Abstract
The information about intended hand gestures can be extracted by processing surface electromyography signals using non-invasive commercial off the shelf surface electromyography data acquisition devices. Surface electromyography signals have a great potential for use in multi-functional prosthetic controllers. The objective of this study is the implementation of a classifier that can be used to classify gestures from Myo-electric data obtained from the Myo-armband. This study describes in detail a method for data acquisition, feature extraction, and offline gesture classification using Artificial Neural Network. The performance is then compared with a Support Vector Machine Classifier. The proposed approach results in an accuracy greater than 94% for validation data set for classification of five distinct hand gestures. It could be concluded that this technique could be used in the human-machine interfaces with five distinct control signals including rest. A significant observation in this study was that a single artificial neural network taking inputs from all sensors simultaneously gives inferences with better accuracy compared to a system with a separate neural network for each sensor with a majority voting to decide the classification of the gesture.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Englehart, K., Hudgins, B., Parker, P.A., Stevenson, M.: Classification of the myoelectric signal using time-frequency based representations. Med. Eng. Phys. 21(6–7), 431–438 (1999)
Englehart, K., Hudgins, B., Parker, P.A.: Time frequency based classification of the myoelectric signal: static vs. dynamic contractions. In: Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143), Chicago, IL, USA, vol. 1, pp. 317–320. IEEE (2000)
Englehart, K., Hudgins, B., Parker, P.A.: A wavelet based continuous classification scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 48(3), 302–311 (2001)
Englehart, K., Hudgins, B.: A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 50(7), 848–854 (2003)
Hudgins, B., Parker, P.A., Scott, R.N.: A new strategy for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 40(1), 82–94 (1993)
Hargrove, L.J., Englehart, K., Hudgins, B.: The effect of electrode displacements on pattern recognition based myoelectric control. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York, NY, USA, pp. 2203–2206. IEEE (2006)
Hargrove, L.J., Englehart, K., Hudgins, B.: A comparison of surface and intramuscular myoelectric signal classification. IEEE Trans. Biomed. Eng. 54(5), 847–853 (2007)
Oskoei, M.A., Hu, H.: Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans. Biomed. Eng. 55(8), 1956–1965 (2008)
Junez, G.P., Terriza, J.H.: Hand gesture recognition based on sEMG signals using Support Vector Machines. In: 2016 IEEE 6th International Conference on Consumer Electronics-Berlin (ICCE-Berlin), Berlin, Germany, pp. 174–178. IEEE (2016)
Sueaseenak, D., Khawdee, C., Pakornsirikul, N. Sukjamsri, C.: A performance of modern gesture control device with application in pattern classification. In: 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), Nagoya, Japan, pp. 428–431. IEEE (2017)
Mendez, I., Hansen, B.W., Grabow, C.M., Smedegaard, E.J.L., Skogberg, N.B., Uth, X.J., Bruhn, A., Geng, B., Kamavuako, E.N.: Evaluation of the Myo armband for the classification of hand motions. In: 2017 International Conference on Rehabilitation Robotics (ICORR). London, pp. 1211–1214. IEEE (2017)
Hargrove, L.J., Englehart, K., Hudgins, B.: A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control. Biomed. Sig. Process. Control 3(2), 175–180 (2008)
Young, A.J., Hargrove, L.J., Kuiken, T.A.: Improving myoelectric pattern recognition robustness to electrode shift by changing inter electrode distance and electrode configuration. IEEE Trans. Biomed. Eng. 59(3), 645–652 (2012)
Kaufmann, P., Englehart, K, Platzner, M.: Fluctuating EMG signals: investigating long-term effects of pattern matching algorithms. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, pp. 6357–6360. IEEE (2010)
Haris, M., Chakraborty, P., Rao, B.V.: EMG signal based finger movement recognition for prosthetic hand control. In: 2015 Communication, Control and Intelligent Systems (CCIS), Mathura, India, pp. 194–198. IEEE (2015)
Kelly, M.F., Parker, P.A., Scott, R.N.: The application of neural networks to myoelectric signal analysis: a preliminary study. IEEE Trans. Biomed. Eng. 37(3), 221–230 (1990)
Saponas, T.S., Tan, D.S., Morris, D., Turner, J., Landay, J.A.: Making muscle-computer interfaces more practical. In: Proceedings of the 28th International Conference on Human Factors in Computing Systems—CHI ’10. Atlanta, Georgia, USA, pp. 851–854. ACM Press (2010)
Tiwari, D.K., Bhateja, V., Anand, D., Srivastava, A., Omar, Z.: Combination of EEMD and morphological filtering for baseline wander correction in EMG signals. In: Proceedings of 2nd International Conference on Micro-electronics, Electromagnetics and Telecommunications. Singapore, pp. 365–373. Springer (2018)
Srivastava, A., Bhateja, V., Tiwari, D.K., Anand, D.,: AWGN suppression algorithm in EMG signals using ensemble empirical mode decomposition. In: Intelligent Computing and Information and Communication. Singapore, pp. 515–524. Springer (2018)
Mishra, A., Bhateja, V., Gupta, A., Mishra, A., Satapathy, S.C.: Feature fusion and classification of EEG/EOG signals. In: Soft Computing and Signal Processing, Singapore, pp. 793–799. Springer (2019)
Kizirian, A.: Muscles of the Forearm. https://antranik.org/muscles-of-the-forearm. Accessed 23 Jan 2019
Arief, Z., Sulistijono, I.A., Ardiansyah, R.A.: Comparison of five time series EMG features extractions using Myo Armband. In: 2015 International Electronics Symposium (IES), Surabaya, Indonesia, pp. 11–14. IEEE (2015)
Amirabdollahian, F., Walters, M.L.: Application of support vector machines in detecting hand grasp gestures using a commercially off the shelf wireless myoelectric armband. In: 2017 International Conference on Rehabilitation Robotics (ICORR), London, pp. 111–115. IEEE (2017)
Acknowledgements
The authors would like to thank Department of Electronics, Mangalore University, and Flashflow Technologies (OPC) Private Limited, for their support during the research work by providing access to a variety of journals which has been tremendously helpful in guiding this work and for all the technical infrastructure and equipment provided for establishing the experimental setup which are immensely critical for this work.
Declaration
We have taken permission from competent authorities to use the data as given in the paper. In case of any dispute in the future, we shall be wholly responsible.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Amin, P., Khan, A.M., Bhat, A.R., Rao, G. (2021). Feature Extraction and Classification of Gestures from Myo-Electric Data Using a Neural Network Classifier. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_7
Download citation
DOI: https://doi.org/10.1007/978-981-15-5788-0_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5787-3
Online ISBN: 978-981-15-5788-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)