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Hand Gestures Classification Using Multichannel sEMG Armband

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XXVI Brazilian Congress on Biomedical Engineering

Part of the book series: IFMBE Proceedings ((IFMBE,volume 70/2))

Abstract

This work presented a method to automatic classify six hand and wrist gestures (wrist flexion, wrist extension, wrist flexion to left, wrist extension to right, supination, and pronation) using multichannel sEMG signal features from the forearm and machine learning techniques. Data were collected using a wearable armband and the signal processed in LabVIEWTM platform. Six classifiers were evaluated: Multi-Layer Perceptron (MLP, an Artificial Neural Network), K-nearest neighbor (k-NN), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Decision Tree (DT), and Naïve Bayes (NB). The method demonstrated to be suitable, achieving high overall accuracy (over 90%) and up to 99% on single movements using MLP with 31 hidden layers. Other methods, such as LDA, QDA, k-NN and DT, have shown accuracy around 80% and therefore must not be reject due to its low computational complexity.

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References

  1. Akan, E., Tora, H., Uslu, B.: Hand gesture classification using inertial based sensors via a neural network. IEEE, pp. 140–143 (2017). https://doi.org/10.1109/icecs.2017.8292074

  2. Sarro Junior, A.D., Mendes Júnior, J.J.A., Frantz, S.H.: Controle de um braço robótico através de Eletromiografia. Universidade Tecnológica Federal do Paraná (2014)

    Google Scholar 

  3. Freer, D.R., Liu, J., Yang, G.-Z.: Optimization of EMG movement recognition for use in an upper limb wearable robot. IEEE, pp. 202–205 (2017). https://doi.org/10.1109/bsn.2017.7936041

  4. Jamal, M.Z.: Signal acquisition using surface EMG and circuit design considerations for robotic prosthesis. In: Naik, G.R. (eds.) Computational Intelligence in Electromyography Analysis—A Perspective on Current Applications and Future Challenges. InTech (2012)

    Google Scholar 

  5. Turchiello, G.M., Marino-Neto, J., Marques, J.L.B.: Plataforma computacional para processamento de sinais biomédicos. Uberlândia, Minas Gerais, pp. 1046–1049 (2014)

    Google Scholar 

  6. Daud, S.A., Elamvazuthi, I., Zulkifli, Z.B., et al.: Analysis of thermal imaging in determining muscle contractions of upper extremities. IEEE, pp. 1–5 (2017). https://doi.org/10.1109/roma.2017.8231828

  7. Bastos, I.L.O., Angelo, M.F., Loula, A.C.: Recognition of static gestures applied to Brazilian sign language (Libras). IEEE, pp. 305–312 (2015). https://doi.org/10.1109/sibgrapi.2015.26

  8. Soumya, C.V., Ahmed, M.: Artificial neural network based identification and classification of images of Bharatanatya gestures. IEEE, pp. 162–166 (2017). https://doi.org/10.1109/icimia.2017.7975593

  9. Nazarpour, K., Sharafat, A.R., Firoozabadi, S.M.P.: Surface EMG signal classification using a selective mix of higher order statistics. IEEE, pp. 4208–4211 (2005). https://doi.org/10.1109/iembs.2005.1615392

  10. Orosco, E., López, N., Soria, C., di Sciascio, F.: Surface electromyogram signals classification based on bispectrum. IEEE, pp. 4610–4613 (2010). https://doi.org/10.1109/iembs.2010.5626516

  11. Rusydi, M.I., Sasaki, M., Huda, S., et al.: Robot manipulator control using absolute encoder and electromyography signal. IEEE, pp. 109–113 (2016). https://doi.org/10.1109/acirs.2016.7556197

  12. Saikia, A., Mazumdar, S., Sahai, N., et al.: Comparative study and feature extraction of the muscle activity patterns in healthy subjects. IEEE, pp. 147–151 (2016). https://doi.org/10.1109/spin.2016.7566678

  13. Mazumdar, S., Saikia, A., Sahai, N., et al.: Determination of significant muscle in movement of upper limb using maximum voluntary contraction of EMG signal. IEEE, pp. 96–99 (2017). https://doi.org/10.1109/spin.2017.8049923

