Signal processing of EMG signal for continuous thumbangle estimation
Human hand functions range from precise-minute handling to heavy and robust movements. 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 r...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2016
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/52509/ http://irep.iium.edu.my/52509/ http://irep.iium.edu.my/52509/14/52509-Signal%20processing%20of%20EMG%20signal%20for%20continuous%20thumb-angle%20estimation_SCOPUS.pdf http://irep.iium.edu.my/52509/15/52509-updated.pdf |
| Summary: | Human hand functions range from precise-minute
handling to heavy and robust movements. 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 total imitation. The paper looks into the classification of
Electromyogram (EMG) signals from thumb muscles for the
prediction of thumb angle during flexion motion. For this
purpose, an experimental setup is developed to measure the
thumb angle throughout the range of flexion and simultaneously
gather the EMG signals. Further various different features are
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. The most determinant features are
found to be the 2nd order Auto-regressive (AR) coefficients with
Simple Square Integral (SSI) giving an accuracy of 85.41% in
average while the best classification method is Support Vector
Machine (SVM - with Puk kernel) with an average accuracy of
86.53%. |
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