Ameri Ali, Englehart Kevin B, Parker Phillip A
Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB, E3B 5A3, Canada.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1342-5. doi: 10.1109/EMBC.2012.6346186.
This work studies the simultaneous and proportional myoelectric force and position estimation of multiple degrees of freedom (DOFs) for unilateral transradial amputees. Two experiments were conducted to compare force and position control paradigms. In the first, a force experiment, subjects performed isometric contractions, while the force applied by the limb and EMG were recorded. In the second, a position experiment, dynamic contractions were permitted during which position of the limb and EMG were measured. Artificial neural networks (ANNs) were trained to estimate force/position from EMG of the contralateral limb during mirrored bilateral contractions. This study involved contractions with combined activations of three DOFs including wrist: flexion/extension, radial/ulnar deviation and forearm supination/pronation. For the given data set, while force estimation demonstrated high accuracy (R(2)=0.84±0.02), position estimation performance was relatively poor (R(2)=0.57±0.05). Two healthy subjects participated in this work.
这项工作研究了单侧经桡骨截肢者多自由度（DOF）的同步和比例肌电力量与位置估计。进行了两项实验以比较力量和位置控制范式。在第一个实验，即力量实验中，受试者进行等长收缩，同时记录肢体施加的力量和肌电图。在第二个实验，即位置实验中，允许进行动态收缩，在此期间测量肢体位置和肌电图。训练人工神经网络（ANN）以在镜像双侧收缩期间从对侧肢体的肌电图估计力量/位置。本研究涉及包括手腕三个自由度联合激活的收缩：屈伸、桡尺偏斜和前臂旋前/旋后。对于给定的数据集，虽然力量估计显示出高精度（R(2)=0.84±0.02），但位置估计性能相对较差（R(2)=0.57±0.05）。两名健康受试者参与了这项工作。
