Estimation of the Dynamic Spinal Forces Using a Recurrent Fuzzy Neural Network
Estimation of the Dynamic Spinal Forces Using a Recurrent Fuzzy Neural Network
复制标题
使用循环模糊神经网络估计动态脊柱力
DOI：
--
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
K. Davis
中科院分区：
文献类型：
--
作者：
Y. Hou;J. Zurada;W. Karwowski;W. Marras;K. Davis
从运动学数据估计动态脊柱力是非常复杂的，因为它涉及到运动学变量和肌电图（EMG）信号之间的关系，以及EMG信号和力之间的关系的处理。提出了一种递归模糊神经网络（RFNN）模型来建立运动学-肌电-力的关系，并对肌肉活动的动力学进行建模。肌电信号被用作中间输出，并被反馈到输入层。由于肌电是肌肉活动的直接反映，因此该模型的反馈具有物理意义。它以一种简单的方式表达肌肉活动的动力学，并利用了循环特性。然后，经过训练的模型可以直接从运动学变量预测力，同时绕过测量EMG信号的昂贵过程，并避免使用生物力学模型。推导了RFNN模型的学习算法
Estimation of the dynamic spinal forces from kinematics data is very complicated because it involves the handling of the relationship between kinematic variables and electromyography (EMG) signals, as well as the relationship between EMG signals and the forces. A recurrent fuzzy neural network (RFNN) model is proposed to establish the kinematics-EMG-force relationship and model the dynamics of muscular activities. The EMG signals are used as an intermediate output and are fed back to the input layer. Since EMG is a direct reflection of muscular activities, the feedback of this model has a physical meaning. It expresses the dynamics of muscular activities in a straightforward way and takes advantage from the recurrent property. The trained model can then have the forces predicted directly from kinematic variables while bypassing the costly procedure of measuring EMG signals and avoiding the use of a biomechanics model. A learning algorithm is derived for the RFNN model