Determination of the relationship between EMG signals and hand grips using a commercial Myo armband

• Autorzy: Błędowski Michał , Wołczowski Andrzej

Identyfikatory

DOI

10.14313/JAMRIS/2-2019/17

• Języki publikacji: EN

Abstrakty

EN

The work discusses the construction of a measurement system for determining the relationship between EMG signals and hand grip movements. The relationship is necessary for the synthesis of control of the hand bioprosthesis. The measurement system is based on commercial Myo armband with EMG signals sensors and sensory glove with bend and pressure sensors. There are presented possibilites, advantages and disadvantages of such approach.

Słowa kluczowe

EN

EMG signals; electromyography; pattern recognition; hand bioprosthesis control

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2019
• Tom: Vol. 13, No. 2
• Strony: 40--47
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

Błędowski Michał

• Chair of Cybernetics and Robotics, Faculty of Electronics, Wroclaw University of Science and Technology, ul. Janiszewskiego 11/17, Wrocław, Poland

autor

Wołczowski Andrzej

• Chair of Cybernetics and Robotics, Faculty of Electronics, Wroclaw University of Science and Technology, ul. Janiszewskiego 11/17, Wrocław, Poland

Bibliografia

• [1] “MYO armband tech specs”. https: //support.getmyo.com/hc/en-us/articles/202647853-What-gestures-doesthe-Myo-armband-recognize-. Accessed on:28.08.2019.
• [2] H. M. Al-Angari, G. Kanitz, S. Tarantino, and C. Cipriani, “Distance and mutual information methods for EMG feature and channel subset selection for classification of hand movements”, Biomedical Signal Processing and Control, vol. 27,2016, 24–31, 10.1016/j.bspc.2016.01.011.
• [3] M. Geryes, J. Charara, A. Skaiky, A. Mcheick, and J. Girault, “A novel biomedical application for the Myo gesture control armband”. In: 2017 29th International Conference on Microelectronics (ICM), 2017, 1–4, 10.1109/ICM.2017.8268823.
• [4] K. H. Ghazali, M. F. Mansor, M. M. Mustafa, and A. Hussain, “Feature Extraction Technique using Discrete Wavelet Transform for Image Classification”. In: 2007 5th Student Conference on Research and Development, 2007, 1–4, 10.1109/SCORED.2007.4451366.
• [5] A. A. Hidayat, Z. Arief, and D. C. Happyanto, “LOVETT Scaling with Flex Sensor and MYO Armband for Monitoring Finger Muscles Therapy of Post-Stroke People”, EMITTER International Journal of Engineering Technology, vol. 3, no. 2, 2016, 60–76, 10.24003/emitter.v3i2.45.
• [6] M. Kurzynski and A. Wolczowski, “Multiple Classifier System Applied to the Control of Bioprosthetic Hand Based on Recognition of Multimodal Biosignals”. In: J. Goh, ed., The 15th International Conference on Biomedical Engineering, 2014, 577–580.
• [7] M. Kurzynski, M. Krysmann, P. Trajdos, and A. Wolczowski, “Multiclassifier system with hybrid learning applied to the control of bioprosthetic hand”, Computers in Biology and Medicine, vol. 69, 2016, 286–297, 10.1016/j.compbiomed.2015.04.023.
• [8] I. Mendez, B. W. Hansen, C. M. Grabow, E. J. L. Smedegaard, N. B. Skogberg, X. J. Uth, A. Bruhn, B. Geng, and E. N. Kamavuako, “Evaluation of the Myo armband for the classification of hand motions”. In: 2017 International Conference on Rehabilitation Robotics (ICORR), 2017, 1211–1214,10.1109/ICORR.2017.8009414.
• [9] M. Mironovova and J. Bı́la, “Fast fourier transform for feature extraction and neural network for classification of electrocardiogram signals”. In: 2015 Fourth International Conference on Future Generation Communication Technology (FGCT), 2015, 1–6, 10.1109/FGCT.2015.7300244.
• [10] E. Sejdić, I. Djurović, and J. Jiang, “Timefrequency feature representation using Energy concentration: An overview of recent advances”, Digital Signal Processing, vol. 19, no. 1, 2009, 153–183, 10.1016/j.dsp.2007.12.004.
• [11] P. Tu and J. Chung, “A new decision-tree classification algorithm for machine learning”. In: Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI ‘92, 1992,370–377, 10.1109/TAI.1992.246431.
• [12] A. Wołczowski and K. Krysztoforski, “Artificial hand control via EMG signal classification – experimental investigation of alghoritms”. In: K. Tchoń , ed., Progress in robotics, Wydawnictwa Komunikacji i Łączności, Warszawa, 2008, 97–122.
• [13] A. Wołczowski and S. Myśliński, “Identifying the relation between finger motion and EMG signals for bioprosthesis control”. In: Proceedings of the 12th IEEE International Conference on Methods and Models in Automation and Robotics (MMAR 2006), 2006, 817–822.
• [14] A. Wołczowski, M. Błędowski, and J. Witkowski, “System do rejestracji sygnałów EMG i MMG dla sterowania bioprotezą dłoni”, Prace Naukowe Politechniki Warszawskiej. Elektronika, vol. z. 195, t. 1, 2016.
• [15] A. Wolczowski and M. Kurzynski, “Control of hand prosthesis using fusion of information from bio-signals and from prosthesis sensors”. In: 2014 Asia-Pacific Conference on Computer Aided System Engineering (APCASE), 2014, 19–24, 10.1109/APCASE.2014.6924465.
• [16] G. P. Zhang, “Neural networks for classification: a survey”, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 30, no. 4, 2000, 451–462, 10.1109/5326.897072.
• [17] S. Zhang, X. Li, M. Zong, X. Zhu, and R. Wang, “Efficient kNN Classification With Different Numbers of Nearest Neighbors”, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 5, 2018, 1774–1785, 10.1109/TNNLS.2017.2673241.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).