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