Trunk acceleration for neuroprosthetic control of standing: a pilot study.
Trunk acceleration for neuroprosthetic control of standing: a pilot study.
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用于站立神经假体控制的躯干加速：一项试点研究。
DOI：
10.1123/jab.28.1.85
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发表时间：
2012
影响因子：
1.4
通讯作者：
Triolo,RonaldJ
中科院分区：
文献类型：
--
作者：
Nataraj,Raviraj;Audu,MusaL;Kirsch,RobertF;Triolo,RonaldJ
本初步研究探讨了使用躯干加速度反馈控制压力中心（COP）对抗瘫痪后站立神经假体姿势障碍的潜力。人工神经网络（ANN）进行了训练，使用三维躯干加速度作为输入，以预测COP的变化，身体健全的受试者在双足站立期间经历扰动。人工神经网络预测和实际COP之间的相关系数范围从0.67到0.77。使用在所有受试者标准化数据中训练的ANN来驱动代表站立神经假体用户的计算机模型的踝关节肌肉兴奋水平的反馈控制。反馈控制减少了平均上半身的负载在扰动开始和恢复的42%和峰值负载fby 29%相比，最佳的，恒定的激励。
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 fby 29% compared with optimal, constant excitation.