跳跃
扭矩
接头(建筑物)
地面反作用力
运动学
逆动力学
人工神经网络
控制理论(社会学)
蹲下
数学
计算机科学
工程类
物理
结构工程
物理医学与康复
人工智能
经典力学
医学
控制(管理)
量子力学
热力学
作者
Yu Liu,Stephen H. Shih,Shuo Tian,Yanjun Zhong,Li Li
标识
DOI:10.1016/j.jbiomech.2009.01.033
摘要
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.
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