The classification of elbow extension and flexion: A feature selection investigation
Nowadays, the worldwide primary reasons of long-term disability is stroke. When the blood supply to your brain is interupted and reduced, stroke occurs as it depriving brain tissue of nutrients and oxygen. In the modern world, advanced technology are revolutionizing the rehabilitation process. This...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33642/ http://umpir.ump.edu.my/id/eprint/33642/1/The%20classification%20of%20elbow%20extension%20and%20flexion.pdf |
| _version_ | 1848824307061358592 |
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| author | Mohamad Ilyas, Rizan Muhammad Nur Aiman, Shapiee Muhammad Amirul, Abdullah Mohd Azraai, Mohd Razman Anwar P. P., Abdul Majeed |
| author_facet | Mohamad Ilyas, Rizan Muhammad Nur Aiman, Shapiee Muhammad Amirul, Abdullah Mohd Azraai, Mohd Razman Anwar P. P., Abdul Majeed |
| author_sort | Mohamad Ilyas, Rizan |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Nowadays, the worldwide primary reasons of long-term disability is stroke. When the blood supply to your brain is interupted and reduced, stroke occurs as it depriving brain tissue of nutrients and oxygen. In the modern world, advanced technology are revolutionizing the rehabilitation process. This research uses mechanomyography (MMG) and machine learning models to classify the elbow movement, extension and flexion of the elbow joint. The study will aid in the control of an exoskeleton for stroke patient's rehabilitation process in future studies. Five volunteers (21 to 23 years old) were recruited in Universiti Malaysia Pahang (UMP) to execute the right elbow movement of extension and flexion. The movements are repeated five times each for two active muscles for the extension and flexion motion, namely triceps and biceps. From the time domain based MMG signals, twenty-four features were extracted from the MMG before being classified by the machine learning model, namely k-Nearest Neighbors (k-NN). The k-NN has achieved the classification accuracy (CA) with 88.6% as the significant features are identified through the information gain approach. It may well be stated that the suggested process was able to classify the elbow movement well |
| first_indexed | 2025-11-15T03:10:57Z |
| format | Article |
| id | ump-33642 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:10:57Z |
| publishDate | 2020 |
| publisher | Penerbit UMP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-336422022-04-07T02:15:27Z http://umpir.ump.edu.my/id/eprint/33642/ The classification of elbow extension and flexion: A feature selection investigation Mohamad Ilyas, Rizan Muhammad Nur Aiman, Shapiee Muhammad Amirul, Abdullah Mohd Azraai, Mohd Razman Anwar P. P., Abdul Majeed RD Surgery T Technology (General) Nowadays, the worldwide primary reasons of long-term disability is stroke. When the blood supply to your brain is interupted and reduced, stroke occurs as it depriving brain tissue of nutrients and oxygen. In the modern world, advanced technology are revolutionizing the rehabilitation process. This research uses mechanomyography (MMG) and machine learning models to classify the elbow movement, extension and flexion of the elbow joint. The study will aid in the control of an exoskeleton for stroke patient's rehabilitation process in future studies. Five volunteers (21 to 23 years old) were recruited in Universiti Malaysia Pahang (UMP) to execute the right elbow movement of extension and flexion. The movements are repeated five times each for two active muscles for the extension and flexion motion, namely triceps and biceps. From the time domain based MMG signals, twenty-four features were extracted from the MMG before being classified by the machine learning model, namely k-Nearest Neighbors (k-NN). The k-NN has achieved the classification accuracy (CA) with 88.6% as the significant features are identified through the information gain approach. It may well be stated that the suggested process was able to classify the elbow movement well Penerbit UMP 2020 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/33642/1/The%20classification%20of%20elbow%20extension%20and%20flexion.pdf Mohamad Ilyas, Rizan and Muhammad Nur Aiman, Shapiee and Muhammad Amirul, Abdullah and Mohd Azraai, Mohd Razman and Anwar P. P., Abdul Majeed (2020) The classification of elbow extension and flexion: A feature selection investigation. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 2 (2). pp. 68-73. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v2i2.7017 https://doi.org/10.15282/mekatronika.v2i2.7017 |
| spellingShingle | RD Surgery T Technology (General) Mohamad Ilyas, Rizan Muhammad Nur Aiman, Shapiee Muhammad Amirul, Abdullah Mohd Azraai, Mohd Razman Anwar P. P., Abdul Majeed The classification of elbow extension and flexion: A feature selection investigation |
| title | The classification of elbow extension and flexion: A feature selection investigation |
| title_full | The classification of elbow extension and flexion: A feature selection investigation |
| title_fullStr | The classification of elbow extension and flexion: A feature selection investigation |
| title_full_unstemmed | The classification of elbow extension and flexion: A feature selection investigation |
| title_short | The classification of elbow extension and flexion: A feature selection investigation |
| title_sort | classification of elbow extension and flexion: a feature selection investigation |
| topic | RD Surgery T Technology (General) |
| url | http://umpir.ump.edu.my/id/eprint/33642/ http://umpir.ump.edu.my/id/eprint/33642/ http://umpir.ump.edu.my/id/eprint/33642/ http://umpir.ump.edu.my/id/eprint/33642/1/The%20classification%20of%20elbow%20extension%20and%20flexion.pdf |