Prediction of muscle activity during loaded movements of the upper limb.
Prediction of muscle activity during loaded movements of the upper limb.
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DOI：
10.1186/1743-0003-12-6
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发表时间：
2015-01-15
影响因子：
5.1
通讯作者：
Fuglevand AJ
中科院分区：
文献类型：
--
作者：
Tibold R;Fuglevand AJ
从理论上讲，对与各种运动行为相关的肌电图（EMG）信号进行准确预测，可作为使用功能性电刺激在瘫痪个体中诱发运动所需的活动模板。此类预测应涵盖复杂的多关节运动，并包括与环境中物体的相互作用。
在此，我们测试了不同的人工神经网络（ANN）在人类受试者自由活动手臂或抓取并移动不同重量和尺寸的物体时预测12块手臂肌肉的肌电活动的能力。经过训练的人工神经网络的输入包括手部位置、手部方向和拇指握力。
人工神经网络预测肌电的能力在涉及与外部负载相互作用的任务中与在无负载运动中同样出色。预测效果最佳的人工神经网络是一个前馈网络，它由一个包含30个神经元的单一隐藏层组成。对于该网络，在涉及移动物体的运动过程中，所有9名受试者和12块肌肉的预测肌电信号与实际肌电信号之间的平均决定系数（R²值）为0.43。
这种合理的准确性表明，人工神经网络可用于对瘫痪个体产生包括涉及物体相互作用的各种运动所需的复杂肌肉刺激模式进行初步估计。
Accurate prediction of electromyographic (EMG) signals associated with a variety of motor behaviors could, in theory, serve as activity templates needed to evoke movements in paralyzed individuals using functional electrical stimulation. Such predictions should encompass complex multi-joint movements and include interactions with objects in the environment. Here we tested the ability of different artificial neural networks (ANNs) to predict EMG activities of 12 arm muscles while human subjects made free movements of the arm or grasped and moved objects of different weights and dimensions. Inputs to the trained ANNs included hand position, hand orientation, and thumb grip force. The ability of ANNs to predict EMG was equally as good for tasks involving interactions with external loads as for unloaded movements. The ANN that yielded the best predictions was a feed-forward network consisting of a single hidden layer of 30 neural elements. For this network, the average coefficient of determination (R2 value) between predicted and actual EMG signals across all nine subjects and 12 muscles during movements that involved episodes of moving objects was 0.43. This reasonable accuracy suggests that ANNs could be used to provide an initial estimate of the complex patterns of muscle stimulation needed to produce a wide array of movements, including those involving object interaction, in paralyzed individuals.
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影响因子：
2.4
作者：
Blana, Dimitra;Hincapie, Juan G.;Kirsch, Robert F.
通讯作者：
Kirsch, Robert F.
影响因子：
4.6
作者：
HOSHIMIYA, N;NAITO, A;HANDA, Y
通讯作者：
HANDA, Y
影响因子：
3.8
作者：
Holzbaur, KRS;Murray, WM;Delp, SL
通讯作者：
Delp, SL
影响因子：
2.5
作者：
Kosterina, Natalia;Wang, Ruoli;Gutierrez-Farewik, Elena M.
通讯作者：
Gutierrez-Farewik, Elena M.
DOI：
10.1109/tnsre.2008.2010480
发表时间：
2009-02
期刊：
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
影响因子：
--
作者：
Hincapie JG;Kirsch RF
通讯作者：
Kirsch RF