| dc.contributor.author | Aung, Yee Mon | |
| dc.contributor.author | Anam, Khairul | |
| dc.contributor.author | Al-Jumaily, Adel | |
| dc.date.accessioned | 2017-10-11T08:02:03Z | |
| dc.date.available | 2017-10-11T08:02:03Z | |
| dc.date.issued | 2017-10-11 | |
| dc.identifier.isbn | 978-1-4673-6387-7 | |
| dc.identifier.uri | http://repository.unej.ac.id/handle/123456789/82069 | |
| dc.description | 7th Annual International IEEE EMBS Conference on Neural Engineering
Montpellier, France, 22 - 24 April, 2015 | en_US |
| dc.description.abstract | Continuous prediction of dynamic joint angle
from surface electromyography (sEMG) signal is one of the
most important applications in rehabilitation area for stroke
survivors as these can directly reflect the user motor intention.
In this study, new shoulder joint angle prediction method in
real-time based on the biosignal: sEMG is proposed. Firstly,
sEMG to muscle activation model is built up to extract the user
intention from contracted muscles and then feed into the
extreme learning machine (ELM) to estimate the angle in realtime
continuously. The estimated joint angle is then compare
with the webcam captured joint angle to analyze the
effectiveness of the proposed method. The result reveals that
correlation coefficient between actual angle and estimated
angle is as high as 0.96 in offline and 0.93 in online mode. In
addition, the processing time for the estimation is less than
32ms in both cases which is within the semblance of human
natural movements. Therefore, the proposed method is able to
predict the user intended movement very well and naturally
and hence, it is suitable for real-time applications. | en_US |
| dc.language.iso | en | en_US |
| dc.subject | Shoulder Joint Angle | en_US |
| dc.subject | surface electromyography (sEMG) | en_US |
| dc.title | Continuous Prediction of Shoulder Joint Angle in Real-Time | en_US |
| dc.type | Prosiding | en_US |
