Article  Google Scholar 

  41. Delp, S.L., Anderson, F.C., Arnold, A.S., Loan, P., Habib, A., John, C.T., Guendelman, E., Thelen, D.G.: OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 54, 1940–1950 (2007). https://doi.org/10.1109/TBME.2007.901024

    Article  Google Scholar 

  42. Millard, M., McPhee, J., Kubica, E.: Multi-step forward dynamic gait simulation. In: Bottasso, C.L. (ed.) Multibody Dynamics, pp. 25–43. Springer, Dordrecht (2009)

    Google Scholar 

  43. Tlalolini, D., Aoustin, Y., Chevallereau, C.: Design of a walking cyclic gait with single support phases and impacts for the locomotor system of a thirteen-link 3D biped using the parametric optimization. Multibody Syst. Dyn. 23, 33–56 (2010). https://doi.org/10.1007/s11044-009-9175-1

    Article  MathSciNet  MATH  Google Scholar 

  44. Lugrís, U., Carlín, J., Pàmies-Vilà, R., Font-Llagunes, J.M., Cuadrado, J.: Solution methods for the double-support indeterminacy in human gait. Multibody Syst. Dyn. 30, 247–263 (2013). https://doi.org/10.1007/s11044-013-9363-x

    Article  MathSciNet  Google Scholar 

  45. Asano, F.: Stability analysis of underactuated compass gait based on linearization of motion. Multibody Syst. Dyn. 33, 93–111 (2015). https://doi.org/10.1007/s11044-014-9416-9

    Article  MathSciNet  MATH  Google Scholar 

  46. Khadiv, M., Ezati, M., Moosavian, S.A.A.: A computationally efficient inverse dynamics solution based on virtual work principle for biped robots. Iran. J. Sci. Technol. Trans. Mech. Eng. (2017). https://doi.org/10.1007/s40997-017-0138-5

    Article  Google Scholar 

  47. Martin, A.E., Schmiedeler, J.P.: Predicting human walking gaits with a simple planar model. J. Biomech. 47, 1416–1421 (2014). https://doi.org/10.1016/J.JBIOMECH.2014.01.035

    Article  Google Scholar 

  48. Gregg, R.D., Rouse, E.J., Hargrove, L.J., Sensinger, J.W.: Evidence for a time-invariant phase variable in human ankle control. PLoS ONE 9, e89163 (2014). https://doi.org/10.1371/journal.pone.0089163

    Article  Google Scholar 

  49. Mouzo, F., Lugris, U., Pamies Vila, R., Font Llagunes, J.M., Cuadrado Aranda, J.: Underactuated approach for the control-based forward dynamic analysis of acquired gait motions. In: Proceedings of the ECCOMAS Thematic Conference on Multibody Dynamics, pp. 1092–1100 (2015)

    Google Scholar 

  50. Shourijeh, M.S., McPhee, J.: Efficient hyper-volumetric contact dynamic modelling of the foot within human gait simulations. In: Volume 7A: 9th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, p. V07AT10A012. ASME, Oregon (2013)

    Chapter  Google Scholar 

  51. Pàmies-Vilà, R., Pätkau, O., Dòria-Cerezo, A., Font-Llagunes, J.M.: Influence of the controller design on the accuracy of a forward dynamic simulation of human gait. Mech. Mach. Theory 107, 123–138 (2017). https://doi.org/10.1016/J.MECHMACHTHEORY.2016.09.002

    Article  Google Scholar 

  52. Mehrabi, N., Sharif Razavian, R., Ghannadi, B., McPhee, J.: Predictive simulation of reaching moving targets using nonlinear model predictive control. Front. Comput. Neurosci. 10, 143 (2017). https://doi.org/10.3389/fncom.2016.00143

    Article  Google Scholar 

  53. Sun, J., Voglewede, P.A.: Dynamic simulation of human gait using a combination of model predictive and PID control. In: Volume 6: 10th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, p. V006T10A008. ASME, New York (2014)

    Chapter  Google Scholar 

  54. Sun, J., Wu, S., Voglewede, P.A.: Dynamic simulation of human gait model with predictive capability. J. Biomech. Eng. 140, 31008 (2018). https://doi.org/10.1115/1.4038739

    Article  Google Scholar 

  55. Sartori, M., Reggiani, M., Farina, D., Lloyd, D.G.: EMG-driven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity. PLoS ONE 7, e52618 (2012). https://doi.org/10.1371/journal.pone.0052618

