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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...

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Main Authors: Mohamad Ilyas, Rizan, Muhammad Nur Aiman, Shapiee, Muhammad Amirul, Abdullah, Mohd Azraai, Mohd Razman, Anwar P. P., Abdul Majeed
Format: Article
Language:English
Published: Penerbit UMP 2020
Subjects:
RD Surgery
T Technology (General)
Online Access:http://umpir.ump.edu.my/id/eprint/33642/1/The%20classification%20of%20elbow%20extension%20and%20flexion.pdf
http://umpir.ump.edu.my/id/eprint/33642/
https://doi.org/10.15282/mekatronika.v2i2.7017
https://doi.org/10.15282/mekatronika.v2i2.7017
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spelling my.ump.umpir.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 https://doi.org/10.15282/mekatronika.v2i2.7017 https://doi.org/10.15282/mekatronika.v2i2.7017
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic RD Surgery
T Technology (General)
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
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
format Article
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
title 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_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_sort classification of elbow extension and flexion: a feature selection investigation
publisher Penerbit UMP
publishDate 2020
url http://umpir.ump.edu.my/id/eprint/33642/1/The%20classification%20of%20elbow%20extension%20and%20flexion.pdf
http://umpir.ump.edu.my/id/eprint/33642/
https://doi.org/10.15282/mekatronika.v2i2.7017
https://doi.org/10.15282/mekatronika.v2i2.7017
_version_ 1729703441016553472
score 13.211869

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