Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2020, Cilt: 3 Sayı: 3, 325 - 333, 30.12.2020

Muzaffer Aslan

https://doi.org/10.35377/saucis.03.03.811480
Cited By: 1

Öz

Kaynakça

  • J. J. A. Mendes Junior, M. L. B. Freitas, H. V. Siqueira, A. E. Lazzaretti, S. F. Pichorim, and S. L. Stevan, “Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach,” Biomed. Signal Process. Control, vol. 59, 2020, doi: 10.1016/j.bspc.2020.101920.
  • Babita, P. Kumari, Y. Narayan, and L. Mathew, “Binary movement classification of sEMG signal using linear SVM and Wavelet Packet Transform,” 1st IEEE Int. Conf. Power Electron. Intell. Control Energy Syst. ICPEICES 2016, pp. 30–33, 2017, doi: 10.1109/ICPEICES.2016.7853640.
  • C. Sravani, V. Bajaj, S. Taran, and A. Sengur, “Flexible Analytic Wavelet Transform Based Features for Physical Action Identification Using sEMG Signals,” IRBM, 2020, doi: 10.1016/j.irbm.2019.07.002.
  • A. Arı, F. AYAZ, and D. HANBAY, “EMG Sinyallerinin Kısa Zamanlı Fourier Dönüşüm Özellikleri Kullanılarak Yapay Sinir Ağları ile Sınıflandırılması,” Fırat Üniversitesi Mühendislik Bilim. Derg., pp. 443–451, Sep. 2019, doi: 10.35234/fumbd.545161.
  • C. Sapsanis, G. Georgoulas, A. Tzes, and D. Lymberopoulos, “Improving EMG based classification of basic hand movements using EMD,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 5754–5757, 2013, doi: 10.1109/EMBC.2013.6610858.
  • J. Qi, G. Jiang, G. Li, Y. Sun, and B. Tao, “Intelligent Human-Computer Interaction Based on Surface EMG Gesture Recognition,” IEEE Access, vol. 7, pp. 61378–61387, 2019, doi: 10.1109/ACCESS.2019.2914728.
  • N. A. Chaya, B. R. Bhavana, S. B. Anoogna, M. Hiranmai, and N. B. Krupa, “Real-Time Replication of Arm Movements Using Surface EMG Signals,” Procedia Comput. Sci., vol. 154, pp. 186–193, 2018, doi: 10.1016/j.procs.2019.06.028.
  • A. Arı, B. Arı, and Ö. F. Alçin, “Elektromiyografi Sinyallerinin Permütasyon Entropi ve Bir Boyutlu Yerel İkili Özellikler Kullanılarak Sınıflandırılması,” J. Tepecik Educ. Res. Hosp., vol. 30, no. 1, pp. 46–49, 2020, [Online]. Available: https://www.journalagent.com/terh/pdfs/TERH_30_1_1_82.pdf.
  • T. Tuncer, S. Dogan, and A. Subasi, “Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition,” Biomed. Signal Process. Control, vol. 58, p. 101872, 2020, doi: 10.1016/j.bspc.2020.101872.
  • A. C. Turlapaty and B. Gokaraju, “Feature Analysis for Classification of Physical Actions Using Surface EMG Data,” IEEE Sens. J., vol. 19, no. 24, pp. 12196–12204, 2019, doi: 10.1109/JSEN.2019.2937979.
  • Ö. F. Alçin, “Fraktal Eğimden Arındırılmış Dalgalılık Analizi ve Pencereli Kare Ortalamanın Karekökü Tabanlı EMG Sınıflandırma,” Fırat Üniversitesi Mühendislik Bilim. Derg., vol. 32, no. 2, pp. 359–368, 2020, doi: 10.35234/fumbd.771205.
  • M. Rostaghi and H. Azami, “Dispersion Entropy: A Measure for Time-Series Analysis,” IEEE Signal Process. Lett., vol. 23, no. 5, pp. 610–614, 2016, doi: 10.1109/LSP.2016.2542881.
  • H. Azami et al., “Multiscale fluctuation-based dispersion entropy and its applications to neurological diseases,” IEEE Access, vol. 7, pp. 68718–68733, 2019, doi: 10.1109/ACCESS.2019.2918560.
  • M. Zanin, L. Zunino, O. A. Rosso, and D. Papo, “Permutation entropy and its main biomedical and econophysics applications: A review,” Entropy, vol. 14, no. 8, pp. 1553–1577, 2012, doi: 10.3390/e14081553.
  • H. Azami, L. E. V. da Silva, A. C. M. Omoto, and A. Humeau-Heurtier, “Two-dimensional dispersion entropy: An information-theoretic method for irregularity analysis of images,” Signal Process. Image Commun., vol. 75, no. April, pp. 178–187, 2019, doi: 10.1016/j.image.2019.04.013.
  • E. Kafantaris, I. Piper, T. Y. M. Lo, and J. Escudero, “Augmentation of dispersion entropy for handling missing and outlier samples in physiological signal monitoring,” Entropy, vol. 22, no. 3, 2020, doi: 10.3390/e22030319.
  • M. Aslan, Y. Akbulut, A. Şengür, and M. C. Ince, “Skeleton based efficient fall detection,” J. Fac. Eng. Archit. Gazi Univ., 2017, doi: 10.17341/gazimmfd.369347.
  • F. Demir, M. Turkoglu, M. Aslan, and A. Sengur, “A new pyramidal concatenated CNN approach for environmental sound classification,” Appl. Acoust., 2020, doi: 10.1016/j.apacoust.2020.107520.
  • S. Yu, X. Li, X. Zhang, and H. Wang, “The OCS-SVM: An Objective-Cost-Sensitive SVM With Sample-Based Misclassification Cost Invariance,” IEEE Access, vol. 7, pp. 118931–118942, 2019, doi: 10.1109/access.2019.2933437.
  • X. Wu, W. Zuo, L. Lin, W. Jia, and D. Zhang, “F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation,” IEEE Trans. Neural Networks Learn. Syst., vol. 29, no. 11, pp. 5185–5199, 2018, doi: 10.1109/TNNLS.2018.2791507.
  • O. F. Alcin, A. Sengur, J. Qian, and M. C. Ince, “OMP-ELM: Orthogonal matching pursuit-based extreme learning machine for regression,” J. Intell. Syst., 2015, doi: 10.1515/jisys-2014-0095.
  • G. Bin Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, 2006, doi: 10.1016/j.neucom.2005.12.126.

