Published May 27, 2021


Google Scholar
Search GoogleScholar

María Alexandra Fajardo-Perdomo, MSc

Verónica Guardo Gómez, BSc

Alvaro David Orjuela-Cañon, PhD

Andrés Felipe Ruiz-Olaya, PhD



Objective: To evaluate a group of features in a myoelectric pattern recognition algorithm to differentiate between five angular positions of the wrist during flexion-extension movements. Materials and Methods: An experimental configuration was made to capture the EMG and wrist joint angle related to flexion-extension movements. After that, a myoelectric pattern recognition algorithm based on a multilayer perceptron artificial neural network (ANN) was implemented. Three different groups were used: Time domain characteristics, autoregressive (AR) model parameters, and representation of time frequency using Wavelet transform (WT). Results and Discussion: The experimental results of 10 healthy subjects indicate that the coefficients of the AR models offer the best parameters for classification, with a differentiation rate of 78 % for the five angular positions studied. The combination of frequency and time frequency resulted in a differentiation rate that reached 82 %. Conclusions: An algorithm based on pattern recognition of EMG signals was used to carry out a comparative study of groups of features that allow for the differentiation of the angular position of the wrist in terms of flexion-extension movements. The method has the potential for application in the field of rehabilitation engineering to detect the user’s movement intent.


Movement intent, electromyography signals, pattern recognition, machine learning techniques, artificial neural networksintencionalidad de movimiento, señales de electromiografía, reconocimiento de patrones, técnicas de aprendizaje automático, redes neuronales artificiales

