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2022 | OriginalPaper | Buchkapitel

Electromyography Classification Techniques Analysis for Upper Limb Prostheses Control

verfasst von : F. A. Boris, R. T. Xavier, J. P. Codinhoto, J. E. Blanco, M. A. A. Sanches, C. A. Alves, A. A. Carvalho

Erschienen in: XXVII Brazilian Congress on Biomedical Engineering

Verlag: Springer International Publishing

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Abstract

The classification of surface electromyographic signals is an important task for the control of active upper-limb prostheses. This article aims to analyze and evaluate techniques to classify surface electromyographic signals for the control of upper limb prostheses. The electromyographic signals were obtained from a public database. Machine learning algorithms and seven features of the EMG signal were used to classify the signals. Random samples were created for the training and testing tasks in subsets with 80% and 20% of the data, respectively. Machine learning algorithms for classifying electromyographic signals were trained with different configurations, allowing evaluation between combinations of techniques and parameters. It was observed that signal feature extraction is an important process for obtaining accurate results. The best result produced an average accuracy of 95% with a Random Forest classifier and three features extracted from surface electromyography signals of two channels.

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Sapsanis C, Georgoulas G, Tzes A (2013) EMG based classification of basic hand movements based on time-frequency features. In: 21st Mediterranean Conference on Control and Automation, vol 21, pp 716–722. https://doi.org/10.1109/MED.2013.6608802

Chowdhury R, Reaz M, Ali M, Bakar A et al (2013) Surface electromyography signal processing and classification techniques. Sensors 13:12431–12466. https://doi.org/10.3390/s130912431CrossRef

Calado A, Soares F, Matos D (2019) A review on commercially available anthropomorphic myoelectric prosthetic hands, pattern-recognition-based microcontrollers and sEMG sensors used for prosthetic control. In: 2019 IEEE International conference on autonomous robot systems and competitions (ICARSC), pp 1–6. https://doi.org/10.1109/ICARSC.2019.8733629

Xavier RT, Boris FA, Castro FR et al (2016) Prótese de membro superior com movimentos pré-definidos pelo usuário. XXV Congr Bras Eng Bioméd 25:856–859. ISSN 2359-3164

Xavier RT, de Carvalho AA, Rohmer E et al (2019) Upper limb prosthesis for patients with congenital or acquired deformity. XXVI Congr Bras Eng Bioméd 26:723–728. https://doi.org/10.1007/978-981-13-2119-1_111CrossRef

Sapsanis C, Georgoulas G, Tzes A et al (2013) Improving EMG based classification of basic hand movements using EMD. In: 35th Annual international conference on IEEE engineering in medicine and biology society (EMBC), vol. 35, pp 5754–5757. https://doi.org/10.1109/EMBC.2013.6610858

Dheeru D, Karra Taniskidou E UCI machine learning repository at https://archive.ics.uci.edu/ml

Negi S, Kumar Y, Mishra VM (2016) Feature extraction and classification for EMG signals using linear discriminant analysis. In: 2nd International conference on advances in computing, communication, and automation (ICACCA), vol 2, pp 1–6. https://doi.org/10.1109/ICACCAF.2016.7748960

Jarrassé N, Nicol C, Touillet A et al (2017) Classification of phantom finger, hand, wrist, and elbow voluntary gestures in transhumeral amputees with sEMG. IEEE Trans Neural Syst Rehabil Eng 25(1):68–77. https://doi.org/10.1109/TNSRE.2016.2563222CrossRef

10.

Bellingegni AD, Gruppioni E, Colazzo G et al (2017) NLR, MLP, SVM, and LDA: a comparative analysis on EMG data from people with trans-radial amputation. J NeuroEngineering Rehabil 14(1):82. https://doi.org/10.1186/s12984-017-0290-6CrossRef

11.

Krasoulis A, Kyranou I, Erden MS et al (2017) Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements. J NeuroEngineering Rehabil 14(1):71. https://doi.org/10.1186/s12984-017-0284-4CrossRef

12.

Phukpattaranont P, Thongpanja S, Anam K et al (2018) Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal. Med Biol Eng Comput 56:2259–2271. https://doi.org/10.1007/s11517-018-1857-5CrossRef

13.

Wu Y, Liang S, Zhang L et al (2018) Gesture recognition method based on a single-channel sEMG envelope signal. EURASIP J Wirel Commun Netw 2018:35. https://doi.org/10.1186/s13638-018-1046-0CrossRef

14.

Yang S, Chai Y, Ai J et al (2018) Hand motion recognition based on GA optimized SVM using sEMG signals. In: 11th International symposium on computational intelligence and design (ISCID), vol 11, pp. 146–149. https://doi.org/10.1109/ISCID.2018.10134

15.

Pancholi S, Joshi AM (2019) Electromyography-based hand gesture recognition system for upper limb amputees. IEEE Sens Lett 3:1–4. https://doi.org/10.1109/LSENS.2019.2898257CrossRef

16.

Pancholi S, Joshi AM (2019) Time derivative moments based feature extraction approach for recognition of upper limb motions using EMG. IEEE Sens Lett 3:1–4. https://doi.org/10.1109/LSENS.2019.2906386CrossRef

17.

Van Rossum G, Drake FL (2009) Python 3 Reference Manual. CreateSpace, Scotts Valley

18.

Anaconda Software Distribution at https://docs.anaconda.com/

19.

Oliphant TE (2006) Guide to NumPy. Trelgol Publishing

20.

Virtanen P, Gommers R, Oliphant TE et al (2020) SciPy 1.0: fundamental algorithms for scientific computing in python. Nat Methods 17:261–272. https://doi.org/10.1038/s41592-019-0686-2CrossRef

21.

Hunter JD (2007) Matplotlib: A 2D graphics environment. Comput Sci Eng 9:90–95. https://doi.org/10.1109/MCSE.2007.55CrossRef

22.

McKinney W (2010) Data structures for statistical computing in Python. In: Proceedings of the 9th python in science conference, pp 51–56. https://doi.org/10.25080/Majora-92bf1922-00a

23.

Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830. https://doi.org/10.5555/1953048.2078195MathSciNetCrossRefMATH

24.

Jahan M, Manas M, Sharma BB et al (2015) Feature extraction and pattern recognition of EMG-based signal for hand movements. In: International symposium on advanced computing and communication (ISACC), pp 49–52. https://doi.org/10.1109/ISACC.2015.7377314

25.

James G, Witten D, Hastie T et al (2013) An introduction to statistical learning. Springer, New YorkCrossRef

26.

Raschka S (2016) Python machine learning: unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics. Packt Publishing, Birmingham Mumbai

Titel: Electromyography Classification Techniques Analysis for Upper Limb Prostheses Control
verfasst von: F. A. Boris
R. T. Xavier
J. P. Codinhoto
J. E. Blanco
M. A. A. Sanches
C. A. Alves
A. A. Carvalho
Verlag: Springer International Publishing
Buch: XXVII Brazilian Congress on Biomedical Engineering
Print ISBN: 978-3-030-70600-5

Electronic ISBN: 978-3-030-70601-2

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-70601-2_272

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