Winslow Jeffrey, Jacobs Patrick L, Tepavac Dejan
The Miami Project To Cure Paralysis, University of Miami School of Medicine, Miami, FL 33101, USA.
J Electromyogr Kinesiol. 2003 Dec;13(6):555-68. doi: 10.1016/s1050-6411(03)00055-5.
Muscle fatigue limits the effectiveness of FES when applied to regain functional movements in spinal cord injured (SCI) individuals. The stimulation intensity must be manually increased to provide more force output to compensate for the decreasing muscle force due to fatigue. An artificial neural network (ANN) system was designed to compensate for muscle fatigue during functional electrical stimulation (FES) by maintaining a constant joint angle. Surface electromyography signals (EMG) from electrically stimulated muscles were used to determine when to increase the stimulation intensity when the muscle's output started to drop. In two separate experiments on able-bodied subjects seated in hard back chairs, electrical stimulation was continuously applied to fatigue either the biceps (during elbow flexion) or the quadriceps muscle (during leg extension) while recording the surface EMG. An ANN system was created using processed surface EMG as the input, and a discrete fatigue compensation control signal, indicating when to increase the stimulation current, as the output. In order to provide training examples and test the systems' performance, the stimulation current amplitude was manually increased to maintain constant joint angles. Manual stimulation amplitude increases were required upon observing a significant decrease in the joint angle. The goal of the ANN system was to generate fatigue compensation control signals in an attempt to maintain a constant joint angle. On average, the systems could correctly predict 78.5% of the instances at which a stimulation increase was required to maintain the joint angle. The performance of these ANN systems demonstrates the feasibility of using surface EMG feedback in an FES control system.
当应用功能性电刺激（FES）来恢复脊髓损伤（SCI）个体的功能性运动时，肌肉疲劳会限制其有效性。必须手动增加刺激强度，以提供更大的力输出，来补偿因疲劳导致的肌肉力量下降。设计了一种人工神经网络（ANN）系统，通过维持恒定的关节角度来补偿功能性电刺激（FES）过程中的肌肉疲劳。来自电刺激肌肉的表面肌电图信号（EMG）用于确定当肌肉输出开始下降时何时增加刺激强度。在两项针对坐在硬背椅上的健全受试者的独立实验中，持续施加电刺激使肱二头肌（在肘部屈曲期间）或股四头肌（在腿部伸展期间）疲劳，同时记录表面肌电图。使用处理后的表面肌电图作为输入创建了一个人工神经网络系统，并将表示何时增加刺激电流的离散疲劳补偿控制信号作为输出。为了提供训练示例并测试系统性能，手动增加刺激电流幅度以维持恒定的关节角度。在观察到关节角度显著下降时，需要手动增加刺激幅度。人工神经网络系统的目标是生成疲劳补偿控制信号，以试图维持恒定的关节角度。平均而言，该系统能够正确预测78.5%的需要增加刺激以维持关节角度的情况。这些人工神经网络系统的性能证明了在FES控制系统中使用表面肌电图反馈的可行性。
