/Evaluation of a neuromechanical walking control model using disturbance experiments

Evaluation of a neuromechanical walking control model using disturbance experiments

Seungmoon Song and Hartmut Geyer
Journal Article, Frontiers in Computational Neuroscience, Vol. 11, March, 2017

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Abstract

Neuromechanical simulations have been used to study the spinal control of human locomotion which involves complex mechanical dynamics. So far, most neuromechanical simulation studies have focused on demonstrating the capability of a proposed control model in generating normal walking. As many of these models with competing control hypotheses can generate human-like normal walking behaviors, a more in-depth evaluation is required. Here, we conduct the more in-depth evaluation on a spinal-reflex-based control model using five representative gait disturbances, ranging from electrical stimulation to mechanical perturbation at individual leg joints and at the whole body. The immediate changes in muscle activations of the model are compared to those of humans across different gait phases and disturbance magnitudes. Remarkably similar response trends for the majority of investigated muscles and experimental conditions reinforce the plausibility of the reflex circuits of the model. However, the model’s responses lack in amplitude for two experiments with whole body disturbances suggesting that in these cases the proposed reflex circuits need to be amplified by additional control structures such as location-specific cutaneous reflexes. A model that captures these selective amplifications would be able to explain both steady and reactive spinal control of human locomotion. Neuromechanical simulations that investigate hypothesized control models are complementary to gait experiments in better understanding the control of human locomotion.

BibTeX Reference
@article{Song-2017-102625,
author = {Seungmoon Song and Hartmut Geyer},
title = {Evaluation of a neuromechanical walking control model using disturbance experiments},
journal = {Frontiers in Computational Neuroscience},
year = {2017},
month = {March},
volume = {11},
}
2018-01-24T09:19:55+00:00