    Article  Google Scholar 

  56. Crowninshield, R.D., Johnston, R.C., Andrews, J.G., Brand, R.A.: A biomechanical investigation of the human hip. J. Biomech. 11, 75–85 (1978). https://doi.org/10.1016/0021-9290(78)90045-3

    Article  Google Scholar 

  57. Ackermann, M., Schiehlen, W.: Physiological methods to solve the force-sharing problem in biomechanics. Comput. Methods Appl. Sci. 12, 1–23 (2008)

    MATH  Google Scholar 

  58. Zajac, F.E., Neptune, R.R., Kautz, S.A.: Biomechanics and muscle coordination of human walking: part II: lessons from dynamical simulations and clinical implications. Gait Posture 17, 1–17 (2003). https://doi.org/10.1016/S0966-6362(02)00069-3

    Article  Google Scholar 

  59. Zajac, F.E., Neptune, R.R., Kautz, S.A.: Biomechanics and muscle coordination of human walking. Part I: introduction to concepts, power transfer, dynamics and simulations. Gait Posture 16, 215–232 (2002). https://doi.org/10.1016/S0966-6362(02)00068-1

    Article  Google Scholar 

  60. Shourijeh, M.S., Mehrabi, N., McPhee, J.: Forward static optimization in dynamic simulation of human musculoskeletal systems: a proof-of-concept study. J. Comput. Nonlinear Dyn. 12, 51005 (2017). https://doi.org/10.1115/1.4036195

    Article  Google Scholar 

  61. Yamasaki, T., Idehara, K., Xin, X.: Estimation of muscle activity using higher-order derivatives, static optimization, and forward-inverse dynamics. J. Biomech. 49, 2015–2022 (2016). https://doi.org/10.1016/j.jbiomech.2016.04.024

    Article  Google Scholar 

  62. Tsirakos, D., Baltzopoulos, V., Bartlett, R.: Inverse optimization: functional and physiological considerations related to the force-sharing problem. Crit. Rev. Biomed. Eng. 25, 371–407 (1997). https://doi.org/10.1615/CritRevBiomedEng.v25.i4-5.20

    Article  Google Scholar 

  63. Crowninshield, R.D., Brand, R.A.: A physiologically based criterion of muscle force prediction in locomotion. J. Biomech. 14, 793–801 (1981). https://doi.org/10.1016/0021-9290(81)90035-X

    Article  Google Scholar 

  64. Davy, D.T., Audu, M.L.: A dynamic optimization technique for predicting muscle forces in the swing phase of gait. J. Biomech. 20, 187–201 (1987). https://doi.org/10.1016/0021-9290(87)90310-1

    Article  Google Scholar 

  65. Davoudabadi Farahani, S., Svinin, M., Andersen, M.S., de Zee, M., Rasmussen, J.: Prediction of closed-chain human arm dynamics in a Crank-rotation task. J. Biomech. 49, 2684–2693 (2016). https://doi.org/10.1016/j.jbiomech.2016.05.034

    Article  Google Scholar 

  66. Pandy, M.G., Anderson, F.C., Hull, D.G.: A parameter optimization approach for the optimal control of large-scale musculoskeletal systems. J. Biomech. Eng. 114, 450–460 (1992). https://doi.org/10.1115/1.2894094

    Article  Google Scholar 

  67. Serrancolí, G., Font-Llagunes, J.M., Barjau, A.: A weighted cost function to deal with the muscle force sharing problem in injured subjects: a single case study. Proc. Inst. Mech. Eng., Proc., Part K, J. Multi-Body Dyn. 228, 241–251 (2014). https://doi.org/10.1177/1464419314530110

    Article  Google Scholar 

  68. Frank, C.A., Pandy, M.G.: A dynamic optimization solution for vertical jumping in three dimensions. Comput. Methods Biomech. Biomed. Eng. 2, 201–231 (1999). https://doi.org/10.1080/10255849908907988

    Article  Google Scholar 

  69. Morrow, M.M., Rankin, J.W., Neptune, R.R., Kaufman, K.R.: A comparison of static and dynamic optimization muscle force predictions during wheelchair propulsion. J. Biomech. 47, 3459–3465 (2014). https://doi.org/10.1016/j.jbiomech.2014.09.013

