Optimal Control Based Stiffness Identification of an Ankle-Foot Orthosis Using a Predictive Walking Model

Optimal Control Based Stiffness Identification of an Ankle-Foot Orthosis Using a Predictive Walking Model. Sreenivasa, M., Millard, M., Felis, M., Mombaur, K., & Wolf, S. I. Frontiers in Computational Neuroscience, 11:23:1--13, 2017.

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Predicting the movements, ground reaction forces and neuromuscular activity during gait can be a valuable asset to the clinical rehabilitation community, both to understand pathology, as well as to plan effective intervention. In this work we use an optimal control method to generate predictive simulations of pathological gait in the sagittal plane. We construct a patient-specific model corresponding to a 7-year old child with gait abnormalities and identify the optimal spring characteristics of an ankle-foot orthosis that minimizes muscle effort. Our simulations include the computation of foot-ground reaction forces, as well as the neuromuscular dynamics using computationally efficient muscle torque generators and excitation-activation equations. The optimal control problem is solved with a direct multiple shooting method. The solution of this problem is physically consistent synthetic neural excitation commands, muscle activations and whole body motion. Our simulations produced similar changes to the gait characteristics as those recorded on the patient. The orthosis-equipped model was able to walk faster with more extended knees. Notably, our approach can be easily tuned to simulate weakened muscles, produces physiologically realistic ground reaction forces and smooth muscle activations and torques, and can be implemented on a standard workstation to produce results within a few hours. These results are an important contribution towards bridging the gap between research methods in computational neuromechanics and day-to-day clinical rehabilitation.

@Article{Sreenivasa2017,
  Title                    = {Optimal Control Based Stiffness Identification of an Ankle-Foot Orthosis Using a Predictive Walking Model},
  Author                   = {Sreenivasa, M. and Millard, M. and Felis, M. and Mombaur, K. and Wolf, S. I.},
  Journal                  = {Frontiers in Computational Neuroscience},
  Year                     = {2017},
  Pages                    = {23:1--13},
  Volume                   = {11},

  Abstract                 = {Predicting the movements, ground reaction forces and neuromuscular activity during gait can be a valuable asset to the clinical rehabilitation community, both to understand pathology, as well as to plan effective intervention. In this work we use an optimal control method to generate predictive simulations of pathological gait in the sagittal plane. We construct a patient-specific model corresponding to a 7-year old child with gait abnormalities and identify the optimal spring characteristics of an ankle-foot orthosis that minimizes muscle effort. Our simulations include the computation of foot-ground reaction forces, as well as the neuromuscular dynamics using computationally efficient muscle torque generators and excitation-activation equations. The optimal control problem is solved with a direct multiple shooting method. The solution of this problem is physically consistent synthetic neural excitation commands, muscle activations and whole body motion. Our simulations produced similar changes to the gait characteristics as those recorded on the patient. The orthosis-equipped model was able to walk faster with more extended knees. Notably, our approach can be easily tuned to simulate weakened muscles, produces physiologically realistic ground reaction forces and smooth muscle activations and torques, and can be implemented on a standard workstation to produce results within a few hours. These results are an important contribution towards bridging the gap between research methods in computational neuromechanics and day-to-day clinical rehabilitation.},
  Doi                      = {10.3389/fncom.2017.00023},
  ISSN                     = {1662-5188},
  Timestamp                = {2017.04.21},
  Url                      = {http://journal.frontiersin.org/article/10.3389/fncom.2017.00023}
}

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