A Human-Computer Interface based on Electromyography Command-Proportional Control

Sergey Lobov, Nadia Krilova, Innokentiy Kastalskiy, Victor Kazantsev, Valeri Makarov

Abstract

Surface electromyographic (sEMG) signals represent a superposition of the motor unit action potentials that can be recorded by electrodes placed on the skin. Here we explore the use of an easy wearable sEMG bracelet for a remote interaction with a computer by means of hand gestures. We propose a human-computer interface that allows simulating “mouse” clicks by separate gestures and provides proportional control with two degrees of freedom for flexible movement of a cursor on a computer screen. We use an artificial neural network (ANN) for processing sEMG signals and gesture recognition both for mouse clicks and gradual cursor movements. At the beginning the ANN goes through an optimized supervised learning using either rigid or fuzzy class separation. In both cases the learning is fast enough and requires neither special measurement devices nor specific knowledge from the end-user. Thus, the approach enables building of low-budget user-friendly sEMG solutions. The interface was tested on twelve healthy subjects. All of them were able to control the cursor and simulate mouse clicks. The collected data show that at the beginning users may have difficulties that are reduced with the experience and the cursor movement by hand gestures becomes smoother, similar to manipulations by a computer mouse.

References

  1. Baspinar, U., Varol, H. S., & Senyurek, V. Y. (2013). Performance comparison of artificial neural network and Gaussian mixture model in classifying hand motions by using sEMG signals. Biocybernetics and Biomedical Engineering, 33(1), 33-45.
  2. Bottomley, A. & Cowell, T. (1964). An artificial hand controlled by the nerves. New Scientist, 21, 668-71.
  3. Chowdhury, A., Ramadas, R., & Karmakar, S. (2013). Muscle computer interface: A review. In A. Chakrabarti & R. V. Prakash (Eds.), ICoRD'13, Lecture Notes in Mechanical Engineering (pp. 411- 421). Springer India.
  4. Fougner, A., Stavdahl, O., Kyberd, P. J., Losier, Y. G., & Parker, P. A. (2012). Control of upper limb prostheses: terminology and proportional myoelectric control - a review. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(5), 663-667.
  5. Hahne, J. M., Biessmann, F., Jiang, N., Rehbaum, H., Farina, D., Meinecke, F. C., Muller, K. R., & Parra, L. C. (2014). Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(2), 269-279.
  6. Jiang, N., Vest-Nielsen, J. L., Muceli, S., & Farina, D. (2012). EMG-based simultaneous and proportional estimation of wrist/hand kinematics in uni-lateral trans-radial amputees. Journal of Neuroengineering and Rehabilitation (9).
  7. Kiguchi, K. & Hayashi, Y. (2012). An EMG-based control for an upper-limb power-assist exoskeleton robot. IEEE Transactions on Systems; Man, and Cybernetics, Part B: Cybernetics, 42(4), 1064-1071.
  8. Kobrinskiy, A. E. (1960). Bioelectrical control of prosthetic devices. Herald of the Academy of Sciences USSR, 30, 58-61.
  9. Lobov, S. A., Mironov, V. I., Kastalskiy, I. A., & Kazantsev, V. B. (2015a). A spiking neural network in sEMG feature extraction. Sensors, 15(11), 27894- 27904.
  10. Lobov, S. A., Mironov, V. I., Kastalskiy, I. A., & Kazantsev, V. B. (2015b). Combined use of command-proportional control of external robotic devices based on electromyography signals. Sovremennye tehnologii v medicine [Modern Technologies in Medicine], 7(4), 30-38.
  11. Mironov, V. I., Lobov, S. A., Kastalskiy, I. A., & Kazantsev, V. B. (2015). Myoelectric control system of lower limb exoskeleton for re-training motion deficiencies. Lecture Notes in Computer Science, 9492, 428-435.
  12. MyoTM Gesture Control Armband - Wearable Technology by Thalmic Labs (2013). Thalmic Labs Inc. Retrieved May 26, 2016, from www.myo.com
  13. Peerdeman, B., Boere, D., Witteveen, H., Huis in 't Veld, R. H., Hermens, H., Stramigioli, S., Rietman, H., Veltink, P., & Misra, S. (2011). Myoelectric forearm prostheses: State of the art from a user-centered perspective. Journal of Rehabilitation Research and Development, 48(6), 719-738.
  14. Roche, A. D., Rehbaum, H., Farina, D., & Aszmann, O. C. (2014). Prosthetic myoelectric control strategies: A clinical perspective. Current Surgery Reports, 2(3), 1- 11.
  15. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985). Learning internal representations by error propagation. Parallel Distributed Processing (pp. 318-362). San Diego: La Jolla Institute for Cognitive Science, California University.
  16. Singh, R. & Chatterji, S. (2012). Trends and challenges in EMG based control scheme of exoskeleton robots - a review. International Journal of Scientific & Engineering Research, 3(8), 1-8.
  17. Wang, B., Li, Z., Ye, W., & Xie, Q. (2012). Development of human-machine interface for teleoperation of a mobile manipulator. International Journal of Control, Automation and Systems, 10(6), 1225-1231.
Download


Paper Citation


in Harvard Style

Lobov S., Krilova N., Kastalskiy I., Kazantsev V. and Makarov V. (2016). A Human-Computer Interface based on Electromyography Command-Proportional Control . In Proceedings of the 4th International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX, ISBN 978-989-758-204-2, pages 57-64. DOI: 10.5220/0006033300570064


in Bibtex Style

@conference{neurotechnix16,
author={Sergey Lobov and Nadia Krilova and Innokentiy Kastalskiy and Victor Kazantsev and Valeri Makarov},
title={A Human-Computer Interface based on Electromyography Command-Proportional Control},
booktitle={Proceedings of the 4th International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,},
year={2016},
pages={57-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006033300570064},
isbn={978-989-758-204-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,
TI - A Human-Computer Interface based on Electromyography Command-Proportional Control
SN - 978-989-758-204-2
AU - Lobov S.
AU - Krilova N.
AU - Kastalskiy I.
AU - Kazantsev V.
AU - Makarov V.
PY - 2016
SP - 57
EP - 64
DO - 10.5220/0006033300570064