Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning
This research focuses on the minimum process of classifying three upper arm movements (elbow extension, shoulder extension, combined shoulder and elbow extension) of humans with three electromyography (EMG) signals, to control a 2-degrees of freedom (DoF) robotic arm. The proposed minimum process co...
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MDPI AG
2021-03-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/9/3/56 |
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| author | Pringgo Widyo Laksono Takahide Kitamura Joseph Muguro Kojiro Matsushita Minoru Sasaki Muhammad Syaiful Amri bin Suhaimi |
| author_facet | Pringgo Widyo Laksono Takahide Kitamura Joseph Muguro Kojiro Matsushita Minoru Sasaki Muhammad Syaiful Amri bin Suhaimi |
| author_sort | Pringgo Widyo Laksono |
| collection | DOAJ |
| description | This research focuses on the minimum process of classifying three upper arm movements (elbow extension, shoulder extension, combined shoulder and elbow extension) of humans with three electromyography (EMG) signals, to control a 2-degrees of freedom (DoF) robotic arm. The proposed minimum process consists of four parts: time divisions of data, Teager–Kaiser energy operator (TKEO), the conventional EMG feature extraction (i.e., the mean absolute value (MAV), zero crossings (ZC), slope-sign changes (SSC), and waveform length (WL)), and eight major machine learning models (i.e., decision tree (medium), decision tree (fine), k-Nearest Neighbor (KNN) (weighted KNN, KNN (fine), Support Vector Machine (SVM) (cubic and fine Gaussian SVM), Ensemble (bagged trees and subspace KNN). Then, we compare and investigate 48 classification models (i.e., 47 models are proposed, and 1 model is the conventional) based on five healthy subjects. The results showed that all the classification models achieved accuracies ranging between 74–98%, and the processing speed is below 40 ms and indicated acceptable controller delay for robotic arm control. Moreover, we confirmed that the classification model with no time division, with TKEO, and with ensemble (subspace KNN) had the best performance in accuracy rates at 96.67, recall rates at 99.66, and precision rates at 96.99. In short, the combination of the proposed TKEO and ensemble (subspace KNN) plays an important role to achieve the EMG classification. |
| first_indexed | 2024-03-09T05:25:37Z |
| format | Article |
| id | doaj.art-c78fdba2201f439287cd2f29ca288c0c |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T05:25:37Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-c78fdba2201f439287cd2f29ca288c0c2023-12-03T12:37:41ZengMDPI AGMachines2075-17022021-03-01935610.3390/machines9030056Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine LearningPringgo Widyo Laksono0Takahide Kitamura1Joseph Muguro2Kojiro Matsushita3Minoru Sasaki4Muhammad Syaiful Amri bin Suhaimi5School of Engineering, Gifu University, Gifu 501-1193, JapanSchool of Engineering, Gifu University, Gifu 501-1193, JapanSchool of Engineering, Gifu University, Gifu 501-1193, JapanSchool of Engineering, Gifu University, Gifu 501-1193, JapanSchool of Engineering, Gifu University, Gifu 501-1193, JapanNational Institute of Technology, Gifu College, Gifu 501-0495, JapanThis research focuses on the minimum process of classifying three upper arm movements (elbow extension, shoulder extension, combined shoulder and elbow extension) of humans with three electromyography (EMG) signals, to control a 2-degrees of freedom (DoF) robotic arm. The proposed minimum process consists of four parts: time divisions of data, Teager–Kaiser energy operator (TKEO), the conventional EMG feature extraction (i.e., the mean absolute value (MAV), zero crossings (ZC), slope-sign changes (SSC), and waveform length (WL)), and eight major machine learning models (i.e., decision tree (medium), decision tree (fine), k-Nearest Neighbor (KNN) (weighted KNN, KNN (fine), Support Vector Machine (SVM) (cubic and fine Gaussian SVM), Ensemble (bagged trees and subspace KNN). Then, we compare and investigate 48 classification models (i.e., 47 models are proposed, and 1 model is the conventional) based on five healthy subjects. The results showed that all the classification models achieved accuracies ranging between 74–98%, and the processing speed is below 40 ms and indicated acceptable controller delay for robotic arm control. Moreover, we confirmed that the classification model with no time division, with TKEO, and with ensemble (subspace KNN) had the best performance in accuracy rates at 96.67, recall rates at 99.66, and precision rates at 96.99. In short, the combination of the proposed TKEO and ensemble (subspace KNN) plays an important role to achieve the EMG classification.https://www.mdpi.com/2075-1702/9/3/56electromyography (EMG)upper-limb motionmachine-learningrobot arm control |
| spellingShingle | Pringgo Widyo Laksono Takahide Kitamura Joseph Muguro Kojiro Matsushita Minoru Sasaki Muhammad Syaiful Amri bin Suhaimi Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning Machines electromyography (EMG) upper-limb motion machine-learning robot arm control |
| title | Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning |
| title_full | Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning |
| title_fullStr | Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning |
| title_full_unstemmed | Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning |
| title_short | Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning |
| title_sort | minimum mapping from emg signals at human elbow and shoulder movements into two dof upper limb robot with machine learning |
| topic | electromyography (EMG) upper-limb motion machine-learning robot arm control |
| url | https://www.mdpi.com/2075-1702/9/3/56 |
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