Publication: Prediction gait during ascending stair by using Artificial Neural Networks
dc.contributor.author | Thunyanoot Prasertsakul | en_US |
dc.contributor.author | Jutamanee Poonsiri | en_US |
dc.contributor.author | Warakorn Charoensuk | en_US |
dc.contributor.other | Mahidol University | en_US |
dc.date.accessioned | 2018-06-11T04:47:14Z | |
dc.date.available | 2018-06-11T04:47:14Z | |
dc.date.issued | 2012-12-01 | en_US |
dc.description.abstract | Walking up or down stairs is an important activity for human lives. Gait pattern of this activity is as same as walking except the range of motion and phase of muscles activities. Many studies have been focused on behavior of this motion. Two cameras and electromyogram (EMG) are the applications used in this study and analysis the motion. To determine the relationship of the both data, it can be performed in many techniques but in this study used artificial neural network model. Nonlinear Autoregressive model with exogenous (NARX) input was applied to this study to define the relationship between the electromyogram of eight muscles and angular displacement of knee and ankle joints of both legs. The results show that the predicted data from NARX were similar to the measured data. ©2012 IEEE. | en_US |
dc.identifier.citation | 5th 2012 Biomedical Engineering International Conference, BMEiCON 2012. (2012) | en_US |
dc.identifier.doi | 10.1109/BMEiCon.2012.6465464 | en_US |
dc.identifier.other | 2-s2.0-84875100431 | en_US |
dc.identifier.uri | https://repository.li.mahidol.ac.th/handle/20.500.14594/14114 | |
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=84875100431&origin=inward | en_US |
dc.subject | Engineering | en_US |
dc.title | Prediction gait during ascending stair by using Artificial Neural Networks | 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=84875100431&origin=inward | en_US |