Fractional Integration Based Feature Extractor for EMG Signals

Year 2022, Volume: 10 Issue: 2, 132 - 138, 30.04.2022

İbrahim Ethem Saçu

https://doi.org/10.17694/bajece.899088

Abstract

Electromyography (EMG) signals have been extensively used for
identification of finger movements, hand gestures
and physical activities. In the classification of EMG signals, the
performance of the classifier is widely determined by the feature
extraction methods. Thus, plenty of feature extraction methods
based on time, histogram and frequency domain have been
reported in literature. However, these methods have several
drawbacks such as high time complexity, high computation demand
and user supplied parameters. To overcome these deficiencies, in
this work, a new feature extraction method has been proposed
to classify EMG signals taken from two different data sets finger
movements (FM) and physical actions (PA). While FM data set
includes 14 different finger movements, PA data set involves
20 different physical activities. The proposed method is based
on numerical fractional integration of time series EMG signals
with different fractional-orders. K Nearest Neighborhood (KNN)
classifier with 8-fold cross validation has been employed for
prediction of EMG signals. The derived fractional features can
give better results than the two commonly used time domain
features, notably, mean absolute value (MAV) and waveform
length (WL) in terms of accuracy. The experimental results are
also supported by statistical analysis results.

Keywords

Fractional integration, feature extraction, EMG, signal processing

References

• [1] C. Sapsanis, G. Georgoulas, and A. Tzes, “Emg based classification of basic hand movements based on time-frequency features,” in 21st Mediterranean Conference on Control and Automation, June 2013, pp. 716–722.
• [2] H. Kataoka and K. Sugie, “Recent advancements in lateral trunk flexion in parkinson disease,” Neurology. Clinical practice, vol. 9, no. 1, p. 74—82, February 2019.
• [3] F. H. Y. Chan, Yong-Sheng Yang, F. K. Lam, Yuan-Ting Zhang, and P. A. Parker, “Fuzzy emg classification for prosthesis control,” IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 3, pp. 305–311, Sep. 2000.
• [4] K. Andrianesis and A. Tzes, “Design of an anthropomorphic prosthetic hand driven by shape memory alloy actuators,” in 2008 2nd IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics, Oct 2008, pp. 517–522.
• [5] N. Parajuli, N. Sreenivasan, P. Bifulco, M. Cesarelli, S. Savino, V. Niola, D. Esposito, T. J. Hamilton, G. R. Naik, U. Gunawardana, and G. D. Gargiulo, “Real-time emg based pattern recognition control for hand prostheses: A review on existing methods, challenges and future implementation,” in Sensors, 2019.
• [6] A. Phinyomark, P. Phukpattaranont, and C. Limsakul, “Feature reduction and selection for emg signal classification,” Expert Syst. Appl., vol. 39, no. 8, p. 7420–7431, Jun. 2012.
• [7] X. Zhang, X. Chen, Y. Li, V. Lantz, K. Wang, and J. Yang, “A framework for hand gesture recognition based on accelerometer and emg sensors,” IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 41, no. 6, pp. 1064–1076, Nov 2011.
• [8] J. Qi, G. Jiang, G. Li, Y. Sun, and B. Tao, “Surface emg hand gesture recognition system based on pca and grnn,” Neural Computing and Applications, Mar 2019.
• [9] S. Raurale, J. McAllister, and J. M. del Rincon, “Emg wrist-hand motion ´ recognition system for real-time embedded platform,” ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1523–1527, 2019.
• [10] F. V. G. Tenore*, A. Ramos, A. Fahmy, S. Acharya, R. EtienneCummings, and N. V. Thakor, “Decoding of individuated finger movements using surface electromyography,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 5, pp. 1427–1434, May 2009.
• [11] R. N. Khushaba, S. Kodagoda, D. Liu, and G. Dissanayake, “Muscle computer interfaces for driver distraction reduction,” Computer Methods and Programs in Biomedicine, vol. 110, no. 2, pp. 137 – 149, 2013.
• [12] A. Phinyomark, R. N. Khushaba, and E. Scheme, “Feature extraction and selection for myoelectric control based on wearable emg sensors,” Sensors, vol. 18, no. 5, p. 1615, May 2018. [Online]. Available: http://dx.doi.org/10.3390/s18051615
• [13] O. ULKIR, G. Gokmen, and E. KAPLANOGLU, “Emg signal classification using fuzzy logic,” Balkan Journal of Electrical and Computer Engineering, vol. 5, no. 2, pp. 97–101, 2017.
• [14] F. Kuncan, Y. Kaya, and M. Kuncan, “A novel approach for activity recognition with down-sampling 1d local binary pattern,” Advances in Electrical and Computer Engineering, vol. 19, no. 1, pp. 35–44, 2019.
