Classifying motion intention from EMG signal: A kNN approach
The use of robotic systems has been investigated over the past couple of decades in improving rehabilitation training of hemiplegic patients. In an ideal situation, the system should be able to detect the intention of the subject and assist them as needed in performing certain training tasks. In...
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Main Authors: | , , , |
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Format: | Conference or Workshop Item |
Language: | English English |
Published: |
IEEE
2020
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Subjects: | |
Online Access: | http://irep.iium.edu.my/78714/8/78714%20Classifying%20Motion%20Intention%20from%20EMG%20signal.pdf http://irep.iium.edu.my/78714/9/78714%20Classifying%20Motion%20Intention%20from%20EMG%20signal%20SCOPUS.pdf http://irep.iium.edu.my/78714/ https://ieeexplore.ieee.org/document/8952042 |
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Summary: | The use of robotic systems has been investigated
over the past couple of decades in improving rehabilitation
training of hemiplegic patients. In an ideal situation, the system
should be able to detect the intention of the subject and assist
them as needed in performing certain training tasks. In this
study, we leverage on the information from the electromyogram
(EMG) signals, to detect the subject’s intentions in generating
motion commands for a robotic assisted upper limb
rehabilitation system. As EMG signals are known for its very
low amplitude apart from its susceptibility to noise, hence, signal
processing is mandatory, and this step is non-trivial for feature
extraction. The EMG signals are recorded from ten healthy
subjects’ bicep muscles, who are required to provide a voluntary
movement of the elbow’s flexion and extension along the sagittal
plane. The signals are filtered by a fifth-order Butterworth
filter. Several features were extracted from the filtered signals
namely waveform length, mean absolute value, root mean
square and standard deviation. Two different classifiers viz.
Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) were investigated on its efficacy in accurately classifying the
pre-intention and intention classes based on the selected
features, and it was observed from this investigation that the kNN classifier yielded a better classification with a classification
accuracy of 96.4 % |
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