  14. Côté-Allard, U., Fall, C.L., Drouin, A., et al.: Deep learning for electromyographic hand gesture signal classification by leveraging transfer learning (2018)

    Google Scholar 

  15. Bailey, M., Grant, A., Lake, S.: Muscle interface device and method for interacting with content displayed on wearable head mounted displays, pp. 1–12 (2014)

    Google Scholar 

  16. Menon, R., Di Caterina, G., Lakany, H., et al.: Study on interaction between temporal and spatial information in classification of EMG signals for myoelectric prostheses. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 1832–1842 (2017). https://doi.org/10.1109/TNSRE.2017.2687761

    Article  Google Scholar 

  17. Moin, A., Zhou, A., Rahimi, A., et al.: An EMG gesture recognition system with flexible high-density sensors and brain-inspired high-dimensional classifier. IEEE, pp. 1–5 (2018). https://doi.org/10.1109/iscas.2018.8351613

  18. Caesarendra, W., Tjahjowidodo, T., Pamungkas, D.: EMG based classification of hand gestures using PCA and ANFIS. IEEE, pp. 18–23 (2017). https://doi.org/10.1109/robionetics.2017.8203430

  19. Mendes Júnior, J.J.A., Pires, M.B., Vieira, M.E.M., et al.: Desenvolvimento de armband com fusão de sEMG e giroscópio para identificação de grupos mulculares do braço. Foz do Iguaçu, Paraná, pp. 528–531 (2016)

    Google Scholar 

  20. Freitas, M.L.B., Mendes Junior, J.J.A., Pires, M.B., Stevan Jr., S.L.: Sistema de Extração de Características do sinal de Eletromiografia de Tempo e Frequência em LabVIEW. Even3 (2018). https://doi.org/10.29327/cobecseb.78825

  21. De Luca, C.J.: Surface electromyography: detection and recording (2002)

    Google Scholar 

  22. Clancy, E., Morin, E., Merletti, R.: Sampling, noise-reduction and amplitude estimation issues in surface electromyography. J. Electromyogr. Kinesiol. 12, 1–16 (2002). https://doi.org/10.1016/S1050-6411(01)00033-5

    Article  Google Scholar 

  23. Procedimentos de Distribuição de Energia Elétrica no Sistema Elétrico Nacional – PRODIST Módulo 8 – Qualidade da Energia Elétrica (2009)

    Google Scholar 

  24. Phinyomark, A., Phukpattaranont, P., Limsakul, C.: Feature reduction and selection for EMG signal classification. Expert Syst. Appl. 39, 7420–7431 (2012). https://doi.org/10.1016/j.eswa.2012.01.102

    Article  Google Scholar 

  25. Nazmi, N., Abdul Rahman, M., Yamamoto, S.-I., et al.: A review of classification techniques of EMG signals during isotonic and isometric contractions. Sensors 16, 1304 (2016). https://doi.org/10.3390/s16081304

    Article  Google Scholar 

  26. Shroffe, E.H., Manimegalai, P.: Hand gesture recognition based on EMG signals using ANN. Int. J. Comput. Appl. 2, 31–39 (2013)

    Google Scholar 

  27. Huang, H.-P., Chen, C.-Y.: Development of a myoelectric discrimination system for a multi-degree prosthetic hand. IEEE, pp. 2392–2397 (1999). https://doi.org/10.1109/robot.1999.770463

  28. Kim, K.S., Choi, H.H., Moon, C.S., Mun, C.W.: Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions. Curr. Appl. Phys. 11, 740–745 (2011). https://doi.org/10.1016/j.cap.2010.11.051

    Article  Google Scholar 

  29. Phinyomark, A., Quaine, F., Charbonnier, S., et al.: Feature extraction of the first difference of EMG time series for EMG pattern recognition. Comput. Methods Programs Biomed. 117, 247–256 (2014). https://doi.org/10.1016/j.cmpb.2014.06.013

    Article  Google Scholar 

  30. Hill, T., Lewicki, P.: Statistics: Methods and Applications: A Comprehensive Reference for Science, Industry, and Data Mining. StatSoft, Tulsa, OK (2006)

    Google Scholar 

  31. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 3rd edn. Elsevier, Burlington, MA (2011)

