Nataraj Raviraj, Audu Musa L, Kirsch Robert F, Triolo Ronald J
Louis Stokes VAMC, Cleveland, OH, USA.
J Appl Biomech. 2012 Feb;28(1):85-92. doi: 10.1123/jab.28.1.85. Epub 2011 Oct 4.
This pilot study investigated the potential of using trunk acceleration feedback control of center of pressure (COP) against postural disturbances with a standing neuroprosthesis following paralysis. Artificial neural networks (ANNs) were trained to use three-dimensional trunk acceleration as input to predict changes in COP for able-bodied subjects undergoing perturbations during bipedal stance. Correlation coefficients between ANN predictions and actual COP ranged from 0.67 to 0.77. An ANN trained across all subject-normalized data was used to drive feedback control of ankle muscle excitation levels for a computer model representing a standing neuroprosthesis user. Feedback control reduced average upper-body loading during perturbation onset and recovery by 42% and peak loading by 29% compared with optimal, constant excitation.
这项初步研究调查了使用躯干加速度反馈控制压力中心(COP)以对抗瘫痪后站立式神经假体引起的姿势干扰的潜力。训练人工神经网络(ANN),将三维躯干加速度作为输入,以预测双足站立姿势下接受扰动的健全受试者的COP变化。ANN预测与实际COP之间的相关系数在0.67至0.77之间。使用在所有受试者归一化数据上训练的ANN来驱动代表站立式神经假体使用者的计算机模型的踝关节肌肉兴奋水平的反馈控制。与最佳恒定兴奋相比,反馈控制在扰动开始和恢复期间将平均上身负荷降低了42%,峰值负荷降低了29%。