EMG based classification for continuous thumb angle and force prediction
Human hand functions range from precise-minute handling to heavy and robust movements. Remarkably, 50 percent of all hand functions are made possible by the thumb. Therefore, developing an artificial thumb which can mimic the actions of a real thumb precisely is a majo...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2015
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/47317/ http://irep.iium.edu.my/47317/ http://irep.iium.edu.my/47317/ http://irep.iium.edu.my/47317/4/47317.pdf |
| Summary: | Human hand functions range from precise-minute
handling to heavy and robust movements. Remarkably, 50
percent of all hand functions are made possible by the thumb.
Therefore, developing an artificial thumb which can mimic the
actions of a real thumb precisely is a major achievement. Despite
many efforts dedicated to this area of research, control of
artificial thumb movements in resemblance to our natural
movement, still poses as a challenge. Most of the development in
this area is based on discontinuous thumb position control, which
makes it possible to recreate several of the most important
functions of the thumb but does not result in totalimitation. This
work looks into the classification of Electromyogram (EMG)
signals from thumb muscles for the prediction of thumb angle
and force during flexion motion. For this purpose, an
experimental setup is developed to measure the thumb angle &
force throughout the range of flexion and simultaneously gather
the EMG signals. Further various different featuresare extracted
from these signals for classification and the most suitable feature
set is determined and applied to different classifiers. A
‘piecewise- discretization’ approach is used for continuous angle
prediction. Breaking away from previous researches, the
frequency-domain features performed better than the timedomain features, with the best feature combination turning out to
be MDF-MNF-MNP. As for the classifiers, the Support Vector
Machine proved to be the most accurate classifier giving about
70% accuracy for both angle and force classification and close to
50% for joint angle-force classification. |
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