Normal Cumulative Distribution Function and Dispersion Entropy Based EMG Classification

Yıl 2020, Cilt: 3 Sayı: 3, 325 - 333, 30.12.2020

Muzaffer Aslan

https://doi.org/10.35377/saucis.03.03.811480
Cited By: 1

Öz

Electromyography (EMG) is used to measure muscle activity. EMG signals are widely used in many biomedical practices such as motion recognition, prosthetic control, physical rehabilitation, and human-computer interfaces. The effective use of EMG in such practices depends on distinctive feature extraction. In this study, Dispersion Entropy (DisEn) and Normal Cumulative Distribution Function (NCDF) methods are used for feature extraction from EMG signals. The suggested method was tested with a data set containing immersion of six different objects. In the experimental studies, the proposed method distinguished the movements with an accuracy performance of 98%. When compared to other methods using the same data set, the suggested method has about 1.2% better performance.

Anahtar Kelimeler

EMG Normal Cumulative Distribution Function Dispersion Entropy SVM ELM

Kaynakça

  • J. J. A. Mendes Junior, M. L. B. Freitas, H. V. Siqueira, A. E. Lazzaretti, S. F. Pichorim, and S. L. Stevan, “Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach,” Biomed. Signal Process. Control, vol. 59, 2020, doi: 10.1016/j.bspc.2020.101920.
  • Babita, P. Kumari, Y. Narayan, and L. Mathew, “Binary movement classification of sEMG signal using linear SVM and Wavelet Packet Transform,” 1st IEEE Int. Conf. Power Electron. Intell. Control Energy Syst. ICPEICES 2016, pp. 30–33, 2017, doi: 10.1109/ICPEICES.2016.7853640.
  • C. Sravani, V. Bajaj, S. Taran, and A. Sengur, “Flexible Analytic Wavelet Transform Based Features for Physical Action Identification Using sEMG Signals,” IRBM, 2020, doi: 10.1016/j.irbm.2019.07.002.
  • A. Arı, F. AYAZ, and D. HANBAY, “EMG Sinyallerinin Kısa Zamanlı Fourier Dönüşüm Özellikleri Kullanılarak Yapay Sinir Ağları ile Sınıflandırılması,” Fırat Üniversitesi Mühendislik Bilim. Derg., pp. 443–451, Sep. 2019, doi: 10.35234/fumbd.545161.
  • C. Sapsanis, G. Georgoulas, A. Tzes, and D. Lymberopoulos, “Improving EMG based classification of basic hand movements using EMD,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 5754–5757, 2013, doi: 10.1109/EMBC.2013.6610858.
  • J. Qi, G. Jiang, G. Li, Y. Sun, and B. Tao, “Intelligent Human-Computer Interaction Based on Surface EMG Gesture Recognition,” IEEE Access, vol. 7, pp. 61378–61387, 2019, doi: 10.1109/ACCESS.2019.2914728.
  • N. A. Chaya, B. R. Bhavana, S. B. Anoogna, M. Hiranmai, and N. B. Krupa, “Real-Time Replication of Arm Movements Using Surface EMG Signals,” Procedia Comput. Sci., vol. 154, pp. 186–193, 2018, doi: 10.1016/j.procs.2019.06.028.
  • A. Arı, B. Arı, and Ö. F. Alçin, “Elektromiyografi Sinyallerinin Permütasyon Entropi ve Bir Boyutlu Yerel İkili Özellikler Kullanılarak Sınıflandırılması,” J. Tepecik Educ. Res. Hosp., vol. 30, no. 1, pp. 46–49, 2020, [Online]. Available: https://www.journalagent.com/terh/pdfs/TERH_30_1_1_82.pdf.
  • T. Tuncer, S. Dogan, and A. Subasi, “Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition,” Biomed. Signal Process. Control, vol. 58, p. 101872, 2020, doi: 10.1016/j.bspc.2020.101872.
