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.

[1]  M Haeri,et al.  Modeling of pain using artificial neural networks. , 2003, Journal of theoretical biology.

[2]  T Chau,et al.  A review of analytical techniques for gait data. Part 2: neural network and wavelet methods. , 2001, Gait & posture.

[3]  E Michel,et al.  Artificial neural network for predicting intracranial haemorrhage in preterm neonates , 1998, Acta paediatrica.

[4]  R Begg,et al.  A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. , 2005, Journal of biomechanics.

[5]  A M Walker,et al.  Prediction and cross-validation of neural networks versus logistic regression: using hepatic disorders as an example. , 1998, American journal of epidemiology.

[6]  D. Winter,et al.  Moments of force and mechanical power in jogging. , 1983, Journal of biomechanics.

[7]  Rong Song,et al.  Using recurrent artificial neural network model to estimate voluntary elbow torque in dynamic situations , 2005, Medical and Biological Engineering and Computing.

[8]  Georges Dalleau,et al.  Validity and Reliability of a Kinematic Device for Measuring the Force Developed During Squatting , 2000 .

[9]  Kenzo Akazawa,et al.  Estimating Torque-Angle Relations of Human Elbow Joint in Isovelocity Flexion Movements , 2006, IEICE Trans. Inf. Syst..

[10]  W I Schöllhorn,et al.  Applications of artificial neural nets in clinical biomechanics. , 2004, Clinical biomechanics.

[11]  Ronald F. Zernicke,et al.  Biomechanical Insights into Neural Control of Movement , 2011 .

[12]  M. Hahn.  Feasibility of estimating isokinetic knee torque using a neural network model. , 2007, Journal of biomechanics.

[13]  J. Tihanyi,et al.  A dynamometer for evaluation of dynamic muscle work , 2004, European Journal of Applied Physiology and Occupational Physiology.

[14]  Stefan Holzreiter.  Autolabeling 3D tracks using neural networks. , 2005, Clinical biomechanics.

[15]  Lin Wang,et al.  Prediction of joint moments using a neural network model of muscle activations from EMG signals , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  D. Winter,et al.  Overall principle of lower limb support during stance phase of gait. , 1980, Journal of biomechanics.

[17]  K. Akazawa,et al.  Static torque-angle relation of human elbow joint estimated with artificial neural network technique. , 1998, Journal of biomechanics.

[18]  W Herzog,et al.  Dynamic muscle force predictions from EMG: an artificial neural network approach. , 1999, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[19]  B M Nigg,et al.  Contribution of the lower extremity joints to mechanical energy in running vertical jumps and running long jumps. , 1998, Journal of sports sciences.

[20]  G E Caldwell,et al.  Improved agreement of foot segmental power and rate of energy change during gait: inclusion of distal power terms and use of three-dimensional models. , 1996, Journal of biomechanics.

[21]  T. Kuo,et al.  Isokinetic elbow joint torques estimation from surface EMG and joint kinematic data: using an artificial neural network model. , 1999, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[22]  G Van der Perre,et al.  Subject-specific hip geometry affects predicted hip joint contact forces during gait. , 2007, Journal of biomechanics.

[23]  D. Gordon E. Robertson,et al.  Research Methods in Biomechanics , 2004 .