DocumentCode :
2861694
Title :
A neural network based technique for muscle coordination and vertical jump height prediction
Author :
Verma, Brijesh ; Lane, Chris
Author_Institution :
Sch. of Inf. Technol., Griffith Univ., Brisbane, Qld., Australia
Volume :
3
fYear :
1998
fDate :
4-9 May 1998
Firstpage :
2163
Abstract :
The purpose of this study was to investigate the training of artificial neural networks (ANNs) to predict muscle coordination and vertical jump height. The paper presents the structure and training techniques of ANNs that give the best prediction of muscle coordination and jump height. In training the ANNs different electromyography (EMG) characteristics were investigated for optimal ANN muscle coordination and jump height prediction. The technique has been implemented in C++ on the SP2 supercomputer. The preliminary results are very promising, some of which are presented in this paper
Keywords :
biomechanics; electromyography; learning (artificial intelligence); neurophysiology; SP2 supercomputer; electromyography characteristics; muscle coordination; neural network based technique; vertical jump height prediction; Artificial neural networks; Biological control systems; Control systems; Electromyography; Information technology; Muscles; Nervous system; Neural networks; Performance analysis; Switches;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
ISSN :
1098-7576
Print_ISBN :
0-7803-4859-1
Type :
conf
DOI :
10.1109/IJCNN.1998.687195
Filename :
687195
Link To Document :
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