[1] R. Merletti and P. A. Parker, Electromyography: Physiology, Engineering, and Non-invasive Applications. Hoboken, NJ: John Wiley & Sons, 2004.
[2] C. Germany, “A low cost signal acquisition board design for myopathy’s EMG database construction,” in 13th Int. Multi-Conf. Syst. Signals Devices (SSD), 2016, pp. 274–279. doi: 10.1109/SSD.2016.7473767
[3] S. Kalwa, “Neuromuscular disease classification based on discrete wavelet transform of dominant motor unit action potential of EMG signal,” in 2015 Int. Conf. Inf. Process. (ICIP), 2015. doi: 10.1109/INFOP.2015.7489474
[4] A. A. Ali, A. Albarahany, and L. Quan, “EMG signals detection technique in voluntary muscle movement,” in 6th Int. Conf. New Trends Inf. Sci. Serv. Sci. Data Min. (ISSDM), Taipei, Taiwan, 2012, pp. 738–742. Available:
[5] S. Thongpanja, A. Phinyomark, F. Quaine, Y. Laurillau, C. Limsakul, and P. Phukpattaranont, “Probability density functions of stationary surface EMG signals in noisy environments,” IEEE Trans. Instrum. Meas., 2016, vol. 65, no. 7, pp. 1547–1557. Available:
[6] F. Ortes, D. Karabulut, and Y. Z. Arslan, “General perspectives on electromyography signal features and classifiers used for control of human arm prosthetics,” in Adv. Methodol. Technol. Eng. Environ. Sci. Hershey, PA: IGI Global, 2019, pp. 1–17.
[7] C. Garg, “Development of a software module for feature extraction and classification of EMG signals,” in Conf. Commun. Control Intell. Syst. (CCIS), 2015, pp. 250–254.
[8] C. J. G. Duque, L. D. Muñoz, J. G. Mejía, and E. D. Trejos, “Discrete wavelet transform and k-nn classification in EMG signals for diagnosis of neuromuscular disorders,” in Image, Signal Process. Artificial Vision (STSIVA), 2014 XIX Sym., pp. 1–5. Available:
[9] M. Jahan, M. Manas, B. B. Sharma, and B. B. Gogoi, “Feature extraction and pattern recognition of EMG-based signal for hand movements,” in Adv. Comput. Commun. (ISACC), 2015 Int. Sym., pp. 49–52. Available:
[10] C. Sapsanis, G. Georgoulas, and A. Tzes, “EMG based classification of basic hand movements based on time-frequency features,” in Control Autom. (MED), 2013 21st Mediterranean Conf., pp. 716–722. Available:
[11] L. Pan, D. Zhang, X. Sheng, and X. Zhu, “Residuals of autoregressive model providing additional information for feature extraction of pattern recognition-based myoelectric control,” in Eng. Med. Biol. S. (EMBC), 2015 37th Annu. Int. Conf. IEEE, pp. 7270–7273. Available:
[12] I. Haider, M. Shahbaz, M. Abdullah, and M. Nazim, “Feature Extraction for Identification of Extension and Flexion Movement of Wrist using EMG Signals,” in Electr. Comput. Eng. (CCECE), 2015 IEEE 28th Can. Conf., pp. 792–795. Available:
[13] J. Liu, “Feature dimensionality reduction for myoelectric pattern recognition: A comparison study of feature selection and feature projection methods,” Med. Eng. Phys., vol. 36, no. 12, pp. 1716–1720, 2014. Available:
[14] G. Purushothaman and R. Vikas, “Identification of a feature selection based pattern recognition scheme for finger movement recognition from multichannel EMG signals,” Australas. Phys. Eng. Sci. Med., vol. 41, no. 2, pp. 549–559, 2018. doi: 10.1007/s13246-018-0646-7
[15] N. Nazmi, M. Abdul Rahman, S.-I. Yamamoto, S. Ahmad, H. Zamzuri, and S. Mazlan, “A review of classification techniques of EMG signals during isotonic and isometric contractions,” Sensors, vol. 16, no. 8, p. 1304, 2016. doi: 10.3390/s16081304
[16] M.-K. Kim, M. Kim, E. Oh, and S.-P. Kim, “A review on the computational methods for emotional state estimation from the human EEG,” Comput. Math. Methods Med., vol. 2013, 2013. doi: 10.1155/2013/573734
[17] R. M. Rangayyan, Biomedical Signal Analysis. New Jersey: John Wiley & Sons, 2015.
[18] P. Onsy, A. Alim, M. Moselhy, and E. F. Mroueh, “EMG Signal Processing and Diagnostic of Muscle Diseases,” in 2012 6th Int. Conf. New Trends Inf. Sci. Serv. Sci. Data Mining (ISSDM), pp. 1–6. Available:
[19] M. H. Jali, T. A. Izzuddin, Z. H. Bohari, H. I. Jaafar, and M. N. M. Nasir, “Pattern recognition of EMG signal during load lifting using Artificial Neural Network (ANN),” in Control Syst., Comput. Eng. (ICCSCE), 2015 IEEE Int. Conf., pp. 172–177. Available:
[20] M. Sood, “A novel module based approach for classifying epileptic seizures using EEG signals,” in Ind. Inform. Comput. Syst. (CIICS), 2016 Int. Conf., pp. 3–7. Available:
[21] N. Jiang, J. L. G. Vest-Nielsen, S. Muceli, and D. Farina, “EMG-based simultaneous and proportional estimation of wrist/hand kinematics in uni-lateral trans-radial amputees,” J. Neuroeng. Rehabil., vol. 9, no. 1, p. 42, 2012. doi: 10.1186/1743-0003-9-42
[22] X. Zhang, X. Li, O. W. Samuel, Z. Huang, P. Fang, and G. Li, “Improving the robustness of electromyogram-pattern recognition for prosthetic control by a postprocessing strategy,” Front. Neurorobot., vol. 11, p. 51, 2017. DOI: 10.3389/fnbot.2017.00051
[23] R. Merletti and D. Farina, Surface Electromyography: Physiology, Engineering, and Applications. New Jersey: John Wiley & Sons, 2016.
[24] D. Stegeman and H. Hermens, “Standards for surface electromyography: The European project Surface EMG for non-invasive assessment of muscles (SENIAM),” Enschede Roessingh Res. Dev., pp. 108–112, 2007.
[25] L. Logesparan, A. J. Casson, and E. Rodriguez-Villegas, “Assessing the impact of signal normalization: Preliminary results on epileptic seizure detection,” in Eng. Med. Biol. Soc., EMBC, 2011 Ann. Int. Conf. IEEE, pp. 1439–1442. doi: 10.1109/IEMBS.2011.6090356
