ACADSTAFF UGM

CREATION
Title : Robust Features for Elbow Joint Angle Estimation Based on Electromyography
Author :

TRIWIYANTO (1) Prof. Ir. Oyas Wahyunggoro, MT., Ph.D. (2) Prof. Ir. Hanung Adi Nugroho, S.T., M.Eng., Ph.D., IPM., SMIEEE. (3) Prof. Dr.Eng. Ir. Herianto, S.T., M.Eng., IPU., ASEAN Eng. (4)
Date : 0 2018
Keyword : EMG, feature extraction, Kalman filter, white Gaussian noise, elbow joint angle estimation EMG, feature extraction, Kalman filter, white Gaussian noise, elbow joint angle estimation
Abstract : In order to obtain a good performance of the estimation in the noisy environment, a robust feature is essential in elbow joint angle estimation based on electromyography (EMG). The purpose of this study is to modify and evaluate the time domain features which robust against the white Gaussian noise. In this work, the EMG signal (from biceps) contaminated by artificial white Gaussian noise was extracted using twelve standard time domain features and one modified feature. The threshold of the modified feature (MYOPM) was calculated based on the root mean square (RMS) of the contaminated EMG signal. The Kalman filter was used to filter the features and to improve the performance of the estimation. The robustness of the features was evaluated using the root mean square error (RMSE). The result shows that the proposed feature MYOPM is the most robust feature (the lowest median RMSE of 9º) for the signal to noise ratio (SNR) ranged from 17.96 to 60 dB, compared with the others’ features. The mean RMSE of the MYOPM feature improves by 27.91% from the prior feature (MYOP).
Group of Knowledge : Teknik Elektro
Original Language : English
Level : Internasional
Status :
Published
Document
No Title Document Type Action
1 Robust Features for Elbow Joint Angle Estimation Based on Electromyography.pdf
Document Type : [PAK] Full Dokumen
[PAK] Full Dokumen View
2 20 Similarity Robust Features for Elbow Joint Angle Estimation Based on Electromyography.pdf
Document Type : [PAK] Cek Similarity
[PAK] Cek Similarity View
3 26 Suket Publikasi PAK_Robust Features for Elbow.pdf
Document Type : Dokumen Pendukung Karya Ilmiah (Hibah, Publikasi, Penelitian, Pengabdian)
Dokumen Pendukung Karya Ilmiah (Hibah, Publikasi, Penelitian, Pengabdian) View