Publication: Human postural stability model and artificial neural network for prediction the center of pressure
dc.contributor.author | Thunyanoot Prasertsakul | en_US |
dc.contributor.author | Yodchanan Wongsawat | en_US |
dc.contributor.author | Warakorn Charoensuk | en_US |
dc.contributor.other | Mahidol University | en_US |
dc.date.accessioned | 2018-11-09T02:14:26Z | |
dc.date.available | 2018-11-09T02:14:26Z | |
dc.date.issued | 2014-01-01 | en_US |
dc.description.abstract | © 2014 IEEE. Human postural stability is the necessary function for human livings. This function controls the whole body into the upright position. To understand the behavior of human balance control can be achieved by many methods. The mathematical model is a general technique for explanation the mechanism of biomechanics. The human postural stability system has been designed into the mathematical model. The model at sagittal and coronal plane utilized to describe the motion. This study focused on the model at coronal plane. There were two methods which performed. The first method was to use the mathematical formula for determination the COP. Second, there was the human postural stability model and artificial neural network to predict the COP. The result indicated that both methods could determine the COP, but the neural network has less error than the other method. However, there was some limitation to define the suitable parameter of neural network for getting better output. | en_US |
dc.identifier.citation | 2014 International Electrical Engineering Congress, iEECON 2014. (2014) | en_US |
dc.identifier.doi | 10.1109/iEECON.2014.6925900 | en_US |
dc.identifier.other | 2-s2.0-84911912567 | en_US |
dc.identifier.uri | https://repository.li.mahidol.ac.th/handle/20.500.14594/33843 | |
dc.rights | Mahidol University | en_US |
dc.rights.holder | SCOPUS | en_US |
dc.source.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84911912567&origin=inward | en_US |
dc.subject | Engineering | en_US |
dc.title | Human postural stability model and artificial neural network for prediction the center of pressure | en_US |
dc.type | Conference Paper | en_US |
dspace.entity.type | Publication | |
mu.datasource.scopus | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84911912567&origin=inward | en_US |