    MATH  Google Scholar 

  32. Balakrishnama, S., Ganapathiraju, A.: Linear discriminant analysis—a brief tutorial. Mississippi State University, Mississipi State (1998)

    Google Scholar 

  33. Tharwat, A., Gaber, T., Ibrahim, A., Hassanien, A.E.: Linear discriminant analysis: a detailed tutorial. AI Commun. 30, 169–190 (2017). https://doi.org/10.3233/AIC-170729

    Article  MathSciNet  Google Scholar 

  34. Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, Boston (1990)

    MATH  Google Scholar 

  35. Tharwat, A.: Linear vs. quadratic discriminant analysis classifier: a tutorial. Int. J. Appl. Pattern Recognit. 3, 145 (2016). https://doi.org/10.1504/ijapr.2016.079050

  36. Pan, F., Song, G., Gan, X., Gu, Q.: Consistent feature selection and its application to face recognition. J. Intell. Inf. Syst. 43, 307–321 (2014). https://doi.org/10.1007/s10844-014-0324-5

    Article  Google Scholar 

  37. Guo, Y., Hastie, T., Tibshirani, R.: Regularized linear discriminant analysis and its application in microarrays. Biostatistics 8, 86–100 (2007). https://doi.org/10.1093/biostatistics/kxj035

    Article  MATH  Google Scholar 

  38. James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning: With Applications in R. Springer, New York (2013)

    Book  Google Scholar 

  39. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)

    MATH  Google Scholar 

  40. Russell, S.J., Norvig, P., Davis, E.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Upper Saddle River (2010)

    MATH  Google Scholar 

  41. Da Silva, I.N., Spatti, D.H., Flauzino, R.A.: Redes neurais artificiais: para engenharia e ciências aplicadas. Artliber, São Paulo (2010)

    Google Scholar 

  42. Haykin, S.S.: Neural Networks: A Comprehensive Foundation, 2 edn. [Nachdr.]. Prentice Hall, Upper Saddle River, NJ (1999)

    Google Scholar 

  43. Dev, R., Singh, A.K.: Performance analysis of classifiers for EMG signal of different hand movements. Int. J. Biomed. Eng. Technol. 22, 233 (2016). https://doi.org/10.1504/IJBET.2016.079487

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. UTFPR-PG, Graduate School of Electronic Engineering, Federal University of Technology, Paraná (UTFPR), Ponta Grossa, 84016-210, Brazil

    Melissa La Banca Freitas & Sergio Luiz Stevan Jr.

  2. CPGEI, Graduate School of Electrical Engineering and Computer Science, Federal University of Technology, Paraná (UTFPR), Curitiba, 80230-901, Brazil

    José Jair Alves Mendes Jr. & Daniel Prado Campos

  3. PPGEE, Graduated Program in Electrical Engineering, Federal University of Technology, Paraná (UTFPR), Ponta Grossa, 84016-210, Brazil

    Sergio Luiz Stevan Jr.

Authors
  1. Melissa La Banca Freitas
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  2. José Jair Alves Mendes Jr.
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  3. Daniel Prado Campos
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  4. Sergio Luiz Stevan Jr.
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Corresponding author

Correspondence to Melissa La Banca Freitas .

Editor information

Editors and Affiliations

  1. INMETRO-Instituto Nacional de Metrologia, Qualidade e Tecnologia, Duque de Caxias, Rio de Janeiro, Brazil

    Rodrigo Costa-Felix

  2. Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    João Carlos Machado

  3. INMETRO-Instituto Nacional de Metrologia, Qualidade e Tecnologia, Duque de Caxias, Rio de Janeiro, Brazil

    André Victor Alvarenga

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La Banca Freitas, M., Mendes, J.A., Campos, D.P., Stevan, S.L. (2019). Hand Gestures Classification Using Multichannel sEMG Armband. In: Costa-Felix, R., Machado, J., Alvarenga, A. (eds) XXVI Brazilian Congress on Biomedical Engineering. IFMBE Proceedings, vol 70/2. Springer, Singapore. https://doi.org/10.1007/978-981-13-2517-5_37

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  • DOI: https://doi.org/10.1007/978-981-13-2517-5_37

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2516-8

  • Online ISBN: 978-981-13-2517-5

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