    Article  Google Scholar 

  70. Menegaldo, L.L., Fleury, A. de T., Weber, H.I.: A “cheap” optimal control approach to estimate muscle forces in musculoskeletal systems. J. Biomech. 39, 1787–1795 (2006). https://doi.org/10.1016/j.jbiomech.2005.05.029

    Article  Google Scholar 

  71. Seth, A., Pandy, M.G.: A neuromusculoskeletal tracking method for estimating individual muscle forces in human movement. J. Biomech. 40, 356–366 (2007). https://doi.org/10.1016/j.jbiomech.2005.12.017

    Article  Google Scholar 

  72. Anderson, F.C., Pandy, M.G.: Dynamic optimization of human walking. J. Biomech. Eng. 123, 381 (2001). https://doi.org/10.1115/1.1392310

    Article  Google Scholar 

  73. Anderson, F.C., Pandy, M.G.: Static and dynamic optimization solutions for gait are practically equivalent. J. Biomech. 34, 153–161 (2001). https://doi.org/10.1016/S0021-9290(00)00155-X

    Article  Google Scholar 

  74. Ackermann, M.: Dynamics and energetics of walking with prostheses (2007)

  75. Nikooyan, A.A., Veeger, H.E.J., Chadwick, E.K.J., Praagman, M., van der Helm, F.C.T.: Development of a comprehensive musculoskeletal model of the shoulder and elbow. Med. Biol. Eng. Comput. 49, 1425–1435 (2011). https://doi.org/10.1007/s11517-011-0839-7

    Article  Google Scholar 

  76. Rasmussen, J., Damsgaard, M., Christensen, S.T.: Inverse-inverse dynamics simulation of musculo-skeletal systems. In: Proceedings of the 12th Conference of the European Society of Biomechanics Royal Academy of Medicine in Ireland. Royal Academy of Medicine in Ireland, Dublin (2000)

    Google Scholar 

  77. Quental, C., Folgado, J., Ambrósio, J.: A window moving inverse dynamics optimization for biomechanics of motion. Multibody Syst. Dyn. 38, 157–171 (2016). https://doi.org/10.1007/s11044-016-9529-4

    Article  Google Scholar 

  78. Liu, M.Q., Anderson, F.C., Schwartz, M.H., Delp, S.L.: Muscle contributions to support and progression over a range of walking speeds. J. Biomech. 41, 3243–3252 (2008). https://doi.org/10.1016/j.jbiomech.2008.07.031

    Article  Google Scholar 

  79. Hamner, S.R., Seth, A., Delp, S.L.: Muscle contributions to propulsion and support during running. J. Biomech. 43, 2709–2716 (2010). https://doi.org/10.1016/j.jbiomech.2010.06.025

    Article  Google Scholar 

  80. Sharif Shourijeh, M., Mehrabi, N., McPhee, J.: Forward static optimization in dynamic simulation of human musculoskeletal systems: a proof-of-concept study. J. Comput. Nonlinear Dyn. 12, 51005 (2017). https://doi.org/10.1115/1.4036195

    Article  Google Scholar 

  81. van den Bogert, A.J., Blana, D., Heinrich, D.: Implicit methods for efficient musculoskeletal simulation and optimal control. Proc. IUTAM 2, 297–316 (2011). https://doi.org/10.1016/J.PIUTAM.2011.04.027

    Article  Google Scholar 

  82. Chadwick, E.K., Blana, D., Kirsch, R.F., van den Bogert, A.J.: Real-time simulation of three-dimensional shoulder girdle and arm dynamics. IEEE Trans. Biomed. Eng. 61, 1947–1956 (2014). https://doi.org/10.1109/TBME.2014.2309727

    Article  Google Scholar 

  83. Challis, J.H., Kerwin, D.G.: An analytical examination of muscle force estimations using optimization techniques. Proc. Inst. Mech. Eng. H 207, 139–148 (1993). https://doi.org/10.1243/PIME_PROC_1993_207_286_02

    Article  Google Scholar 

  84. Terrier, A., Aeberhard, M., Michellod, Y., Mullhaupt, P., Gillet, D., Farron, A., Pioletti, D.P.: A musculoskeletal shoulder model based on pseudo-inverse and null-space optimization. Med. Eng. Phys. 32, 1050–1056 (2010). https://doi.org/10.1016/j.medengphy.2010.07.006

