Feasibility of EMG-based neural network controller for an upper extremity neuroprosthesis.
Feasibility of EMG-based neural network controller for an upper extremity neuroprosthesis.
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DOI：
10.1109/tnsre.2008.2010480
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发表时间：
2009-02
期刊：
影响因子：
--
通讯作者：
Kirsch RF
中科院分区：
文献类型：
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作者：
Hincapie JG;Kirsch RF
该项目的首要目标是使用功能性电刺激(FES)为C5/C6脊髓损伤(SCI)患者提供肩部和肘部功能，增加目前手部神经假体提供的功能结果。这项研究的具体目标是设计一种基于人工神经网络(ANN)的控制器，该控制器从保持在自愿控制下的肌肉的活动中提取信息，足以预测上肢几块瘫痪肌肉的适当刺激水平。用模拟获得的激活数据训练人工神经网络，使用手臂的肌肉骨骼模型，该模型被修改以反映C5脊髓损伤和FES的能力。来自健全受试者的几个手臂动作被记录下来，这些运动学被用作反向动力学模拟的输入，该模拟预测与所记录的动作相对应的肌肉激活模式。系统识别程序被用来从C5脊髓损伤中通常处于自愿控制下的较大集合中识别出最优的自愿输入肌肉集合。这些自愿的激活被用作神经网络的输入，而在C5脊髓损伤中典型瘫痪的肌肉是要预测的输出。神经网络控制器能够从“自愿的”激活中预测所需的FES瘫痪肌肉激活，预测误差小于3.6%。
The overarching goal of this project is to provide shoulder and elbow function to individuals with C5/C6 Spinal Cord Injury (SCI) using functional electrical stimulation (FES), increasing the functional outcomes currently provided by a hand neuroprosthesis. The specific goal of this study was to design a controller based on an artificial neural network (ANN) that extracts information from the activity of muscles that remain under voluntary control sufficient to predict appropriate stimulation levels for several paralyzed muscles in the upper extremity. The ANN was trained with activation data obtained from simulations using a musculoskeletal model of the arm that was modified to reflect C5 SCI and FES capabilities. Several arm movements were recorded from able-bodied subjects and these kinematics served as the inputs to inverse dynamic simulations that predicted muscle activation patterns corresponding to the movements recorded. A system identification procedure was used to identify an optimal reduced set of voluntary input muscles from the larger set that are typically under voluntary control in C5 SCI. These voluntary activations were used as the inputs to the ANN and muscles that are typically paralyzed in C5 SCI were the outputs to be predicted. The neural network controller was able to predict the needed FES paralyzed muscle activations from “voluntary” activations with less than a 3.6% RMS prediction error.