Classification of Hand Movements Using MYO Armband on an Embedded Platform
The study proposed the classification and recognition of hand gestures using electromyography (EMG) signals for controlling the upper limb prosthesis. In this research, the EMG signals were measured through an embedded system by wearing a band of MYO gesture control. In order to observe the behavior...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/11/1322 |
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| author | Haider Ali Javaid Mohsin Islam Tiwana Ahmed Alsanad Javaid Iqbal Muhammad Tanveer Riaz Saeed Ahmad Faisal Abdulaziz Almisned |
| author_facet | Haider Ali Javaid Mohsin Islam Tiwana Ahmed Alsanad Javaid Iqbal Muhammad Tanveer Riaz Saeed Ahmad Faisal Abdulaziz Almisned |
| author_sort | Haider Ali Javaid |
| collection | DOAJ |
| description | The study proposed the classification and recognition of hand gestures using electromyography (EMG) signals for controlling the upper limb prosthesis. In this research, the EMG signals were measured through an embedded system by wearing a band of MYO gesture control. In order to observe the behavior of these change movements, the EMG data was acquired from 10 healthy subjects (five male and five females) performing four upper limb movements. After extracting EMG data from MYO, the supervised classification approach was applied to recognize the different hand movements. The classification was performed with a 5-fold cross-validation technique under the supervision of Quadratic discriminant analysis (QDA), support vector machine (SVM), random forest, gradient boosted, ensemble (bagged tree), and ensemble (subspace K-Nearest Neighbors) classifier. The execution of these classifiers shows the overall accuracy of 83.9% in the case of ensemble (bagged tree) which is higher than other classifiers. Additionally, in this research an embedded system-based classification approach of hand movement was used for designing an upper limb prosthesis. This approach is different than previous techniques as MYO is used with an external Bluetooth module and different libraries that make its movement and performance boundless. The results of this study also inferred the operations which were easy for hand recognition and can be used for developing a powerful, efficient, and flexible prosthetic design in the future. |
| first_indexed | 2024-03-10T10:49:53Z |
| format | Article |
| id | doaj.art-87d88c317782436fb55d3109d6f395d1 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T10:49:53Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-87d88c317782436fb55d3109d6f395d12023-11-21T22:17:11ZengMDPI AGElectronics2079-92922021-05-011011132210.3390/electronics10111322Classification of Hand Movements Using MYO Armband on an Embedded PlatformHaider Ali Javaid0Mohsin Islam Tiwana1Ahmed Alsanad2Javaid Iqbal3Muhammad Tanveer Riaz4Saeed Ahmad5Faisal Abdulaziz Almisned6Department of Mechatronics Engineering, College of Electrical and Mechanical Engineering, National University of Sciences & Technology, H-12, Islamabad 44000, PakistanDepartment of Mechatronics Engineering, College of Electrical and Mechanical Engineering, National University of Sciences & Technology, H-12, Islamabad 44000, PakistanSTC’s Artificial Intelligence Chair, Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Mechatronics Engineering, College of Electrical and Mechanical Engineering, National University of Sciences & Technology, H-12, Islamabad 44000, PakistanDepartment of Mechanical, Mechatronics and Manufacturing Engineering, Faisalabad Campus, University of Engineering & Technology (UET) Lahore, Faisalabad 38000, PakistanDepartment of Mechanical Engineering, College of Engineering and Technology, University of Sargodha, Sargodha 40100, PakistanDepartment of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaThe study proposed the classification and recognition of hand gestures using electromyography (EMG) signals for controlling the upper limb prosthesis. In this research, the EMG signals were measured through an embedded system by wearing a band of MYO gesture control. In order to observe the behavior of these change movements, the EMG data was acquired from 10 healthy subjects (five male and five females) performing four upper limb movements. After extracting EMG data from MYO, the supervised classification approach was applied to recognize the different hand movements. The classification was performed with a 5-fold cross-validation technique under the supervision of Quadratic discriminant analysis (QDA), support vector machine (SVM), random forest, gradient boosted, ensemble (bagged tree), and ensemble (subspace K-Nearest Neighbors) classifier. The execution of these classifiers shows the overall accuracy of 83.9% in the case of ensemble (bagged tree) which is higher than other classifiers. Additionally, in this research an embedded system-based classification approach of hand movement was used for designing an upper limb prosthesis. This approach is different than previous techniques as MYO is used with an external Bluetooth module and different libraries that make its movement and performance boundless. The results of this study also inferred the operations which were easy for hand recognition and can be used for developing a powerful, efficient, and flexible prosthetic design in the future.https://www.mdpi.com/2079-9292/10/11/1322electromyography (EMG)MYO gesture controlprosthesisensemble classifier |
| spellingShingle | Haider Ali Javaid Mohsin Islam Tiwana Ahmed Alsanad Javaid Iqbal Muhammad Tanveer Riaz Saeed Ahmad Faisal Abdulaziz Almisned Classification of Hand Movements Using MYO Armband on an Embedded Platform Electronics electromyography (EMG) MYO gesture control prosthesis ensemble classifier |
| title | Classification of Hand Movements Using MYO Armband on an Embedded Platform |
| title_full | Classification of Hand Movements Using MYO Armband on an Embedded Platform |
| title_fullStr | Classification of Hand Movements Using MYO Armband on an Embedded Platform |
| title_full_unstemmed | Classification of Hand Movements Using MYO Armband on an Embedded Platform |
| title_short | Classification of Hand Movements Using MYO Armband on an Embedded Platform |
| title_sort | classification of hand movements using myo armband on an embedded platform |
| topic | electromyography (EMG) MYO gesture control prosthesis ensemble classifier |
| url | https://www.mdpi.com/2079-9292/10/11/1322 |
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