    Article  Google Scholar 

  85. Martelli, S., Calvetti, D., Somersalo, E., Viceconti, M.: Stochastic modelling of muscle recruitment during activity. Interface Focus 5, 20140094 (2015). https://doi.org/10.1098/rsfs.2014.0094

    Article  Google Scholar 

  86. Sharif Razavian, R., McPhee, J.: Minimization of muscle fatigue as the criterion to solve muscle forces-sharing problem. In: ASME 2015 Dynamic Systems and Control Conference, p. V001T15A001. ASME, Ohio (2015)

    Google Scholar 

  87. Tresch, M.C.: Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets. J. Neurophysiol. 95, 2199–2212 (2005). https://doi.org/10.1152/jn.00222.2005

    Article  Google Scholar 

  88. Steele, K.M., Rozumalski, A., Schwartz, M.H.: Muscle synergies and complexity of neuromuscular control during gait in cerebral palsy. Dev. Med. Child Neurol. 57, 1176–1182 (2015). https://doi.org/10.1111/dmcn.12826

    Article  Google Scholar 

  89. Smale, K.B., Sharif Shourijeh, M., Benoit, D.L.: Use of muscle synergies and wavelet transforms to identify fatigue during squatting. J. Electromyogr. Kinesiol. 28, 158–166 (2016). https://doi.org/10.1016/j.jelekin.2016.04.008

    Article  Google Scholar 

  90. Sharif Shourijeh, M., Flaxman, T.E., Benoit, D.L.: An approach for improving repeatability and reliability of non-negative matrix factorization for muscle synergy analysis. J. Electromyogr. Kinesiol. 26, 36–43 (2016). https://doi.org/10.1016/j.jelekin.2015.12.001

    Article  Google Scholar 

  91. Zariffa, J., Steeves, J., Pai, D.K.: Changes in hand muscle synergies in subjects with spinal cord injury: characterization and functional implications. J. Spinal Cord Med. 35, 310–318 (2012). https://doi.org/10.1179/2045772312Y.0000000037

    Article  Google Scholar 

  92. Yoshikawa, F., Hirai, H., Watanabe, E., Nagakawa, Y., Kuroiwa, A., Grabke, E., Uemura, M., Miyazaki, F., Krebs, H.I.: Equilibrium-point-based synergies that encode coordinates in task space: a practical method for translating functional synergies from human to musculoskeletal robot arm. In: 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 1135–1140. IEEE, Cancun (2016)

    Chapter  Google Scholar 

  93. Sharif Razavian, R., Mehrabi, N., McPhee, J.: A model-based approach to predict muscle synergies using optimization: application to feedback control. Front. Comput. Neurosci. 9, 121 (2015). https://doi.org/10.3389/fncom.2015.00121

    Article  Google Scholar 

  94. De Groote, F., Kinney, A.L., Rao, A.V., Fregly, B.J.: Evaluation of direct collocation optimal control problem formulations for solving the muscle redundancy problem. Ann. Biomed. Eng. 44, 2922–2936 (2016). https://doi.org/10.1007/s10439-016-1591-9

    Article  Google Scholar 

  95. Meyer, A.J., Patten, C., Fregly, B.J.: Lower extremity EMG-driven modeling of walking with automated adjustment of musculoskeletal geometry. PLoS ONE 12, e0179698 (2017). https://doi.org/10.1371/journal.pone.0179698

    Article  Google Scholar 

  96. Rao, A.V.: A survey of numerical methods for optimal control. Adv. Astronaut. Sci. 135, 497–528 (2009). https://doi.org/10.1515/jnum-2014-0003

    Article  Google Scholar 

  97. Miller, R.H., Brandon, S.C.E., Deluzio, K.J.: Predicting sagittal plane biomechanics that minimize the axial knee joint contact force during walking. J. Biomech. Eng. 135, 11007 (2012). https://doi.org/10.1115/1.4023151

    Article  Google Scholar 

  98. Sharif Shourijeh, M., McPhee, J.: Forward dynamic optimization of human gait simulations: a global parameterization approach. J. Comput. Nonlinear Dyn. 9, 31018 (2014). https://doi.org/10.1115/1.4026266

    Article  Google Scholar 

  99. Miller, R.H.: A comparison of muscle energy models for simulating human walking in three dimensions. J. Biomech. 47, 1373–1381 (2014). https://doi.org/10.1016/j.jbiomech.2014.01.049

