Seleção de recursos usando algoritmo LEM para a classificação de sinais EMG
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Publicado:
Jun 11, 2020
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Eletromiografia, classificador, reconhecimento de gestos, algoritmo evolutivo, prótese, mão robótica
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Resumo
Nas aplicações médicas, a amputação de um braço ou a falta de um membro do corpo inspira os avanços tecnológicos na área da robótica para a criação de próteses inteligentes substitui e recupera uma porcentagem da funcionalidade do membro ausente de uma pessoa. Uma das bases mais importantes para o desenvolvimento de membros robóticos é a análise e estudo de sinais EMG (sinais eletromiográficos de superfície). Os sinais EMG fornecem informações sobre a dinâmica de um músculo em seus diferentes estados e fornecemvalores de amplitude e frequência que descrevem o movimento, contração e descanso de um músculo. Para um sinal EMG, existem características representativas como o valor RMS, Histograma, desvio padrão, entre outras funções que permitem caracterizar um determinado sinal no domínio de tempo e frequência. O objetivo é comparar as abordagens e características mais utilizadas os sinais EMG para diferenciar entre diferentes sinais que representam gestos ou movimentos da mão.
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Como Citar
Londoño Lopera, J. C., González Alzate, J. P., Lage Cano, E. C., Vallejo Velasquez, M. A., & Ramírez Patiño, J. F. (2020). Seleção de recursos usando algoritmo LEM para a classificação de sinais EMG. Ingenio Magno, 10(2), 10-21. Recuperado de http://revistas.ustatunja.edu.co/index.php/ingeniomagno/article/view/1896
Seção
Artículos Vol. 10-2
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Por medio del presente documento, certifico(amos) que el artículo que se presenta para posible publicación en la revista institucional INGENIO MAGNO del Centro de Investigaciones de Ingeniería Alberto Magno CIIAM de la Universidad Santo Tomás, seccional Tunja, es de mi (nuestra) entera autoría, siendo su contenido producto de mi (nuestra) directa contribución intelectual y aporte al conocimiento.
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Referências
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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.
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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
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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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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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A. Carf`ı, F. Foglino, B. Bruno, and F. Mastrogiovanni, “A multi-sensor dataset of human-human handover,” Data in Brief, vol. 22, pp. 109–117, 2019. [Online]. Available: https://doi.org/10.1016/j.dib.2018.11.110
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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.
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
R. Li, Z. Liu, and J. Tan, “A survey on 3D hand pose estimation: Cameras, methods, and datasets,” Pattern Recognition, vol. 93, pp. 251– 272, 2019.
A. Carf`ı, F. Foglino, B. Bruno, and F. Mastrogiovanni, “A multi-sensor dataset of human-human handover,” Data in Brief, vol. 22, pp. 109–117, 2019. [Online]. Available: https://doi.org/10.1016/j.dib.2018.11.110
S. Pizzolato, L. Tagliapietra, M. Cognolato, M. Reggiani, H. Muller, and ¨ M. Atzori, “Comparison of six electromyography acquisition setups on hand movement classification tasks,” PLoS ONE, vol. 12, no. 10, pp. 1–17, 2017.
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.