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...
| Main Authors: | , , , |
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| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
IEEE
2020
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/78714/ 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 |
| _version_ | 1848788656245964800 |
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| author | Mohd Khairuddin, Ismail Sidek, Shahrul Na'im Abdul Majeed, Anwar P.P. Ahmad Puzi, Asmarani |
| author_facet | Mohd Khairuddin, Ismail Sidek, Shahrul Na'im Abdul Majeed, Anwar P.P. Ahmad Puzi, Asmarani |
| author_sort | Mohd Khairuddin, Ismail |
| building | IIUM Repository |
| collection | Online Access |
| description | 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 % |
| first_indexed | 2025-11-14T17:44:17Z |
| format | Proceeding Paper |
| id | iium-78714 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:44:17Z |
| publishDate | 2020 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-787142020-04-07T03:39:05Z http://irep.iium.edu.my/78714/ Classifying motion intention from EMG signal: A kNN approach Mohd Khairuddin, Ismail Sidek, Shahrul Na'im Abdul Majeed, Anwar P.P. Ahmad Puzi, Asmarani T Technology (General) 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 % IEEE 2020-01-09 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/78714/8/78714%20Classifying%20Motion%20Intention%20from%20EMG%20signal.pdf application/pdf en http://irep.iium.edu.my/78714/9/78714%20Classifying%20Motion%20Intention%20from%20EMG%20signal%20SCOPUS.pdf Mohd Khairuddin, Ismail and Sidek, Shahrul Na'im and Abdul Majeed, Anwar P.P. and Ahmad Puzi, Asmarani (2020) Classifying motion intention from EMG signal: A kNN approach. In: 2019 7th International Conference on Mechatronics Engineering (ICOM) 2019, 30th–31st October 2019, Putrajaya. https://ieeexplore.ieee.org/document/8952042 10.1109/ICOM47790.2019.8952042 |
| spellingShingle | T Technology (General) Mohd Khairuddin, Ismail Sidek, Shahrul Na'im Abdul Majeed, Anwar P.P. Ahmad Puzi, Asmarani Classifying motion intention from EMG signal: A kNN approach |
| title | Classifying motion intention from EMG signal: A kNN approach |
| title_full | Classifying motion intention from EMG signal: A kNN approach |
| title_fullStr | Classifying motion intention from EMG signal: A kNN approach |
| title_full_unstemmed | Classifying motion intention from EMG signal: A kNN approach |
| title_short | Classifying motion intention from EMG signal: A kNN approach |
| title_sort | classifying motion intention from emg signal: a knn approach |
| topic | T Technology (General) |
| url | http://irep.iium.edu.my/78714/ http://irep.iium.edu.my/78714/ http://irep.iium.edu.my/78714/ 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 |