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Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach

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Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach. / Jia, Guangyu; Lam, Hak Keung; Ma, Shichao; Yang, Zhaohui; Xu, Yujia; Xiao, Bo.

In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 28, No. 6, 9062601, 06.2020, p. 1428-1435.

Research output: Contribution to journalArticle

Harvard

Jia, G, Lam, HK, Ma, S, Yang, Z, Xu, Y & Xiao, B 2020, 'Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 6, 9062601, pp. 1428-1435. https://doi.org/10.1109/TNSRE.2020.2986884

APA

Jia, G., Lam, H. K., Ma, S., Yang, Z., Xu, Y., & Xiao, B. (2020). Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(6), 1428-1435. [9062601]. https://doi.org/10.1109/TNSRE.2020.2986884

Vancouver

Jia G, Lam HK, Ma S, Yang Z, Xu Y, Xiao B. Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020 Jun;28(6):1428-1435. 9062601. https://doi.org/10.1109/TNSRE.2020.2986884

Author

Jia, Guangyu ; Lam, Hak Keung ; Ma, Shichao ; Yang, Zhaohui ; Xu, Yujia ; Xiao, Bo. / Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach. In: IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020 ; Vol. 28, No. 6. pp. 1428-1435.

Bibtex Download

@article{64cd955baea6427e9c8b279cc247a121,
title = "Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach",
abstract = "Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification faces the challenges of unsatisfied classification accuracy, insufficient generalization ability, lack of training data and weak robustness. To address these problems, this paper combines unsupervised and supervised learning methods to classify an EMG dataset consisting of 10 classes of hand gestures. To lessen the difficulty of classification, clustering methods including subtractive clustering and fuzzy c-means (FCM) clustering algorithms are employed first to obtain the initial partition of the inputs. In particular, modified FCM algorithm is proposed to accustom the conventional FCM to the multi-class classification problem. Based on the grouping information obtained from clustering, a type of two-step supervised learning approach is proposed. Specifically, a top-classifier and three sub-classifiers integrated with windowing method and majority voting are employed to accomplish the two-step classification. The results demonstrate that the proposed method achieves 100% test accuracy and the strongest robustness compared to the conventional machine learning approaches, which shows the potential for industrial and healthcare applications, such as movement intention detection, grasp recognition and dexterous prostheses control.",
keywords = "clustering, deep learning, EMG signals, fuzzy c-means (FCM), hand gesture classification, machine learning",
author = "Guangyu Jia and Lam, {Hak Keung} and Shichao Ma and Zhaohui Yang and Yujia Xu and Bo Xiao",
year = "2020",
month = jun,
doi = "10.1109/TNSRE.2020.2986884",
language = "English",
volume = "28",
pages = "1428--1435",
journal = "IEEE transactions on neural systems and rehabilitation engineering ",
issn = "1534-4320",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "6",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach

AU - Jia, Guangyu

AU - Lam, Hak Keung

AU - Ma, Shichao

AU - Yang, Zhaohui

AU - Xu, Yujia

AU - Xiao, Bo

PY - 2020/6

Y1 - 2020/6

N2 - Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification faces the challenges of unsatisfied classification accuracy, insufficient generalization ability, lack of training data and weak robustness. To address these problems, this paper combines unsupervised and supervised learning methods to classify an EMG dataset consisting of 10 classes of hand gestures. To lessen the difficulty of classification, clustering methods including subtractive clustering and fuzzy c-means (FCM) clustering algorithms are employed first to obtain the initial partition of the inputs. In particular, modified FCM algorithm is proposed to accustom the conventional FCM to the multi-class classification problem. Based on the grouping information obtained from clustering, a type of two-step supervised learning approach is proposed. Specifically, a top-classifier and three sub-classifiers integrated with windowing method and majority voting are employed to accomplish the two-step classification. The results demonstrate that the proposed method achieves 100% test accuracy and the strongest robustness compared to the conventional machine learning approaches, which shows the potential for industrial and healthcare applications, such as movement intention detection, grasp recognition and dexterous prostheses control.

AB - Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification faces the challenges of unsatisfied classification accuracy, insufficient generalization ability, lack of training data and weak robustness. To address these problems, this paper combines unsupervised and supervised learning methods to classify an EMG dataset consisting of 10 classes of hand gestures. To lessen the difficulty of classification, clustering methods including subtractive clustering and fuzzy c-means (FCM) clustering algorithms are employed first to obtain the initial partition of the inputs. In particular, modified FCM algorithm is proposed to accustom the conventional FCM to the multi-class classification problem. Based on the grouping information obtained from clustering, a type of two-step supervised learning approach is proposed. Specifically, a top-classifier and three sub-classifiers integrated with windowing method and majority voting are employed to accomplish the two-step classification. The results demonstrate that the proposed method achieves 100% test accuracy and the strongest robustness compared to the conventional machine learning approaches, which shows the potential for industrial and healthcare applications, such as movement intention detection, grasp recognition and dexterous prostheses control.

KW - clustering

KW - deep learning

KW - EMG signals

KW - fuzzy c-means (FCM)

KW - hand gesture classification

KW - machine learning

UR - http://www.scopus.com/inward/record.url?scp=85086052635&partnerID=8YFLogxK

U2 - 10.1109/TNSRE.2020.2986884

DO - 10.1109/TNSRE.2020.2986884

M3 - Article

C2 - 32286995

AN - SCOPUS:85086052635

VL - 28

SP - 1428

EP - 1435

JO - IEEE transactions on neural systems and rehabilitation engineering

JF - IEEE transactions on neural systems and rehabilitation engineering

SN - 1534-4320

IS - 6

M1 - 9062601

ER -

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