[26] J. Wang, L. Tang, and J. E. Bronlund, “A pattern recognition system for myoelectric based prosthesis hand control,” in. Ind. Electron. Appl. (ICIEA), 2015 IEEE 10th Conf., pp. 830–834. doi: 10.1109/ICIEA.2015.7334225 ·
[27] A. López-Delis, A. F. Ruiz-Olaya, T. Freire-Bastos, and D. Delisle-Rodríguez, “A comparison of myoelectric pattern recognition methods to control an upper limb active exoskeleton,” in Iberoamer. Congr. Pattern Recogn., 2013, pp. 100–107. Available:
[28] S. Negi, Y. Kumar, and V. M. Mishra, “Feature extraction and classification for EMG signals using linear discriminant analysis,” in Adv. Comput. Commun. Autom. (ICACCA) Int. Conf., 2016, pp. 1–6. doi: 10.1109/ICACCAF.2016.7748960
[29] A. López-Delis and A. F. Ruiz Olaya, “Métodos computacionales para el reconocimiento de patrones mioeléctricos en el control de exoesqueletos robóticos: una revisión,” INGE@ UAN-Tenden. Ing., vol. 3, no. 5, 2013. Available:
[30] M. A. Oskoei and H. Hu, “Myoelectric control systems: A survey,” Biomed. Signal Process. Control, vol. 2, no. 4, pp. 275–294, 2007. Available:
[31] Z. Arief, I. A. Sulistijono, and R. A. Ardiansyah, “Comparison of five time series EMG features extractions using Myo Armband,” in Electron. Symp. (IES), 2015 Int., pp. 11–14. Available:
[32] M. Haris, “EMG signal based finger movement recognition for prosthetic hand control,” in Commun. Control Intell. Syst. (CCIS), 2015. Available:
[33] B. S. Zheng, M. Murugappan, S. Yaacob, and S. Murugappan, “Human emotional stress analysis through time domain electromyogram features,” in Indu. Electron. (ISIE), IEEE Int. Symp., 2013, pp. 172–177. Available:
[34] S. V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction. New Jersey John Wiley & Sons, 2008.
[35] L. Ljung, System Identification: Theory for the User, 2nd ed. Up. Saddle River, NJ: PTR Prentice-Hall, 1999.
[36] A. Subasi, “Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines,” Comput. Biol. Med., vol. 42, no. 8, pp. 806–815, 2012. doi: 10.1016/j.compbiomed.2012.06.004
[37] K. Englehart, B. Hudgins, P. A. Parker, and M. Stevenson, “Classification of the myoelectric signal using time-frequency based representations,” Med. Eng. Phys., vol. 21, no. 6, pp. 431–438, 1999. Available:
[38] G. Strang and T. Nguyen, Wavelets and Filter Banks. Wellesley: Cambridge Press, 1996.
[39] T. Kamali, R. Boostani, and H. Parsaei, “A multi-classifier approach to MUAP classification for diagnosis of neuromuscular disorders,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 1, pp. 191–200, 2014. doi: 10.1109/TNSRE.2013.2291322
[40] R. Kohavi, “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection,” in Int. Joint Conf. Artificial Intell., 1995, pp. 1137–1143. Available:
[41] S. Haykin, Neural Networks and Learning Machines. New Jersey: Prentice Hall, 2009.
[42] A. Akhtar, L. J. Hargrove, and T. Bretl, “Prediction of distal arm joint angles from EMG and shoulder orientation for prosthesis control,” in Eng. Med. Biol. Soc. (EMBC), 2012 Annu. Int. Conf. IEEE, pp. 4160–4163. Available: doi: 10.1109/EMBC.2012.6346883
[43] R. S. Naoum, N. A. Abid, and Z. N. Al-Sultani, “An enhanced resilient backpropagation artificial neural network for intrusion detection system,” Int. J. Comput. Sci. Netw. Secur., vol. 12, no. 3, p. 11, 2012.
[44] S. Abe, Support Vector Machines for Pattern Classification, vol. 2. Berlín: Springer, 2005.
[45] J. Yousefi and A. Hamilton-Wright, “Characterizing EMG data using machine-learning tools,” Comput. Biol. Med., vol. 51, pp. 1–13, 2014. doi: 10.1016/j.compbiomed.2014.04.018
[46] L. R. Quitadamo et al., “Support vector machines to detect physiological patterns for EEG and EMG-based human: Computer interaction; A review,” J. Neural Eng., vol. 14, no. 1, p. 11001, 2017. doi: 10.1088/1741-2552/14/1/011001
[47] A. Alkan and M. Günay, “Identification of EMG signals using discriminant analysis and SVM classifier,” Expert Syst. Appl., vol. 39, no. 1, pp. 44–47, 2012. doi: 10.1016/j.eswa.2011.06.043
[48] P. Konar and P. Chattopadhyay, “Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs),” Appl. Soft Comput., vol. 11, no. 6, pp. 4203–4211, 2011. doi: 10.1016/j.asoc.2011.03.014
[49] G. L. Prajapati and A. Patle, “On performing classification using SVM with radial basis and polynomial kernel functions,” in 2010 3rd Int. Conf. Emerging Trends Eng. Technol., 2010, pp. 512–515. Available:
[50] P. A. Kaplanis, C. S. Pattichis, D. Zazula, and others, “Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders,” Med. Biol. Eng. Comput., vol. 48, no. 8, pp. 773–781, 2010. doi: 10.1007/s11517-010-0629-7
[51] G. R. Naik, D. K. Kumar, and others, “Twin SVM for gesture classification using the surface electromyogram,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 2, pp. 301–308, 2009. Available:
[52] X. Li, Q. Huang, J. Zhu, W. Sun, and H. She, “A novel proportional and simultaneous control method for prosthetic hand,” J. Mech. Med. Biol., vol. 17, no. 08, p. 1750120, 2017. doi: 10.1142/S0219519417501202
[53] H. Shim, H. An, S. Lee, E. H. Lee, H. Min, and S. Lee, “EMG pattern classification by split and merge deep belief network,” Symmetry, vol. 8, no. 12, p. 148, 2016. doi: 10.3390/sym8120148
[54] A. Phinyomark, P. Phukpattaranont, and C. Limsakul, “Feature reduction and selection for EMG signal classification,” Expert Syst. Appl., vol. 39, no. 8, pp. 7420–7431, 2012. doi: 10.1016/j.eswa.2012.01.102
How to Cite
Fajardo-Perdomo, M. A., Guardo-Gómez, V., Orjuela-Cañón, A. D., & Ruiz-Olaya, A. F. (2021). Classification of the Angular Position During Wrist Flexion-extension Based on EMG Signals. Ingenieria Y Universidad, 25.
Special Section: Health Care Engineering