    Article  Google Scholar 

  100. Peasgood, M., Kubica, E., McPhee, J.: Stabilization of a dynamic walking gait simulation. J. Comput. Nonlinear Dyn. 2, 65 (2007). https://doi.org/10.1115/1.2389230

    Article  Google Scholar 

  101. Shourijeh, M.S., McPhee, J.: Foot–ground contact modeling within human gait simulations: from Kelvin–Voigt to hyper-volumetric models. Multibody Syst. Dyn. 35, 393–407 (2015). https://doi.org/10.1007/s11044-015-9467-6

    Article  MathSciNet  MATH  Google Scholar 

  102. Rajagopal, A., Dembia, C.L., DeMers, M.S., Delp, D.D., Hicks, J.L., Delp, S.L.: Full-body musculoskeletal model for muscle-driven simulation of human gait. IEEE Trans. Biomed. Eng. 63, 2068–2079 (2016). https://doi.org/10.1109/TBME.2016.2586891

    Article  Google Scholar 

  103. Farina, D., Merletti, R., Enoka, R.M.: The extraction of neural strategies from the surface EMG. J. Appl. Physiol. 96, 1486–1495 (2004). https://doi.org/10.1152/japplphysiol.01070.2003

    Article  Google Scholar 

  104. De Luca, C.J., Donald Gilmore, L., Kuznetsov, M., Roy, S.H.: Filtering the surface EMG signal: movement artifact and baseline noise contamination. J. Biomech. 43, 1573–1579 (2010). https://doi.org/10.1016/J.JBIOMECH.2010.01.027

    Article  Google Scholar 

  105. Winter, D.A.: Biomechanics and Motor Control of Human Movement. Wiley, New York (2009)

    Book  Google Scholar 

  106. Sartori, M., Farina, D., Lloyd, D.G.: Hybrid neuromusculoskeletal modeling to best track joint moments using a balance between muscle excitations derived from electromyograms and optimization. J. Biomech. 47, 3613–3621 (2014). https://doi.org/10.1016/j.jbiomech.2014.10.009

    Article  Google Scholar 

  107. Shourijeh, M.S., Smale, K.B., Potvin, B.M., Benoit, D.L.: A forward-muscular inverse-skeletal dynamics framework for human musculoskeletal simulations. J. Biomech. 49, 1718–1723 (2016). https://doi.org/10.1016/j.jbiomech.2016.04.007

    Article  Google Scholar 

  108. Hainisch, R., Gfoehler, M., Zubayer-Ul-Karim, M., Pandy, M.G.: Method for determining musculotendon parameters in subject-specific musculoskeletal models of children developed from MRI data. Multibody Syst. Dyn. 28, 143–156 (2012). https://doi.org/10.1007/s11044-011-9289-0

    Article  Google Scholar 

  109. Ma, Y., Xie, S., Zhang, Y.: A patient-specific EMG-driven neuromuscular model for the potential use of human-inspired gait rehabilitation robots. Comput. Biol. Med. 70, 88–98 (2016). https://doi.org/10.1016/j.compbiomed.2016.01.001

    Article  Google Scholar 

  110. Ehsani, H., Rostami, M., Gudarzi, M.: A general-purpose framework to simulate musculoskeletal system of human body: using a motion tracking approach. Comput. Methods Biomech. Biomed. Eng. 19, 306–319 (2016). https://doi.org/10.1080/10255842.2015.1017722

    Article  Google Scholar 

  111. Lee, L.-F., Umberger, B.R.: Generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB. Peer J. 4, e1638 (2016). https://doi.org/10.7717/peerj.1638

    Article  Google Scholar 

  112. Meyer, A.J., Eskinazi, I., Jackson, J.N., Rao, A.V., Patten, C., Fregly, B.J.: Muscle synergies facilitate computational prediction of subject-specific walking motions. Front. Bioeng. Biotechnol. 4, 77 (2016). https://doi.org/10.3389/fbioe.2016.00077

    Article  Google Scholar 

  113. Lin, Y.-C., Pandy, M.G.: Three-dimensional data-tracking dynamic optimization simulations of human locomotion generated by direct collocation. J. Biomech. 59, 1–8 (2017). https://doi.org/10.1016/j.jbiomech.2017.04.038

    Article  Google Scholar 

  114. Schöllhorn, W.I.: Applications of artificial neural nets in clinical biomechanics. Clin. Biomech. 19, 876–898 (2004). https://doi.org/10.1016/J.CLINBIOMECH.2004.04.005

