Restoration of complex movement in the paralyzed upper limb.
Restoration of complex movement in the paralyzed upper limb.
复制标题
瘫痪上肢复杂运动的恢复。
DOI：
10.1088/1741-2552/ac7ad7
复制
发表时间：
2022-07-01
影响因子：
4
通讯作者：
Fuglevand, Andrew J.
中科院分区：
文献类型：
--
作者：
Hasse, Brady A.;Sheets, Drew E. G.;Holly, Nicole L.;Gothard, Katalin M.;Fuglevand, Andrew J.
功能性电刺激（FES）涉及人工激活骨骼肌以恢复瘫痪个体的运动功能。虽然应用于上肢的FES改善了四肢瘫痪患者进行日常生活活动的能力，但仍存在阻碍其广泛使用的关键缺陷。一个主要的限制是，可以产生的运动行为的范围被限制在一小部分简单的、预先编程的运动。这种限制源于在确定产生更复杂运动所需的许多肌肉的刺激模式方面的巨大困难。因此，本研究的目的是使用机器学习来灵活地识别引起广泛的多关节手臂运动所需的肌肉刺激模式。手臂运动学和肌电图活动从29块肌肉被记录下来，而一个“教练”猴子作出了广泛的手臂运动。这些数据被用来训练一个人工神经网络，预测与一组新动作相关的肌肉活动模式。这些模式被转换成一系列刺激脉冲，传递到另外两只暂时瘫痪的猴子的上肢肌肉。基于机器学习的EMG预测对于受试者内预测是好的，但对于跨受试者预测明显较差。只有在某些情况下，诱发反应才能以良好的保真度匹配所需的运动。与FES诱发的运动，以减轻错误的手段进行了讨论。由于我们的方法可以产生的运动范围几乎是无限的，因此该系统可以大大扩展高度瘫痪患者的运动技能。
Functional electrical stimulation (FES) involves artificial activation of skeletal muscles to reinstate motor function in paralyzed individuals. While FES applied to the upper limb has improved the ability of tetraplegics to perform activities of daily living, there are key shortcomings impeding its widespread use. One major limitation is that the range of motor behaviors that can be generated is restricted to a small set of simple, preprogrammed movements. This limitation stems from the substantial difficulty in determining the patterns of stimulation across many muscles required to produce more complex movements. Therefore, the objective of this study was to use machine learning to flexibly identify patterns of muscle stimulation needed to evoke a wide array of multi-joint arm movements. Arm kinematics and electromyographic activity from 29 muscles were recorded while a ‘trainer’ monkey made an extensive range of arm movements. Those data were used to train an artificial neural network that predicted patterns of muscle activity associated with a new set of movements. Those patterns were converted into trains of stimulus pulses that were delivered to upper limb muscles in two other temporarily paralyzed monkeys. Machine-learning based prediction of EMG was good for within-subject predictions but appreciably poorer for across-subject predictions. Evoked responses matched the desired movements with good fidelity only in some cases. Means to mitigate errors associated with FES-evoked movements are discussed. Because the range of movements that can be produced with our approach is virtually unlimited, this system could greatly expand the repertoire of movements available to individuals with high level paralysis.
登录
查看更多内容
影响因子：
3.2
作者：
Blana, Dimitra;Kirsch, Robert F.;Chadwick, Edward K.
通讯作者：
Chadwick, Edward K.
影响因子：
4.6
作者：
CRAGO, PE;NAKAI, RJ;CHIZECK, HJ
通讯作者：
CHIZECK, HJ
影响因子：
5.1
作者：
Awad, Louis N.;Esquenazi, Alberto;Jayaraman, Arun
通讯作者：
Jayaraman, Arun
DOI：
10.1523/jneurosci.1435-10.2010
发表时间：
2010-08-11
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Carmel JB;Berrol LJ;Brus-Ramer M;Martin JH
通讯作者：
Martin JH
影响因子：
2.5
作者：
Anderson, Chad V.;Fuglevand, Andrew J.
通讯作者：
Fuglevand, Andrew J.