Probability-based prediction of activity in multiple arm muscles: implications for functional electrical stimulation
Probability-based prediction of activity in multiple arm muscles: implications for functional electrical stimulation
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DOI：
10.1152/jn.00956.2007
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发表时间：
2008-07-01
影响因子：
2.5
通讯作者：
Fuglevand, Andrew J.
中科院分区：
文献类型：
--
作者：
Anderson, Chad V.;Fuglevand, Andrew J.
功能性电刺激（FES）涉及用植入的电极人工激活肌肉以恢复瘫痪个体的运动功能。然而，FES可以产生的运动行为的范围仅限于一小部分预先编程的运动，例如手的抓握和释放。更广泛的运动尚未实现，因为识别引起指定运动所需的肌肉刺激模式存在相当大的困难。为了克服这种限制，在控制FES系统，我们使用概率的方法来估计在很大范围的自由运动的基础上上肢的运动学信息的人的手臂的肌肉活动的水平。条件概率分布的基础上产生的手运动学和相关的表面肌电图（EMG）信号从12个手臂肌肉记录在训练任务，涉及随机运动的手臂在一个主题。然后，这些分布被用来预测其他四个科目的肌肉活动与八个不同的运动任务的模式。平均而言，实际EMG信号中的方差的约40%可以在预测的EMG信号中解释。这些结果表明，概率的方法最终可能被用来预测的肌肉刺激所需的模式，以产生广泛的所需的运动与FES瘫痪的个人。
Functional electrical stimulation (FES) involves artificial activation of muscles with implanted electrodes to restore motor function in paralyzed individuals. The range of motor behaviors that can be generated by FES, however, is limited to a small set of preprogrammed movements such as hand grasp and release. A broader range of movements has not been implemented because of the substantial difficulty associated with identifying the patterns of muscle stimulation needed to elicit specified movements. To overcome this limitation in controlling FES systems, we used probabilistic methods to estimate the levels of muscle activity in the human arm during a wide range of free movements based on kinematic information of the upper limb. Conditional probability distributions were generated based on hand kinematics and associated surface electromyographic (EMG) signals from 12 arm muscles recorded during a training task involving random movements of the arm in one subject. These distributions were then used to predict in four other subjects the patterns of muscle activity associated with eight different movement tasks. On average, about 40% of the variance in the actual EMG signals could be accounted for in the predicted EMG signals. These results suggest that probabilistic methods ultimately might be used to predict the patterns of muscle stimulation needed to produce a wide array of desired movements in paralyzed individuals with FES.