    Article  Google Scholar 

  115. Liu, Y., Shih, S.-M., Tian, S.-L., Zhong, Y.-J., Li, L.: Lower extremity joint torque predicted by using artificial neural network during vertical jump. J. Biomech. 42, 906–911 (2009). https://doi.org/10.1016/J.JBIOMECH.2009.01.033

    Article  Google Scholar 

  116. Song, R., Tong, K.Y.: Using recurrent artificial neural network model to estimate voluntary elbow torque in dynamic situations. Med. Biol. Eng. Comput. 43, 473–480 (2005). https://doi.org/10.1007/BF02344728

    Article  Google Scholar 

  117. Ardestani, M.M., Zhang, X., Wang, L., Lian, Q., Liu, Y., He, J., Li, D., Jin, Z.: Human lower extremity joint moment prediction: a wavelet neural network approach. Expert Syst. Appl. 41, 4422–4433 (2014). https://doi.org/10.1016/J.ESWA.2013.11.003

    Article  Google Scholar 

  118. Zhang, B., Horváth, I., Molenbroek, J.F.M., Snijders, C.: Using artificial neural networks for human body posture prediction. Int. J. Ind. Ergon. 40, 414–424 (2010). https://doi.org/10.1016/J.ERGON.2010.02.003

    Article  Google Scholar 

  119. Isaksson, M., Jalden, J., Murphy, M.J.: On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications. Med. Phys. 32, 3801–3809 (2005). https://doi.org/10.1118/1.2134958

    Article  Google Scholar 

  120. Bataineh, M., Marler, T., Abdel-Malek, K., Arora, J.: Neural network for dynamic human motion prediction. Expert Syst. Appl. 48, 26–34 (2016). https://doi.org/10.1016/J.ESWA.2015.11.020

    Article  Google Scholar 

  121. Norman-Gerum, V., McPhee, J.: Constrained dynamic optimization of sit-to-stand motion driven by Bézier curves. J. Biomech. Eng. 140, 121011 (2018). https://doi.org/10.1115/1.4041527

    Article  Google Scholar 

  122. Ghannadi, B., Mehrabi, N., Sharif Razavian, R., McPhee, J.: Nonlinear model predictive control of an upper extremity rehabilitation robot using a two-dimensional human-robot interaction model. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 502–507. IEEE, Vancouver (2017)

    Chapter  Google Scholar 

  123. Mehrabi, N., McPhee, J.: Model-based control of biomechatronic systems. In: Segil, J. (ed.) Handbook of Biomechatronics, pp. 95–126. Academic Press, San Diego (2019)

    Chapter  Google Scholar 

  124. Jansen, C., McPhee, J.: Predictive dynamic simulation of seated start-up cycling using Olympic cyclist and bicycle models. In: Proceedings of International Sports Engineering Association, Brisbane, Australia, p. 220 (2018)

    Google Scholar 

  125. Bertolazzi, E., Biral, F., Da Lio, M.: Symbolic-numeric efficient solution of optimal control problems for multibody systems. J. Comput. Appl. Math. 185, 404–421 (2006). https://doi.org/10.1016/J.CAM.2005.03.019

    Article  MathSciNet  MATH  Google Scholar 

  126. Hunt, K.H., Crossley, F.R.E.: Coefficient of restitution interpreted as damping in vibroimpact. J. Appl. Mech. 42, 440 (1975). https://doi.org/10.1115/1.3423596

    Article  Google Scholar 

  127. Brown, P., McPhee, J.: A 3D ellipsoidal volumetric foot–ground contact model for forward dynamics. Multibody Syst. Dyn. 42, 447–467 (2018). https://doi.org/10.1007/s11044-017-9605-4

    Article  MathSciNet  Google Scholar 

  128. Ezati, M., Khadiv, M., Moosavian, S.A.A.: An investigation on the usefulness of employing a two-segment foot for traversing stairs. Int. J. Humanoid Robot. 14, 1750027 (2017). https://doi.org/10.1142/S021984361750027X

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mahdokht Ezati.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ezati, M., Ghannadi, B. & McPhee, J. A review of simulation methods for human movement dynamics with emphasis on gait. Multibody Syst Dyn 47, 265–292 (2019). https://doi.org/10.1007/s11044-019-09685-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11044-019-09685-1

Keywords

Navigation