• Türkçe
    • English
  • English 
    • Türkçe
    • English
  • Login
View Item 
  •   DSpace Home
  • Araştırma Çıktıları | TR-Dizin | WoS | Scopus | PubMed
  • Scopus İndeksli Yayınlar Koleksiyonu
  • View Item
  •   DSpace Home
  • Araştırma Çıktıları | TR-Dizin | WoS | Scopus | PubMed
  • Scopus İndeksli Yayınlar Koleksiyonu
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Efficient Hand Movement Detection Using k-Means Clustering and k-Nearest Neighbor Algorithms

xmlui.dri2xhtml.METS-1.0.item-rights

info:eu-repo/semantics/closedAccess

Date

2021

Author

Bergil E.
Oral C.
Ergul E.U.

Metadata

Show full item record

Abstract

Purpose: Electromyography (EMG) signals are commonly used in prosthetic limb studies. We have proposed a system to detect six basic hand movements using unsupervised and supervised classification algorithms. In this study, two-channel EMG recordings belonging to six different hand movements are analyzed and the performance of the wavelet-based features for hand movement clustering and classification are examined for six subjects (three females and three males). Methods: The approximation and detail components are obtained by four-level symmetric wavelet transform. The energy, mean, standard deviation, and entropy values of the wavelet components are calculated and the feature sets are generated. After feature extraction, feature set dimensionality is reduced using principal component analysis, and then the k-nearest neighbor method and k-means clustering are applied for classification and clustering, respectively. The analyses are performed subject-specifically and gender-specifically. Thus, it is possible to evaluate the gender effect on classification performances. Results: Subject-specific hand movements were detected with accuracy in the range of 86.33–100%. Gender-specific hand movements were detected with an accuracy of 96.67% for males and 92.78% for females. Conclusions: The classification and clustering results support each other. It was observed that the samples of hand movements that were classified incorrectly were concentrated in the same clusters. Similarly, it was found that the hand movements that were easily detected were homogeneously clustered. © 2020, Taiwanese Society of Biomedical Engineering.

Volume

41

Issue

1

URI

https://doi.org/10.1007/s40846-020-00537-4
https://hdl.handle.net/20.500.12450/2858

Collections

  • Scopus İndeksli Yayınlar Koleksiyonu [1574]
  • WoS İndeksli Yayınlar Koleksiyonu [2182]

Related items

Showing items related by title, author, creator and subject.

  • Classification of vertebral column disorders and lumbar discs disease using attribute weighting algorithm with mean shift clustering 

    Unal, Yavuz; Polat, Kemal; Kocer, H. Erdinc (ELSEVIER SCI LTD, 2016)
    In this article, a new data pre-processing method has been suggested to detect and classify vertebral column disorders and lumbar disc diseases with a high accuracy level. The suggested pre-processing method is called the ...
  • Pairwise FCM based feature weighting for improved classification of vertebral column disorders 

    Unal, Yavuz; Polat, Kemal; Kocer, H. Erdinc (PERGAMON-ELSEVIER SCIENCE LTD, 2014)
    In this paper, an innovative data pre-processing method to improve the classification performance and to determine automatically the vertebral column disorders including disk hernia (DH), spondylolisthesis (SL) and normal ...
  • Performance analysis of rule based automatic SNN algorithm on big data sets [Kural tabanli otomatik SNN algoritmasinin büyük veri setleri üzerindeki performans incelemesi] 

    Cavus A.; Karabina A.; Kilic E. (Institute of Electrical and Electronics Engineers Inc., 2018)
    Clustering is defined as the classification of patterns into groups (clusters) without supervision. The clustering of similarities of data is a complex process that can not be done with human hands. There are various ...



DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
Theme by 
@mire NV
 

 




| Instruction | Guide | Contact |

DSpace@Amasya

by OpenAIRE
Advanced Search

sherpa/romeo

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsTypeDepartmentPublisherCategoryLanguageAccess TypeThis CollectionBy Issue DateAuthorsTitlesSubjectsTypeDepartmentPublisherCategoryLanguageAccess Type

My Account

LoginRegister

DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
Theme by 
@mire NV
 

 


|| Instruction || Guide || Library || Amasya University || OAI-PMH ||

Amasya Üniversitesi Kütüphane ve Dokümantasyon Daire Başkanlığı, Amasya, Turkey
If you find any errors in content, please contact: openaccess@amasya.edu.tr

Creative Commons License
DSpace@Amasya by Amasya University Institutional Repository is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License..

DSpace@Amasya: