Abstract
Nature blessed us with one of its best gifts, the human voice, which is an essential tool for vocal communication with others. During silent speech generation, few facial muscles play a vital role. An electromyographic signal is generated around these muscles, which can be captured, processed, and used as communication. The goal of this paper is to automate the signal files reading and the preprocessing module for surface EMG speech signal, which is captured from the facial muscles and saved on disk by LabVIEW software in.tdms file format. An adaptive threshold-based approach is used for detecting facial muscle activity during the silent speech, across random interval recordings in multiple files, where each file has two-channel EMG recordings. With our approach, it is noticed that zygomaticus major muscles show less activity as compared to levator anguli oris muscle for silent speech communication purposes.
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
Similar content being viewed by others
References
Fridlund AJ, Cacioppo JT (1986) Guidelines for human electromyographic research. Psychophysiology 23(5):567–589
Vyas AP, Bhadada R (2017) Feature extraction cum frequency analysis system for facial surface electromyography signals based human speech recognition. Int J Res Appl Sci Eng Technol 5(12):1998–2006
Geryes M, Charara J, Skaiky A, Mcheick A, Girault J (2017) A novel biomedical application for the Myo gesture control armband. In: 29th International conference on microelectronics (ICM), Beirut, pp 1–4
Špulák D, Čmejla R, Bačáková R, Kračmar B, Satrapová L, Novotný P (2014) Muscle activity detection in electromyograms recorded during periodic movements. Comput Biol Med 47(1):93–103
Chowdhury R, Reaz M, Ali M, Bakar A, Chellappan K, Chang T (2013) Surface electromyography signal processing and classification techniques. Sensors 13(9):12431–12466
Wu J et al (2019) sEMG signal processing methods: a review. J Phys Conf Ser 1237:032008
Rasoo G, Iqbal K (2012) Muscle activity onset detection using energy detectors. In: Annual international conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, pp 3094–3097
Caycedo AU, Hurtado DA, Sandoval OLR (2017) Electromyographic analysis for silent speech detection. ARPN J Eng Appl Sci 12(1):107–114
Adam Reeve T (2019) npTDMS Documentation Release 0.17.1
Python Homepage. https://www.python.org/. Last accessed 23 Jan 2020
The NI TDMS File Format. https://www.ni.com/product-documentation/3727/en/\#toc7. Last accessed 23 Jan 2020
npTDMS. https://pypi.org/project/npTDMS/. Last accessed 23 Jan 2020
npTDMS. https://nptdms.readthedocs.io/en/latest/. Last accessed 23 Jan 2020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kachhwaha, R., Vyas, A.P., Bhadada, R. (2021). Adaptive Threshold-Based Approach for Facial Muscle Activity Detection in Silent Speech EMG Recording. In: Mahapatra, R.P., Panigrahi, B.K., Kaushik, B.K., Roy, S. (eds) Proceedings of 6th International Conference on Recent Trends in Computing. Lecture Notes in Networks and Systems, vol 177. Springer, Singapore. https://doi.org/10.1007/978-981-33-4501-0_9
Download citation
DOI: https://doi.org/10.1007/978-981-33-4501-0_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4500-3
Online ISBN: 978-981-33-4501-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)