• [15] M. B. I. Reaz, M. S. Hussain, and F. Mohd-Yasin, “Techniques of emg signal analysis: detection, processing, classification and applications,” Biological Procedures Online, vol. 8, no. 1, pp. 11–35, Dec 2006.
• [16] K. Anam and A. Al-Jumaily, “Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees,” Neural Networks, vol. 85, pp. 51 – 68, 2017.
• [17] L. Zhang, Y. Shi, W. Wang, Y. Chu, and X. Yuan, “Real-time and user independent feature classification of forearm using emg signals,” Journal of the Society for Information Display, vol. 27, no. 2, pp. 101–107, 2019.
• [18] F. Kuncan, Y. Kaya, and M. Kuncan, “New approaches based on local binary patterns for gender identification from sensor signals,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 34, no. 4, pp. 2173–2185, 2019.
• [19] A. Ameri, M. A. Akhaee, E. Scheme, and K. Englehart, “Regression convolutional neural network for improved simultaneous EMG control,” Journal of Neural Engineering, vol. 16, no. 3, p. 036015, apr 2019.
• [20] J. Too, A. R. Abdullah, N. Mohd Saad, and W. Tee, “Emg feature selection and classification using a pbest-guide binary particle swarm optimization,” Computation, vol. 7, no. 1, 2019.
• [21] R. Magin, M. D. Ortigueira, I. Podlubny, and J. Trujillo, “On the fractional signals and systems,” Signal Processing, vol. 91, no. 3, pp. 350 – 371, 2011, advances in Fractional Signals and Systems.
• [22] Y. FERDI, “Some applications of fractional order calculus to design digital filters for biomedical signal processing,” Journal of Mechanics in Medicine and Biology, vol. 12, no. 02, p. 1240008, 2012.
• [23] N. Miljkovic, N. Popovi ´ c, O. Djordjevi ´ c, L. Konstantinovi ´ c, and T. B. ´ ˇ Sekara, “Ecg artifact cancellation in surface emg signals by fractional order calculus application,” Computer Methods and Programs in Biomedicine, vol. 140, pp. 259 – 264, 2017.
• [24] A. Goutas, Y. Ferdi, J.-P. Herbeuval, M. Boudraa, and B. Boucheham, “Digital fractional order differentiation-based algorithm for p and twaves detection and delineation,” ITBM-RBM, vol. 26, no. 2, pp. 127 – 132, 2005.
• [25] Y. Ferdi, J. Herbeuval, A. Charef, and B. Boucheham, “R wave detection using fractional digital differentiation,” ITBM-RBM, vol. 24, no. 5, pp. 273 – 280, 2003.
• [26] J. Wang, Y. Ye, X. Pan, and X. Gao, “Parallel-type fractional zerophase filtering for ecg signal denoising,” Biomedical Signal Processing and Control, vol. 18, pp. 36 – 41, 2015.
• [27] L. Cimesa, N. Popovi ˇ c, N. Miljkovi ´ c, and T. B. ´ Sekara, “Heart rate ˇ detection: Fractional approach and empirical mode decomposition,” in 2017 25th Telecommunication Forum (TELFOR), Nov 2017, pp. 1–4.
• [28] J. Wang, Y. Ye, X. Pan, X. Gao, and C. Zhuang, “Fractional zero-phase filtering based on the riemann–liouville integral,” Signal Processing, vol. 98, pp. 150 – 157, 2014.
• [29] J. Wang, Y. Ye, Y. Gao, S. Qian, and X. Gao, “Fractional compound integral with application to ecg signal denoising,” Circuits, Systems, and Signal Processing, vol. 34, no. 6, pp. 1915–1930, Jun 2015.
• [30] J. Baranowski, W. Bauer, M. Zagorowska, and P. Piatek, “On digital realizations of non-integer order filters,” Circuits, Systems, and Signal Processing, vol. 35, no. 6, pp. 2083–2107, 2016.
• [31] A. Kawala-Janik, W. Bauer, M. Zołubak, and J. Baranowski, “Early- ˙ stage pilot study on using fractional-order calculus-based filtering for the purpose of analysis of electroencephalography signals,” Studies in Logic, Grammar and Rhetoric, vol. 47, no. 1, pp. 103 – 111, 2016.
• [32] T. Moszkowski and E. Pociask, Comparison of Fractional- and Integer Order Filters in Filtration of Myoelectric Activity Acquired from Biceps Brachii. Heidelberg: Springer International Publishing, 2013, pp. 305– 312.
• [33] M. D. Ortigueira, “An introduction to the fractional continuous-time linear systems: the 21st century systems,” IEEE Circuits and Systems Magazine, vol. 8, no. 3, pp. 19–26, Third 2008.
• [34] A. Kilbas, H. Srivastava, and J. Trujillo, Theory And Applications of Fractional Differential Equations, ser. North-Holland Mathematics Studies. Elsevier Science & Tech, 2006.
• [35] K. Oldham and J. Spanier, The Fractional Calculus Theory and Applications of Differentiation and Integration to Arbitrary Order, ser. ISSN. Elsevier Science, 1974.
• [36] K. Diethelm, N. Ford, A. Freed, and Y. Luchko, “Algorithms for the fractional calculus: A selection of numerical methods,” Computer8 Methods in Applied Mechanics and Engineering, vol. 194, no. 6, pp. 743 – 773, 2005.