Trajectory formation from surface emg signals using a neural network model
Trajectory formation from surface emg signals using a neural network model
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使用神经网络模型从表面肌电信号形成轨迹
DOI：
10.1109/iembs.1993.978946
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发表时间：
1993
期刊：
影响因子：
--
通讯作者：
M. Kawato
中科院分区：
文献类型：
--
作者：
Y. Koike;M. Kawato
在这项研究中，使用具有模块化架构的人工神经网络根据表面肌电图信号估计多关节手臂运动。通过训练人工神经网络，构建了人臂的正向动力学模型（FDM），该模型考虑了长度-张力和速度-张力曲线等非线性肌肉特性以及肌肉骨骼系统的复杂几何配置。 FDM 对于运动的计算研究特别有用，并且对于基于最优原理的平滑轨迹形成至关重要。它还可以用于阐明各种计算问题，例如运动过程中虚拟轨迹的直接计算，并且可能具有生物医学应用，例如瘫痪肢体的功能性电刺激。
In t.his study, multi-joint arm movements were estimated from surface EMG signals using an artificial neural network with a modular architecture. A forward dynamics model(FDM) of the human arm which takes into account non-linear muscle properties such as the length-tension and velocity-tension curves and complicated geometrical configurations of the musculo-skeletal system, was constructed by training the artificial neural network. The FDM is especially useful for computational study of movement, and is crucial to smooth trajectory formation based on an optimal principle. I t can also be used to elucidate various computational problems such as direct calculation of the virtual trajectories during movement, and may have biomedical applications such as functional electrical stimulat.ion of paralyzed limbs.