Med Biol Eng Comput - Neural oscillators triggered by loading and hip orientation can generate activation patterns at the ankle during walking in humans.

Tópicos

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Resumo

Spinal pattern generators (SPGs), which are neural networks without a central input from the brain may be responsible for controlling locomotion. In this study, we used neural oscillators to examine the rhythmic patterns generated at the ankle during walking. Seven healthy male subjects were requested to walk at their normal self-selected speed on a treadmill. Force measurements acquired from pressure insoles, electromyography and kinematic data were captured simultaneously. The SPG model consisted of a simple oscillator made up of two neurons; one neuron will activate an ankle extensor and the other will activate an ankle flexor. The outputs of the oscillator represented the muscle activation of each muscle. A nonlinear least squares algorithm was used to determine a set of parameters that would optimise the differences between model output and experimental data. Insole forces and hip angles of six consecutive strides were used as inputs to the model, which generated outputs that closely fitted experimental data. Our results showed that it is possible to reproduce muscle activations using neural oscillators. A close correlation between simulated and measured muscle activations indicated that spinal control should not be underestimated in models of human locomotion.

Resumo Limpo

spinal pattern generat spgs neural network without central input brain may respons control locomot studi use neural oscil examin rhythmic pattern generat ankl walk seven healthi male subject request walk normal selfselect speed treadmil forc measur acquir pressur insol electromyographi kinemat data captur simultan spg model consist simpl oscil made two neuron one neuron will activ ankl extensor will activ ankl flexor output oscil repres muscl activ muscl nonlinear least squar algorithm use determin set paramet optimis differ model output experiment data insol forc hip angl six consecut stride use input model generat output close fit experiment data result show possibl reproduc muscl activ use neural oscil close correl simul measur muscl activ indic spinal control underestim model human locomot

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