DocumentCode :
3685488
Title :
EMG-based learning approach for estimating wrist motion
Author :
S. El-Khoury;I. Batzianoulis;C. W. Antuvan;S. Contu;L. Masia;S. Micera;A. Billard
Author_Institution :
Learning Algorithms and Systems Laboratory (LASA) at Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland
fYear :
2015
Firstpage :
6732
Lastpage :
6735
Abstract :
This paper proposes an EMG based learning approach for estimating the displacement along the 2-axes (abduction/adduction and flexion/extension) of the human wrist in real-time. The algorithm extracts features from the EMG electrodes on the upper and forearm and uses Support Vector Regression to estimate the intended displacement of the wrist. Using data recorded with the arm outstretched in various locations in space, we train the algorithm so as to allow robust prediction even when the subject moves his/her arm across several positions in space. The proposed approach was tested on five healthy subjects and showed that a R2 index of 63.6% is obtained for generalization across different arm positions and wrist joint angles.
Keywords :
"Wrist","Muscles","Testing","Electromyography","Joints","Training","Estimation"
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE
ISSN :
1094-687X
Electronic_ISBN :
1558-4615
Type :
conf
DOI :
10.1109/EMBC.2015.7319938
Filename :
7319938
Link To Document :
بازگشت