Abstract
Today, human-computer interfaces are increasingly more often used and become necessary for human daily activities. Among some remarkable applications, we find: Wireless-computer controlling through hand movement, wheelchair directing/guiding with finger motions, and rehabilitation. Such applications are possible from the analysis of electromyographic (EMG) signals. Despite some research works have addressed this issue, the movement classification through EMG signals is still an open challenging issue to the scientific community -especially, because the controller performance depends not only on classifier but other aspects, namely: used features, movements to be classified, the considered feature-selection methods, and collected data. In this work, we propose an exploratory work on the characterization and classification techniques to identifying movements through EMG signals. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification errors of 5.18% (KNN), 14.7407% (ANN) and 5.17% (Parzen-density-based classifier).
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References
Phinyomark, A., Phukpattaranont, P., Limsakul, C.: A review of control methods for electric power wheelchairs based on electromyography signals with special emphasis on pattern recognition. IETE Techn. Rev. 28(4), 316–326 (2011)
Aguiar, L.F., Bó, A.P.: Hand gestures recognition using electromyography for bilateral upper limb rehabilitation. In: 2017 IEEE Life Sciences Conference (LSC), pp. 63–66. IEEE (2017)
Halaki, M., Ginn, K.: Normalization of EMG signals: to normalize or not to normalize and what to normalize to? (2012)
Atzori, M., et al.: Electromyography data for non-invasive naturally-controlled robotic hand prostheses. Sci. Data 1, 140053 (2014)
Podrug, E., Subasi, A.: Surface EMG pattern recognition by using DWT feature extraction and SVM classifier. In: The 1st Conference of Medical and Biological Engineering in Bosnia and Herzegovina (CMBEBIH 2015), 13–15 March 2015 (2015)
Vicario Vazquez, S.A., Oubram, O., Ali, B.: Intelligent recognition system of myoelectric signals of human hand movement. In: Brito-Loeza, C., Espinosa-Romero, A. (eds.) ISICS 2018. CCIS, vol. 820, pp. 97–112. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76261-6_8
Atzori, M., et al.: Characterization of a benchmark database for myoelectric movement classification. IEEE Trans. Neural Syst. Rehabil. Eng. 23(1), 73–83 (2015)
Krishna, V.A., Thomas, P.: Classification of emg signals using spectral features extracted from dominant motor unit action potential. Int. J. Eng. Adv. Technol. 4(5), 196–200 (2015)
Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171–182. Springer, Heidelberg (1994). https://doi.org/10.1007/3-540-57868-4_57
Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Machine Learning Proceedings 1992, pp. 249–256. Elsevier (1992)
Romo, H., Realpe, J., Jojoa, P., Cauca, U.: Surface EMG signals analysis and its applications in hand prosthesis control. Rev. Av. en Sistemas e Informática 4(1), 127–136 (2007)
Shin, S., Tafreshi, R., Langari, R.: A performance comparison of hand motion EMG classification. In: 2014 Middle East Conference on Biomedical Engineering (MECBME), pp. 353–356. IEEE (2014)
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(3), 740–745 (2011)
Arozi, M., Putri, F.T., Ariyanto, M., Caesarendra, W., Widyotriatmo, A., Setiawan, J.D., et al.: Electromyography (EMG) signal recognition using combined discrete wavelet transform based on artificial neural network (ANN). In: International Conference of Industrial, Mechanical, Electrical, and Chemical Engineering (ICIMECE), pp. 95–99. IEEE (2016)
Pan, Z.W., Xiang, D.H., Xiao, Q.W., Zhou, D.X.: Parzen windows for multi-class classification. J. Complex. 24(5), 606–618 (2008)
Kurzynski, M., Wolczowski, A.: Hetero- and homogeneous multiclassifier systems based on competence measure applied to the recognition of hand grasping movements. In: Piętka, E., Kawa, J., Wieclawek, W. (eds.) Information Technologies in Biomedicine. AISC, vol. 4, pp. 163–174. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06596-0_15
Acknowledgements
This work is supported by the “Smart Data Analysis Systems - SDAS” group (http://sdas-group.com), as well as the “Grupo de Investigación en Ingeniería Eléctrica y Electrónica - GIIEE” from Universidad de Nariño. Also, the authors acknowledge to the research project supported by Agreement No. 095 November 20th, 2014 by VIPRI from Universidad de Nariño.
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Viveros-Melo, A. et al. (2018). Exploration of Characterization and Classification Techniques for Movement Identification from EMG Signals: Preliminary Results. In: Serrano C., J., Martínez-Santos, J. (eds) Advances in Computing. CCC 2018. Communications in Computer and Information Science, vol 885. Springer, Cham. https://doi.org/10.1007/978-3-319-98998-3_11
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