The classification of movement intention through machine learning models: the identification of significant time-domain EMG features
Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classificatio...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Peerj Inc.
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/32419/ http://umpir.ump.edu.my/id/eprint/32419/1/The%20classification%20of%20movement%20intention%20through%20machine%20learning%20models.pdf |
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| author | Ismail, Mohd Khairuddin Shahrul Naim, Sidek Anwar, P. P. Abdul Majeed Mohd Azraai, Mohd Razman Asmarani, Ahmad Puzi Hazlina, Md Yusof |
| author_facet | Ismail, Mohd Khairuddin Shahrul Naim, Sidek Anwar, P. P. Abdul Majeed Mohd Azraai, Mohd Razman Asmarani, Ahmad Puzi Hazlina, Md Yusof |
| author_sort | Ismail, Mohd Khairuddin |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject’s intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects’ biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them. |
| first_indexed | 2025-11-15T03:06:17Z |
| format | Article |
| id | ump-32419 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:06:17Z |
| publishDate | 2021 |
| publisher | Peerj Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-324192021-11-05T03:21:52Z http://umpir.ump.edu.my/id/eprint/32419/ The classification of movement intention through machine learning models: the identification of significant time-domain EMG features Ismail, Mohd Khairuddin Shahrul Naim, Sidek Anwar, P. P. Abdul Majeed Mohd Azraai, Mohd Razman Asmarani, Ahmad Puzi Hazlina, Md Yusof TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject’s intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects’ biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them. Peerj Inc. 2021-02-25 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/32419/1/The%20classification%20of%20movement%20intention%20through%20machine%20learning%20models.pdf Ismail, Mohd Khairuddin and Shahrul Naim, Sidek and Anwar, P. P. Abdul Majeed and Mohd Azraai, Mohd Razman and Asmarani, Ahmad Puzi and Hazlina, Md Yusof (2021) The classification of movement intention through machine learning models: the identification of significant time-domain EMG features. PeerJ Computer Science, 7. pp. 1-15. ISSN 2376-5992. (Published) https://doi.org/10.7717/peerj-cs.379 https://doi.org/10.7717/peerj-cs.379 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery Ismail, Mohd Khairuddin Shahrul Naim, Sidek Anwar, P. P. Abdul Majeed Mohd Azraai, Mohd Razman Asmarani, Ahmad Puzi Hazlina, Md Yusof The classification of movement intention through machine learning models: the identification of significant time-domain EMG features |
| title | The classification of movement intention through machine learning models: the identification of significant time-domain EMG features |
| title_full | The classification of movement intention through machine learning models: the identification of significant time-domain EMG features |
| title_fullStr | The classification of movement intention through machine learning models: the identification of significant time-domain EMG features |
| title_full_unstemmed | The classification of movement intention through machine learning models: the identification of significant time-domain EMG features |
| title_short | The classification of movement intention through machine learning models: the identification of significant time-domain EMG features |
| title_sort | classification of movement intention through machine learning models: the identification of significant time-domain emg features |
| topic | TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/32419/ http://umpir.ump.edu.my/id/eprint/32419/ http://umpir.ump.edu.my/id/eprint/32419/ http://umpir.ump.edu.my/id/eprint/32419/1/The%20classification%20of%20movement%20intention%20through%20machine%20learning%20models.pdf |