Selección de características usando el algoritmo LEM para la clasificación de señales EMG
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Publicado:
jun 11, 2020
Palabras clave:
Electromiografía, clasificador, reconocimiento de gestos, algoritmo evolutivo, prótesis, mano robótica
Contenido principal del artículo
Resumen
En las aplicaciones médicas, la amputación de un brazo o la ausencia de un miembro del cuerpo inspira los avances tecnológicos en el área de la robótica para la creación deprótesis inteligentes que sustituyen y recuperan un porcentaje de la funcionalidad del miembro ausente de una persona. Una de las bases más importantes para el desarrollode las extremidades robóticas es el análisis y estudio de las señales EMG (señales electromiográficas de superficie). Las señales EMG proporcionan información sobre la dinámica de un músculo en sus diferentes estados y proporcionan valores de amplitud y frecuencia que describen el movimiento, la contracción y el descanso de un músculo. Para una señal EMG, existen características representativas como el valor RMS, el Histograma, la desviación estándar, entre otras funciones que permiten caracterizar una señal dada en el dominio del tiempo y la frecuencia. El objetivo es comparar los enfoques y características más utilizados de las señales EMG para diferenciar entre las diferentes señales que representan gestos o movimientos de la mano.
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Cómo citar
Londoño Lopera, J. C., González Alzate, J. P., Lage Cano, E. C., Vallejo Velasquez, M. A., & Ramírez Patiño, J. F. (2020). Selección de características usando el algoritmo LEM para la clasificación de señales EMG. Ingenio Magno, 10(2), 10-21. Recuperado a partir de http://revistas.ustatunja.edu.co/index.php/ingeniomagno/article/view/1896
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Artículos Vol. 10-2
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Citas
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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
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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.