Hincapie Juan Gabriel, Kirsch Robert F
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106 USA.
IEEE Trans Neural Syst Rehabil Eng. 2009 Feb;17(1):80-90. doi: 10.1109/TNSRE.2008.2010480.
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.
本项目的总体目标是利用功能性电刺激（FES）为C5/C6脊髓损伤（SCI）患者提供肩肘功能，提高目前手部神经假体所提供的功能结果。本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，该控制器从仍受自主控制的肌肉活动中提取信息，以预测上肢几块瘫痪肌肉的适当刺激水平。使用经过修改以反映C5 SCI和FES能力的手臂肌肉骨骼模型，通过模拟获得的激活数据对人工神经网络进行训练。记录了健全受试者的几种手臂运动，这些运动学数据作为逆动力学模拟的输入，该模拟预测了与记录的运动相对应的肌肉激活模式。使用系统识别程序从C5 SCI中通常受自主控制的较大肌肉组中识别出一组最佳的减少后的自主输入肌肉。这些自主激活用作人工神经网络的输入，而C5 SCI中通常瘫痪的肌肉则是要预测的输出。神经网络控制器能够从“自主”激活中预测所需的FES瘫痪肌肉激活，均方根预测误差小于3.6%。
