Lower extremity joint torque predicted by using artificial neural network during vertical jump.
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.
Liu
Yu
Y
School of Kinesiology, Shanghai University of Sport, 200438 Shanghai, China.
Shih
Shi-Min
SM
Tian
Shi-Liu
SL
Zhong
Yun-Jian
YJ
Li
Li
L
D033023:Foot Joints / Q000502:physiology*; D006801:Humans; D035002:Lower Extremity / Q000502:physiology*; D008297:Male; D016571:Neural Networks, Computer; D013177:Sports / Q000502:physiology*; D019415:Torque; D055815:Young Adult