Durand Dm, Park H J, Wodlinger B
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3326-9. doi: 10.1109/IEMBS.2009.5333754.
Functional electrical stimulation (FES) can restore volitional motion of patients with neurological injuries or diseases using electrical stimulation of nerves innervating the muscles to be controlled independently. The Flat interface nerve electrode (FINE) enables the selective control of different muscles at the same time. In addition, multiple contact electrode designs allow selective recording of the various signals within the cuff. However, motion control of neuromuscular skeletal systems using multi-contact electrodes is a challenging problem due to the complexities of the systems and the large number of channels required to activate the various muscles involved in the motion. The localization and the recovery of many signals pose a significant challenge to the low signals to noise ratio and the large number of fascicles. Using computer models of the peripheral nerve, we have tested the ability of various algorithms to control the neuromuscular skeletal dynamics. Computer models have also been used to develop new methods to recover fascicular signals within the nerve. Both the control and the detection algorithms are currently being tested experimentally and preliminary results are included. The goal of this study is to develop the ability to detect nerve signals and use these signals to control joint motion in patients with stroke, amputation or paralysis.
功能性电刺激（FES）可以通过对支配待独立控制肌肉的神经进行电刺激，来恢复神经损伤或疾病患者的自主运动。平面界面神经电极（FINE）能够同时对不同肌肉进行选择性控制。此外，多种接触电极设计允许在袖套内选择性记录各种信号。然而，由于神经肌肉骨骼系统的复杂性以及激活运动中涉及的各种肌肉所需的大量通道，使用多接触电极对神经肌肉骨骼系统进行运动控制是一个具有挑战性的问题。许多信号的定位和恢复对低信噪比以及大量神经束构成了重大挑战。利用周围神经的计算机模型，我们测试了各种算法控制神经肌肉骨骼动力学的能力。计算机模型也被用于开发新方法来恢复神经内的神经束信号。控制算法和检测算法目前都在进行实验测试，并给出了初步结果。本研究的目标是培养检测神经信号的能力，并利用这些信号控制中风、截肢或瘫痪患者的关节运动。
