Computer-based test-bed for clinical assessment of hand/wrist feed-forward neuroprosthetic controllers using artificial neural networks.
Computer-based test-bed for clinical assessment of hand/wrist feed-forward neuroprosthetic controllers using artificial neural networks.
复制标题
基于计算机的测试台，用于使用人工神经网络对手/腕前馈神经假体控制器进行临床评估。
DOI：
10.1007/bf02345208
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发表时间：
2004
影响因子：
3.2
通讯作者：
Crago,PE
中科院分区：
文献类型：
--
作者：
Luján,JL;Crago,PE
神经修复系统可用于恢复C5/C6脊髓损伤患者的手部抓握和腕部控制。开发了一个基于计算机的系统，用于神经假体控制器的实施、调整和临床评估，使用现成的硬件和软件。计算机系统将运行Windows NT的Pentium III PC变成了一个非专用的实时系统，用于控制神经假体。软件执行（使用高级编程语言LabVIEW和MATLAB编写）分为两个阶段：训练和实时控制。在训练阶段，计算机系统通过刺激肌肉并实时测量肌肉输出来收集输入/输出数据，分析记录的数据，生成一组训练数据并训练基于人工神经网络（ANN）的控制器。在实时控制期间，计算机系统响应于来自外部命令源的采样输入，使用由ANN控制器预测的刺激脉冲宽度来刺激肌肉，以提供手抓握和手腕姿势的独立控制。系统定时稳定、可靠，能够以高达24 Hz的频率提供肌肉刺激。为了演示试验台的应用，实现了一个基于人工神经网络的控制器，具有三个输入和两个独立的刺激通道。人工神经网络控制器的能力，以控制手的把握和手腕角度独立进行了评估，通过定量比较的输出的刺激肌肉与一组所需的把握或手腕的姿势确定的命令信号。控制器性能结果好坏参半，但该平台提供了实现和评估未来控制器设计的工具。
Neuroprosthestic systems can be used to restore hand grasp and wrist control in individuals with C5/C6 spinal cord injury. A computer-based system was developed for the implementation, tuning and clinical assessment of neuroprosthetic controllers, using off-the-shelf hardware and software. The computer system turned a Pentium III PC running Windows NT into a non-dedicated, real-time system for the control of neuroprostheses. Software execution (written using the high-level programming languages LabVIEW and MATLAB) was divided into two phases: training and real-time control. During the training phase, the computer system collected input/output data by stimulating the muscles and measuring the muscle outputs in real-time, analysed the recorded data, generated a set of training data and trained an artificial neural network (ANN)-based controller. During real-time control, the computer system stimulated the muscles using stimulus pulsewidths predicted by the ANN controller in response to a sampled input from an external command source, to provide independent control of hand grasp and wrist posture. System timing was stable, reliable and capable of providing muscle stimulation at frequencies up to 24 Hz. To demonstrate the application of the test-bed, an ANN-based controller was implemented with three inputs and two independent channels of stimulation. The ANN controller's ability to control hand grasp and wrist angle independently was assessed by quantitative comparison of the outputs of the stimulated muscles with a set of desired grasp or wrist postures determined by the command signal. Controller performance results were mixed, but the platform provided the tools to implement and assess future controller designs.