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Embedded System for Hand Gesture Recognition Using EMG Signals: Effect of Size in the Analysis Windows

  • Juan Mantilla-Brito
  • David Pozo-EspínEmail author
  • Santiago Solórzano
  • Luis Morales
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1078)

Abstract

The electromyography signals (EMG) analysis in the field of robotics has had a great impact due to its application in prosthesis and system control using electrical signals resulting from the action of muscles associated with different parts of the human body. In this article, an embedded system of hand gesture recognition based on EMG signals measured in the forearm is implemented. The EMG signals are acquired through the Myo Armband sensor and processed with a 32-bit STM microcontroller. The signals are classified with the Naïve Bayes algorithm (NB) and by recognizing an established pattern (open or closed), the system sends control signals to a robotic hand to replicate the movement. As main contribution, a comparative analysis of the performance of the embedded system is presented based on: number of samples (analysis windows), acquisition and processing times, as well as the macro and micro-evaluation of multiclass metrics (Accuracy, Precision, Recall and F-Score) for the recognition of movements.

Keywords

Myo Armband Naïve Bayes Embedded system 32-bits microcontroller 
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References

  1. 1.
    Shroffe, E.H., Manimegalai, P.: Hand gesture recognition based on EMG signals using ANN. In: International Conference on Consumer Electronics, ICCE, Berlin, vol. 2013, no. 3, pp. 174–178. IEEE, Berlin, April, 2013Google Scholar
  2. 2.
    Morales, L., Pozo, D.: An experimental comparative analysis among different classifiers applied to identify hand movements based on sEMG. In: 2nd Ecuador Technical Chapters Meeting 2017, ETCM, vol. 2017, pp. 1–6, IEEE, Ecuador, January 2018Google Scholar
  3. 3.
    Mishra, V.K., Bajaj, V., Kumar, A., Sharma, D., Singh, G.K.: An efficient method for analysis of EMG signals using improved empirical mode decomposition. AEU - Int. J. Electron. Commun., 200–209 (2017)CrossRefGoogle Scholar
  4. 4.
    Gruebler, A., Suzuki, K.: Design of a wearable device for reading positive expressions from facial EMG signals. Trans. Affect. Comput. 5(3), 227–237 (2014)CrossRefGoogle Scholar
  5. 5.
    Tsipouras, M.G.: Uterine EMG signals spectral analysis for pre-term birth prediction 8(5), 3310–3315 (2018)Google Scholar
  6. 6.
    Flood, M.W., Jensen, B.R., Malling, A.S., Lowery, M.M.: Increased EMG intermuscular coherence and reduced signal complexity in Parkinson’s disease. Clin. Neurophysiol. 130(2), 259–269 (2018)CrossRefGoogle Scholar
  7. 7.
    Ghassemi, M., et al.: Development of an EMG-controlled serious game for rehabilitation. Trans. Neural Syst. Rehabil. Eng. PP(c), 1 (2019)Google Scholar
  8. 8.
    Bastos-Filho, T., Longo, B., Sime, M.: Serious game based on Myo Armband for upper-limb rehabilitation exercises, vol. 70/2, pp. 701–704 (2019)Google Scholar
  9. 9.
    Correa-Figueroa, J.L., Morales-Sánchez, E., Huerta-Ruelas, J.A., González-Barbosa, J.J., Cárdenas-Pérez, C.R.: Sistema de adquisición de señales SEMG para la detección de fatiga muscular. Rev. Mex. Ing. Biomed. 37(1), 17–27 (2016)Google Scholar
  10. 10.
    Morais, G.D., Neves, L.C., Masiero, A.A., Castro, M.C.F.: Application of Myo Armband system to control a robot interface. In: BIOSTEC, vol. 4, pp. 227–231 (2016)Google Scholar
  11. 11.
    Xu, Y., Yang, C., Liang, P., Zhao, L., Li, Z.: Development of a hybrid motion capture method using MYO Armband with application to teleoperation. In: Conference Mechatronics and Automation, IEEE ICMA 2016, pp. 1179–1184. IEEE (2016)Google Scholar
  12. 12.
    Nymoen, K., Romarheim, M., Alexander, H., Jensenius, R.: MuMYO—evaluating and exploring the MYO Armband for musical interaction. In: New Interfaces Musical Expression, vol. 179 (2015)Google Scholar
  13. 13.
    Haque, F., Nancel, M., Vogel, D.: Myopoint: pointing and clicking using forearm mounted electromyography and inertial motion sensors. In: Proceedings 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI 2015, vol. 1, pp. 3653–3656 (2015)Google Scholar
  14. 14.
    Rawat, S., Vats, S., Kumar, P.: Evaluating and exploring the MYO ARMBAND. In: Proceedings 5th International Conference System Modeling and Advancement in Research Trends, SMART 2016, pp. 115–120 (2016)Google Scholar
  15. 15.
    Sathiyanarayanan, M., Rajan, S.: MYO Armband for physiotherapy healthcare: a case study using gesture recognition application. In: 8th International Conference on Communication Systems Networks, COMSNETS 2016, pp. 1–6 (2016)Google Scholar
  16. 16.
    Shin, H.-S., Ganiev, A., Lee, K.-H.: Design of a virtual robotic arm based on the EMG variation. Adv. Sci. Technol. Lett. 113, 38–43 (2015)CrossRefGoogle Scholar
  17. 17.
    Akhmadeev, K., Rampone, E., Yu, T., Aoustin, Y., Le Carpentier, E.: A testing system for a real-time gesture classification using surface EMG. IFAC-PapersOnLine 50(1), 11498–11503 (2017)CrossRefGoogle Scholar
  18. 18.
    Kutafina, E., Laukamp, D., Bettermann, R., Schroeder, U., Jonas, S.M.: Wearable sensors for eLearning of manual tasks: using forearm EMG in hand hygiene training. Sensors (Switzerland) 16(8), 1–10 (2016)CrossRefGoogle Scholar
  19. 19.
    Lobov, S.A., Krylova, N.P., Anisimova, A.P., Mironov, V.I., Kazantsev, V.B.: Optimizing the speed and accuracy of an EMG interface in practical applications. Hum. Physiol. 45(2), 145–151 (2019)CrossRefGoogle Scholar
  20. 20.
    Mane, S.M., Kambli, R.A., Kazi, F.S., Singh, N.M.: Hand motion recognition from single channel surface EMG using wavelet & artificial neural network. Procedia Comput. Sci. 49(1), 58–65 (2015)CrossRefGoogle Scholar
  21. 21.
    Karimpour, M., Parsaei, H., Sharifian, R., Rojhani, Z., Yazdani, F.: An android application for estimating muscle onset latency using surface EMG signal: In: J. Biomed. Phys. Eng. (2019)Google Scholar
  22. 22.
    Benatti, S., Casamassima, F., Milosevic, B.: A versatile embedded platform for EMG acquisition and gesture recognition, pp. 1–11 (2015)CrossRefGoogle Scholar
  23. 23.
    Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4), 427–437 (2009)CrossRefGoogle Scholar
  24. 24.
    Hossin, M., Sulaiman, M.: A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manag. Process 5(2), 1–11 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Facultad de Ingeniería y Ciencias AplicadasUniversidad de las AméricasQuitoEcuador
  2. 2.Departamento de Automatización y ControlEscuela Politécnica NacionalQuitoEcuador

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