Lower extremity joint torque predicted by using artificial neural network during vertical jump
Lower extremity joint torque predicted by using artificial neural network during vertical jump
复制标题
利用人工神经网络预测垂直弹跳时下肢关节扭矩
DOI：
10.1016/j.jbiomech.2009.01.033
复制
发表时间：
2009-05-11
影响因子：
2.4
通讯作者：
Li, Li
中科院分区：
文献类型：
--
作者：
Liu, Yu;Shih, Shi-Min;Li, Li
本研究的目的是建立一个人工神经网络（ANN），利用地面反作用力（GRF）和由GRF导出的相关参数来预测下肢关节在反运动跳（CMJ）和蹲跳（SJ）中的扭矩。10名学生运动员表演了CMJ和SJ。记录受力板和运动学数据。利用逆动力学和人工神经网络计算关节力矩。我们使用了一个完全连接的前馈网络。该网络由一个输入层、一个隐藏层和一个输出层组成。采用最陡下降法对误差反向传播算法进行训练。神经网络的输入参数为GRF测量值及相关参数。输出参数为三个下肢关节扭矩。人工神经网络模型与逆动力学输出结果拟合良好。研究结果表明，基于地面反力数据和相关参数，该模型可用于估算CMJ和SJ的三个下肢关节扭矩。Elsevier Ltd.出版。
The purpose of this study was to develop an artificial neural network (ANN) for predicting lower extremity joint torques using the ground reaction force (GRF) and related parameters derived by the GRF during counter-movement jump (CMJ) and squat jump (SJ). Ten student athletes performed CMJ and SJ. Force plate and kinematic data were recorded. joint torques were calculated using inverse dynamics and ANN. We used a fully connected, feed-forward network. The network comprised of one input layer, one hidden layer and one output layer. It was trained by error back-propagation algorithm using Steepest Descent Method. Input parameters of the ANN were GRF measurements and related parameters. Output parameters were three lower extremity joint torques. ANN model fitted well with the results of the inverse dynamics output. Our observations indicate that the model developed in this study can be used to estimate three lower extremity joint torques for CMJ and SJ based on ground reaction force data and related parameters. Published by Elsevier Ltd.