SDAV 1.0: A Low-Cost sEMG Data Acquisition & Processing System For Rehabilitatio
Article Sidebar
Main Article Content
Abstract
Over the last two decades, myoelectric signals have been widely used in fields including rehabilitation devices and human-machine interfaces. This study aimed to develop an algorithm for surface electromyography (sEMG) data acquisition utilizing low-cost hardware and validate its performance using English vowels as silent speech content. The sEMG data were collected from the three facial muscles of one healthy subject. The sEMG signals were pre-processed, and various time-domain and statistical features were extracted in real time. The raw data and features were then used to train and test three customized machine learning classifiers: k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN). All customized classifiers achieved almost equivalent accuracy rates of 0.83 ± 0.01 in recognizing the English vowels with an improvement of 27.27% (KNN), 3.75% (SVM), and 51.85% (ANN) utilizing the same low-cost data acquisition hardware. Our findings are substantially closers to the results of commercial hardware setups, which raise the possibility of potential usage of low-cost sEMG data acquisition systems with the proposed algorithm in place of commercial hardware setups for rehabilitation devices and other related sectors of human-machine interaction.
Article Details
References
J. V. Basmajian, "Muscles alive. their functions revealed by electromyography," Academic Medicine, vol. 37, no. 8, p. 802, 1962.
C. J. De Luca, "Physiology and mathematics of myoelectric signals," IEEE Transactions on Biomedical Engineering, vol. BME-26, no. 6, pp. 313–325, 1979.
A. J. Fridlund and J. T. Cacioppo, "Guidelines for human electromyographic research," Psychophysiology, vol.23, no.5, pp. 567–589, 1986.
C. J. De Luca, "Surface electromyography: Detection and recording," DelSys Incorporated, vol. 10, no. 2, pp. 1–10, 2002.
B. Lapatki, D. Stegeman, and I. Jonas, "A surface emg electrode for the simultaneous observation of multiple facial muscles," Journal of neuroscience methods, vol. 123, no. 2, pp. 117–128, 2003.
A. Merlo, D. Farina, and R. Merletti, "A fast and reliable technique for muscle activity detection from surface emg signals," IEEE transactions on biomedical engineering, vol. 50, no. 3, pp. 316–323, 2003.
A.-D. Witman, B. Meneses-Claudio, F. Flores-Medina, P. Condori, N. I. Vargas-Cuentas, and A. Roman-Gonzalez, "Acquisition and classification system of emg signals for interpreting the alphabet of the sign language," International Journal of Advanced Computer Science and Applications, vol. 10, no. 8, 2019.
A.D. Witman, M.C. Brian, and R.G. Avid, "Electromyography signal acquisition and analysis system for finger movement classification," Electromyography, vol.10, no.6, 2019.
S. Kumar, D. K. Kumar, M. Alemu, and M. Burry, "Emg based voice recognition," in Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, 2004., pp. 593–597, 2004.
S. P. Arjunan, D. K. Kumar, W. C. Yau, and H. Weghorn, "Unspoken vowel recognition using facial electromyogram," in International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2191–2194, 2006.
S. P. Arjunan, H. Weghorn, D. K. Kumar, and W. C. Yau, "Vowel recognition of english and german language using facial movement (semg) for speech control based HCI," in Proceedings of the HCSNet workshop on Use of vision in human-computer interaction - Volume 56, pp. 13–18, 2006.
G. R. Naik, D. K. Kumar, and S. P. Arjunan, "Reliability of facial muscle activity to identify vowel utterance," in TENCON 2008- IEEE Region 10 Conference, pp. 1–6, IEEE, 2008.
G. S. Meltzner, G. Colby, Y. Deng, and J. T. Heaton, "Signal acquisition and processing techniques for semg based silent speech recognition," in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4848–4851, 2011.
E. Lopez-Larraz, O. M. Mozos, J. M. Antelis, and J. Minguez, "Syllable-based speech recognition using emg," in Annual International Conference of the IEEE Engineering in Medicine and Biology, pp.4699–4702, 2010.
U. Agnihotri, A. S. Arora and A. Gard, "Vowel recognition using facial movement (semg) for speech control based HCI," in International Journal of Engineering Research Technology ACMEE, vol. 4, no. 15, pp. 1–5, 2016.
A. P. Vyas and R. Bhadada, "Feature extraction cum frequency analysis system for facial surface electromyography signals based human speech recognition," International Journal for Research in Applied Science and Engineering Technology, vol.5, no.12, pp. 1998–2006, 2017.
R. Kachhwaha, A. P. Vyas, and R. Bhadada, "Adaptive threshold-based approach for facial muscle activity detection in silent speech emg recording," in Proceedings of 6th International Conference on Recent Trends in Computing, pp.83–98, Springer Singapore, 2021.
V. Chandrashekhar, "The classification of emg signals using machine learning for the construction of a silent speech interface," The Young Researcher, vol.5, no.1, pp.265–283, 2021.
R. E. Russo, J. G. Fern ?andez, R. R. Rivera, M. G. Kuzman, J. M. Lopez, W. A. Gemin, and M. A. Revuelta, "Algorithm of myoelectric signals processing for the control of prosthetic robotic hands," Journal of Computer Science and Technology, vol. 18, no. 1, pp. 28–34, 2018.
M. Sidik, S. Ghani, and M. M. Padzi, "Development of a wireless surface electromyography (semg) signal acquisition device for power-assisted wheelchair system," International Journal of Engineering and Advanced Technology, vol. 8, no. 6, pp. 3414–3418, 2019.
F. R. Kareem, M. H. A. E. Azeem, M. Genedy, A. Mohamed, and A. Abdeldayem, "Classification of emg signals of lower arm (forearmhand) motion patterns in different ages group," International Conference on ICT in Our Lives, pp. 89–94, 2017.
L. E. Crawford, D. T. Vavra, and J. C. Corbin, "Thinking outside the button box: Emg as a computer input device for psychological research," Collabra: Psychology, vol.3, no.1, 2017.
K. Hartman, "Getting started with myoware muscle sensor," Adafruit Industries, pp. 1–13, 11 2021.
A.Technologies, "Myoware muscle sensor kit", https://learn.sparkfun.com/tutorials/myoware
muscle-sensor-kit/all. Online accessed 13-01-23.
A.Technologies, "Myoware muscle sensor datasheet", https://cdn.sparkfun.com/datasheets/Sensors/Biometric/MyowareUserManualAT-04-001.pdf, 2015. Online accessed 13-01-23.