Identification of phantom movements with an ensemble learning approach
<p dir="ltr">Phantom limb pain after amputation is a debilitating condition that negatively affects activities of daily life and the quality of life of amputees. Most amputees are able to control the movement of the missing limb, which is called the phantom limb movement. Recognition...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , , |
| Published: |
2022
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513539459252224 |
|---|---|
| author | Akhan Akbulut (17380285) |
| author2 | Feray Gungor (17380288) Ela Tarakci (10769812) Muhammed Ali Aydin (17380291) Abdul Halim Zaim (17380294) Cagatay Catal (6897842) |
| author2_role | author author author author author |
| author_facet | Akhan Akbulut (17380285) Feray Gungor (17380288) Ela Tarakci (10769812) Muhammed Ali Aydin (17380291) Abdul Halim Zaim (17380294) Cagatay Catal (6897842) |
| author_role | author |
| dc.creator.none.fl_str_mv | Akhan Akbulut (17380285) Feray Gungor (17380288) Ela Tarakci (10769812) Muhammed Ali Aydin (17380291) Abdul Halim Zaim (17380294) Cagatay Catal (6897842) |
| dc.date.none.fl_str_mv | 2022-11-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.compbiomed.2022.106132 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Identification_of_phantom_movements_with_an_ensemble_learning_approach/24551311 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Phantom motor execution Ensemble learning Classification |
| dc.title.none.fl_str_mv | Identification of phantom movements with an ensemble learning approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Phantom limb pain after amputation is a debilitating condition that negatively affects activities of daily life and the quality of life of amputees. Most amputees are able to control the movement of the missing limb, which is called the phantom limb movement. Recognition of these movements is crucial for both technology-based amputee rehabilitation and prosthetic control. The aim of the current study is to classify and recognize the phantom movements in four different amputation levels of the upper and lower extremities. In the current study, we utilized ensemble learning algorithms for the recognition and classification of phantom movements of the different amputation levels of the upper and lower extremity. In this context, sEMG signals obtained from 38 amputees and 25 healthy individuals were collected and the dataset was created. Studies of processing sEMG signals in amputees are rather limited, and studies are generally on the classification of upper extremity and hand movements. Our study demonstrated that the ensemble learning-based models resulted in higher accuracy in the detection of phantom movements. The ensemble learning-based approaches outperformed the SVM, Decision tree, and kNN methods. The accuracy of the movement pattern recognition in healthy people was up to 96.33%, this was at most 79.16% in amputees.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers in Biology and Medicine<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compbiomed.2022.106132" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2022.106132</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_45cda0e23fcc34cd0bd74db4740bc646 |
| identifier_str_mv | 10.1016/j.compbiomed.2022.106132 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24551311 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Identification of phantom movements with an ensemble learning approachAkhan Akbulut (17380285)Feray Gungor (17380288)Ela Tarakci (10769812)Muhammed Ali Aydin (17380291)Abdul Halim Zaim (17380294)Cagatay Catal (6897842)EngineeringBiomedical engineeringPhantom motor executionEnsemble learningClassification<p dir="ltr">Phantom limb pain after amputation is a debilitating condition that negatively affects activities of daily life and the quality of life of amputees. Most amputees are able to control the movement of the missing limb, which is called the phantom limb movement. Recognition of these movements is crucial for both technology-based amputee rehabilitation and prosthetic control. The aim of the current study is to classify and recognize the phantom movements in four different amputation levels of the upper and lower extremities. In the current study, we utilized ensemble learning algorithms for the recognition and classification of phantom movements of the different amputation levels of the upper and lower extremity. In this context, sEMG signals obtained from 38 amputees and 25 healthy individuals were collected and the dataset was created. Studies of processing sEMG signals in amputees are rather limited, and studies are generally on the classification of upper extremity and hand movements. Our study demonstrated that the ensemble learning-based models resulted in higher accuracy in the detection of phantom movements. The ensemble learning-based approaches outperformed the SVM, Decision tree, and kNN methods. The accuracy of the movement pattern recognition in healthy people was up to 96.33%, this was at most 79.16% in amputees.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers in Biology and Medicine<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compbiomed.2022.106132" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2022.106132</a></p>2022-11-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.compbiomed.2022.106132https://figshare.com/articles/journal_contribution/Identification_of_phantom_movements_with_an_ensemble_learning_approach/24551311CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/245513112022-11-01T00:00:00Z |
| spellingShingle | Identification of phantom movements with an ensemble learning approach Akhan Akbulut (17380285) Engineering Biomedical engineering Phantom motor execution Ensemble learning Classification |
| status_str | publishedVersion |
| title | Identification of phantom movements with an ensemble learning approach |
| title_full | Identification of phantom movements with an ensemble learning approach |
| title_fullStr | Identification of phantom movements with an ensemble learning approach |
| title_full_unstemmed | Identification of phantom movements with an ensemble learning approach |
| title_short | Identification of phantom movements with an ensemble learning approach |
| title_sort | Identification of phantom movements with an ensemble learning approach |
| topic | Engineering Biomedical engineering Phantom motor execution Ensemble learning Classification |