Feature selection using LEM algorithm for the classification of EMG signals
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Published:
Jun 11, 2020
Keywords:
Electromyography, classifier, gesture recognition, evolutionary algorithm, prosthesis, robotic hand
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Abstract
In medical applications, the amputation of an arm or the lack of a limb of the body inspires the technological advances in the area of robotics for the creation of intelligent prosthesis replaces and recovers a percentage of the functionality of the absent limb of a person. One of the most important bases for the development of robotic limbs is the analysis and study of EMG signals (surface electromyographic signals). EMG signals rovide information on the dynamics of a muscle in its different states and provide amplitude and frequency values that describes the movement, contraction and rest of a muscle. For an EMG signal, there are representative characteristics like the RMS value, Histogram, standard deviation, among other functions that allow characterizing a given signal in the time domain and frequency. The objective is to compare the most commonly used approaches and characteristics of EMG signals to differentiate between different signals that represent gestures or movements of the hand.
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How to Cite
Londoño Lopera, J. C., González Alzate, J. P., Lage Cano, E. C., Vallejo Velasquez, M. A., & Ramírez Patiño, J. F. (2020). Feature selection using LEM algorithm for the classification of EMG signals . Ingenio Magno, 10(2), 10-21. Retrieved from http://revistas.ustatunja.edu.co/index.php/ingeniomagno/article/view/1896
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Artículos Vol. 10-2
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J. E. Goez Mora, J. C. Londono Lopera, and D. A. Pati ˜ no Cortes, ˜ “Automatic Visual Classification of Parking Lot Spaces: A Comparison Between BoF and CNN Approaches,” in Communications in Computer and Information Science, 2018, pp. 160–170. [Online]. Available: http://link.springer.com/10.1007/978-3- 030-00350-014
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R. N. Khushaba, S. Kodagoda, M. Takruri, and G. Dissanayake, “Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals,” Expert Systems with Applications, vol. 39, no. 12, pp. 10 731– 10 738, 2012.
A. Dellacasa Bellingegni, E. Gruppioni, G. Colazzo, A. Davalli, R. Sacchetti, E. Guglielmelli, and L. Zollo, “NLR, MLP, SVM, and LDA: A comparative analysis on EMG data from people with trans-radial amputation,” Journal of NeuroEngineering and Rehabilitation, vol. 14, no. 1, pp. 1–16, 2017.
Y. Du, W. Jin, W. Wei, Y. Hu, and W. Geng, “Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation,” Sensors (Switzerland), vol. 17, no. 3, pp. 6–9, 2017.
V. K, P. LS, N. SN, and P. S, “Development of Surface-EMG Based Single Finger Movement Identification and Control for a Bionic Arm,” Journal of Bioengineering & Biomedical Science, vol. 08, no. 01, pp. 1–5, 2018.
V. Kehri, R. Ingle, R. Awale, and S. Oimbe, “Techniques of EMG signal analysis and classification of neuromuscular diseases,” vol. 137, pp. 485– 491, 2017.
H. F. Hassan, S. J. Abou-Loukh, and I. K. Ibraheem, “Teleoperated robotic arm movement using electromyography signal with wearable Myo armband,” Journal of King Saud University - Engineering Sciences, no. xxxx, 2019. [Online]. Available: https://doi.org/10.1016/j.jksues.2019.05.001
A. Phinyomark, P. Phukpattaranont, and C. Limsakul, “Feature reduction and selection for EMG signal classification,” Expert Systems with Applications, vol. 39, no. 8, pp. 7420–7431, 6 2012.
F. Riillo, L. R. Quitadamo, F. Cavrini, E. Gruppioni, C. A. Pinto, N. C. Pasto, L. Sbernini, L. Albero, and G. Saggio, “Optimization ` of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees,” Biomedical Signal Processing and Control, vol. 14, no. 1, pp. 117–125, 2014. [Online]. Available: http://dx.doi.org/10.1016/j.bspc.2014.07.007
U. Cotˆ e-Allard, C. L. Fall, A. Drouin, A. Campeau-Lecours, C. Gos- ´ selin, K. Glette, F. Laviolette, and B. Gosselin, “Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 4, pp. 760–771, 2019.
P. Sattam and B. Abdulaziz, “Deep Learning of EMG Time – Frequency Representations for Identifying Normal and Aggressive Actions,” vol. 18, no. 12, pp. 16–25, 2018.
F. Gomez-Donoso, S. Orts-Escolano, and M. Cazorla, “Largescale multiview 3D hand pose dataset,” Image and Vision Computing, vol. 81, pp. 25–33, 2019. [Online]. Available: https://doi.org/10.1016/j.imavis.2018.12.001
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J. S. Kim and S. B. Pan, “A Study on EMG-based Biometrics,” Journal of Internet Services and Information Security, vol. 2, no. May, pp. 19– 31, 2017.
J. F.-s. L. B, A.-a. Samadani, and D. Kuli, “Segmentation by Data Point Classification Applied to Forearm Surface EMG,” vol. 166, pp. 153–165, 2016. [Online]. Available: http://link.springer.com/10.1007/978-3- 319- 33681-7
A. Phinyomark and E. Scheme, “EMG Pattern Recognition in the Era of Big Data and Deep Learning,” Big Data and Cognitive Computing, vol. 2, no. 3, p. 21, 2018.
M. Atzori, A. Gijsberts, C. Castellini, B. Caputo, A. G. M. Hager, S. Elsig, G. Giatsidis, F. Bassetto, and H. Muller, “Electromyography ¨ data for non-invasive naturally-controlled robotic hand prostheses,” Scientific Data, vol. 1, pp. 1–13, 2014.
J. E. Goez Mora, J. C. Londono Lopera, and D. A. Pati ˜ no Cortes, ˜ “Automatic Visual Classification of Parking Lot Spaces: A Comparison Between BoF and CNN Approaches,” in Communications in Computer and Information Science, 2018, pp. 160–170. [Online]. Available: http://link.springer.com/10.1007/978-3- 030-00350-014
R. S. Michalski and L. Saitta, “Learnable Evolution Model: Evolutionary Processes Guided by Machine Learning,” Machine Learning, vol. 38, no. 1, pp. 9– 40, 2000. [Online]. Available: http://dx.doi.org/10.1023/A:1007677805 582.
G. Sheri and D. W. Corne, “The simplest evolution/learning hybrid: LEM with KNN,” in 2008 IEEE Cong.