  • A. C. Turlapaty and B. Gokaraju, “Feature Analysis for Classification of Physical Actions Using Surface EMG Data,” IEEE Sens. J., vol. 19, no. 24, pp. 12196–12204, 2019, doi: 10.1109/JSEN.2019.2937979.
  • Ö. F. Alçin, “Fraktal Eğimden Arındırılmış Dalgalılık Analizi ve Pencereli Kare Ortalamanın Karekökü Tabanlı EMG Sınıflandırma,” Fırat Üniversitesi Mühendislik Bilim. Derg., vol. 32, no. 2, pp. 359–368, 2020, doi: 10.35234/fumbd.771205.
  • M. Rostaghi and H. Azami, “Dispersion Entropy: A Measure for Time-Series Analysis,” IEEE Signal Process. Lett., vol. 23, no. 5, pp. 610–614, 2016, doi: 10.1109/LSP.2016.2542881.
  • H. Azami et al., “Multiscale fluctuation-based dispersion entropy and its applications to neurological diseases,” IEEE Access, vol. 7, pp. 68718–68733, 2019, doi: 10.1109/ACCESS.2019.2918560.
  • M. Zanin, L. Zunino, O. A. Rosso, and D. Papo, “Permutation entropy and its main biomedical and econophysics applications: A review,” Entropy, vol. 14, no. 8, pp. 1553–1577, 2012, doi: 10.3390/e14081553.
  • H. Azami, L. E. V. da Silva, A. C. M. Omoto, and A. Humeau-Heurtier, “Two-dimensional dispersion entropy: An information-theoretic method for irregularity analysis of images,” Signal Process. Image Commun., vol. 75, no. April, pp. 178–187, 2019, doi: 10.1016/j.image.2019.04.013.
  • E. Kafantaris, I. Piper, T. Y. M. Lo, and J. Escudero, “Augmentation of dispersion entropy for handling missing and outlier samples in physiological signal monitoring,” Entropy, vol. 22, no. 3, 2020, doi: 10.3390/e22030319.
  • M. Aslan, Y. Akbulut, A. Şengür, and M. C. Ince, “Skeleton based efficient fall detection,” J. Fac. Eng. Archit. Gazi Univ., 2017, doi: 10.17341/gazimmfd.369347.
  • F. Demir, M. Turkoglu, M. Aslan, and A. Sengur, “A new pyramidal concatenated CNN approach for environmental sound classification,” Appl. Acoust., 2020, doi: 10.1016/j.apacoust.2020.107520.
  • S. Yu, X. Li, X. Zhang, and H. Wang, “The OCS-SVM: An Objective-Cost-Sensitive SVM With Sample-Based Misclassification Cost Invariance,” IEEE Access, vol. 7, pp. 118931–118942, 2019, doi: 10.1109/access.2019.2933437.
  • X. Wu, W. Zuo, L. Lin, W. Jia, and D. Zhang, “F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation,” IEEE Trans. Neural Networks Learn. Syst., vol. 29, no. 11, pp. 5185–5199, 2018, doi: 10.1109/TNNLS.2018.2791507.
  • O. F. Alcin, A. Sengur, J. Qian, and M. C. Ince, “OMP-ELM: Orthogonal matching pursuit-based extreme learning machine for regression,” J. Intell. Syst., 2015, doi: 10.1515/jisys-2014-0095.
  • G. Bin Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, 2006, doi: 10.1016/j.neucom.2005.12.126.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İstatistik, Elektrik Mühendisliği
Bölüm Makaleler
Yazarlar

Muzaffer Aslan 0000-0002-2418-9472

Yayımlanma Tarihi 30 Aralık 2020
Gönderilme Tarihi 16 Ekim 2020
Kabul Tarihi 10 Aralık 2020
Yayımlandığı Sayı Yıl 2020Cilt: 3 Sayı: 3

Kaynak Göster

IEEE M. Aslan, “Normal Cumulative Distribution Function and Dispersion Entropy Based EMG Classification”, SAUCIS, c. 3, sy. 3, ss. 325–333, 2020, doi: 10.35377/saucis.03.03.811480.

Cited By

EMG Sinyalleri Kullanılarak GoogLeNet ve Çok Seviyeli DPD ile El Tutma Hareketlerinin Sınıflandırılması
Fırat Üniversitesi Mühendislik Bilimleri Dergisi
https://doi.org/10.35234/fumbd.932585
 Kapak Resmi İndir

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