Skip to main content
SpringerLink
Log in

An orbicularis oris, buccinator, zygomaticus, and risorius muscle contraction classification for lip-reading during speech using sEMG signals on multi-channels

  • Published:
International Journal of Speech Technology Aims and scope Submit manuscript

Abstract

The quantity and placement of surface electromyography cathodes have been concentrated broadly because of the need to improve the exactness of the order they complete of the expectation of development. By and by, expanding the quantity of channels utilized for this characterization frequently builds their preparing time too. This exploration work pays with a correlation of the grouping exactness dependent on the diverse quantity of surface electromyography signal put in the correct lower appendage of solid subjects (Human Beings). The examination is performed utilizing Zero Crossings (ZC), Mean Absolute Values (MAV), Slope Signal (SS), and Waveform Length (WL); these qualities contain the component vector. The calculation utilized for the arrangement is the SVM subsequent to applying a PCA to the highlights. The outcomes demonstrate that it is conceivable to arrive at over 95% of order precision by utilizing 11 or 10 channels. Also, the distinction got for 1500 tests-sample for 9, 10 and 11 channels, isn't higher than 4%, which implies that expanding the quantity of channels doesn't ensure 99.5% accuracy in the order. Therefore, these classification results the better understanding of Orbicularis Oris (OO), Buccinator (B), Zygomaticus (Z), and Risorius (R) Muscle contraction during speech of deaf and dumb patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Afsharipour, B., Soedirdjo, S., & Merletti, R. (2019). Two-dimensional surface EMG: The effects of electrode size, interelectrode distance and image truncation. Biomedical Signal Processing and Control, 49, 298–307.

    Article  Google Scholar 

  • Al-Ani, A., Koprinska, I., Naik, G. R., & Khushaba, R. N. (2016). A dynamic channel selection algorithm for the classification of EEG and EMG data. In 2016 International Joint Conference on Neural Networks (IJCNN), 4076–4081. https://doi.org/https://doi.org/10.1109/IJCNN.2016.7727730

  • Alizadeh, J., Vahid, A., & Bahrami, F. (2016, November). Recognizing subjects who are learned how to write with foot from unlearned subjects using EMG signals. In 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME),331–335. https://doi.org/10.1109/ICBME.2016.7890982

  • Al-Timemy, A. H., Bugmann, G., Outram, N., & Escudero, J. (2011). Single channel-based myoelectric control of hand movements with Empirical Mode Decomposition. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 6059–6062. https://doi.org/10.1109/IEMBS.2011.6091497

  • Ceseracciu, E., Reggiani, M., Sawacha, Z., Sartori, M., Spolaor, F., Cobelli, C., & Pagello, E. (2010, September). SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics. In 19th International Symposium in Robot and Human Interactive Communication, 165–170. https://doi.org/10.1109/ROMAN.2010.5598664

  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

    MATH  Google Scholar 

  • Deny, J., & Sudharsan, R. R. (2020). Block Rearrangements and TSVs for a Standard Cell 3D IC Placement. Intelligent Computing and Innovation on Data Science (pp. 207–214). Singapore: Springer.

    Chapter  Google Scholar 

  • Dunteman, G. H. (1989). Principal components analysis (No. 69). Sage.

  • Englehart, K., & Hudgins, B. (2003). A robust, real-time control scheme for multifunction myoelectric control. IEEE transactions on biomedical engineering, 50(7), 848–854.

    Article  Google Scholar 

  • Krishna, R. R., Kumar, P. S., & Sudharsan, R. R. (2017). Optimization of wire-length and block rearrangements for a modern IC placement using evolutionary techniques. In 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), 1–4. https://doi.org/10.1109/ITCOSP.2017.8303081

  • Oskoei, M. A., & Hu, H. (2006). GA-based feature subset selection for myoelectric classification. In 2006 IEEE International Conference on Robotics and Biomimetics, 1465–1470. https://doi.org/10.1109/ROBIO.2006.340145

  • Purushothaman, G., & Vikas, R. (2018). Identification of a feature selection-based pattern recognition scheme for finger movement recognition from multichannel EMG signals. Australasian Physical & Engineering Sciences in Medicine, 41(2), 549–559.

    Article  Google Scholar 

  • Raj, S., & Ray, K. C. (2017). ECG signal analysis using DCT-based DOST and PSO optimized SVM. IEEE Transactions on instrumentation and measurement, 66(3), 470–478.

    Article  Google Scholar 

  • She, Q., Luo, Z., Meng, M., & Xu, P. (2010). Multiple kernel learning SVM-based EMG pattern classification for lower limb control. In 2010 11th International Conference on Control Automation Robotics & Vision, pp. 2109–2113. https://doi.org/10.1109/ICARCV.2010.5707406

  • Sudharsan, R. R. (2019). Synthesis of FIR Filter using ADC-DAC: A FPGA Implementation. In 2019 IEEE International Conference on Clean Energy and Energy Efficient Electronics Circuit for Sustainable Development (INCCES), 1–3. https://doi.org/10.1109/INCCES47820.2019.9167696

  • Sudharsan, R. R., & Deny, J. (2020). Field Programmable Gate Array (FPGA)-Based Fast and Low-Pass Finite Impulse Response (FIR) Filter. Intelligent Computing and Innovation on Data Science (pp. 199–206). Singapore: Springer.

    Chapter  Google Scholar 

  • Sudharsan, R. R., Deny, J., Kumaran, E. M., & Geege, A. S. (2020a). An Analysis of Different Biopotential Electrodes Used for Electromyography. Journal of Nano- and Electronic Physics, 12(1), 1–7.

    Article  Google Scholar 

  • Sudharsan, R. R., Deny, J., Muthukumaran, E., & Selvi, S. C. (2020b). Design, implementation, and estimation of MFCV for 4-different position of human body using FPGA. Microelectronics Journal, 105, 104890.

    Article  Google Scholar 

  • Sudharsan, R. R., Deny, J., Muthukumaran, E., & Varatharajan, R. (2020). FPGA based peripheral myopathy monitoring using MFCV at dynamic contractions. Journal of Ambient Intelligence and Humanized Computing, 1–9.

  • Vuskovic, M., & Du, S. (2002). Classification of prehensile EMG patterns with simplified fuzzy ARTMAP networks. In Proceedings of the 2002 International Joint Conference on Neural Networks, IJCNN'02 (Cat. No. 02CH37290, 3, 2539–2544. https://doi.org/10.1109/IJCNN.2002.1007543

  • Yang, M., Zheng, H., Wang, H., & McClean, S. (2009, April). Feature selection and construction for the discrimination of neurodegenerative diseases based on gait analysis. In 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare, 1–7. https://doi.org/10.4108/ICST.PERVASIVEHEALTH2009.6053

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnan Koil, Virudhunagar (Dt), 626 126, India

    J. Deny & R. Raja Sudharsan

  2. Dr. B.R, Ambedkar Institute of Technology, Port Blair, Andaman and Nicobar Islands, India

    E. Muthu Kumaran

Authors
  1. J. Deny
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Raja Sudharsan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. E. Muthu Kumaran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to J. Deny or R. Raja Sudharsan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deny, J., Raja Sudharsan, R. & Muthu Kumaran, E. An orbicularis oris, buccinator, zygomaticus, and risorius muscle contraction classification for lip-reading during speech using sEMG signals on multi-channels. Int J Speech Technol 24, 593–600 (2021). https://doi.org/10.1007/s10772-021-09816-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10772-021-09816-0

Keywords

Search

Navigation