Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification

Year 2023, Volume: 27 Issue: 1, 214 - 225, 28.02.2023

Harun Güneş , Abdullah Erhan Akkaya

https://doi.org/10.16984/saufenbilder.1176459

Abstract

In this study; time series electromyography (EMG) data have been classified according to hand movements using wavelet analysis and deep learning. A pre-trained deep CNN (Convolitonal Neural Network-GoogLeNet) has been used in the classification process performed with signal processing, by this way the results can be obtained by continuous wavelet transform and classification methods. The dataset used has been taken from the Machine Learning Repository at the University of California. In the data set; EMG data of 5 healthy individuals, 2 males and 3 females, of the same age (~20-22 years) are available. Data; It consists of grasping spherical objects (Spher), grasping small objects with fingertips (Tip), grasping objects with palms (Palm), grasping thin/flat objects (Lat), grasping cylindrical objects (Cyl) and holding heavy objects (Hook). It is desired to perform 6 hand movements at the same time. While these movements are necessary, speed and power depend on one's will. People perform each movement for 6 seconds and repeat each movement (action) 30 times. The CWT (Continuous Wavelet Transform) method was used to transform the signal into an image. The scalogram image of the signal was created using the CWT method and the generated images were collected in a data set folder. The collected scalogram images have been classified using GoogLeNet, a deep learning network model. With GoogLeNet, results with 97.22% and 88.89% accuracy rates were obtained by classifying the scalogram images of the signals received separately from channel 1 and channel 2 in the data set. The applied model can be used to classify EMG signals in EMG data with high success rate. In this study, 80% of data was used for educational purposes and 20% for validation purposes. In the study, the results of the classification processes have been evaluated separately for first and second channel data.

Keywords

Deep learning, continuous wavelet transform (CWT), skalogram, electromyography (EMG), GoogLeNet

Project Number

1

References

• [1] E. Kaniusas, “Fundamentals of Biosignals,“ Springer, Berlin, Heidelberg, 2012, pp. 1-26.
• [2] K. Andrianesis, A. Tzes, “Design of an anthropomorphic prosthetic hand driven by shape memory alloy actuators,” 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, IEEE, 2008, pp. 517-522.
• [3] Y. Zeng, J. Yang, C. Peng, Y. Yin, “Evolving Gaussian process autoregression based learning of human motion intent using improved energy kernel method of EMG,” IEEE Transactions on Biomedical Engineering, vol. 66(9), pp. 2556-2565, 2019.
• [4] X. Zhang, X. Chen, Y. Li, V. Lantz, K. Wang, J. Yang,“A framework for hand gesture recognition based on accelerometer and EMG sensors,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 41(6), pp. 1064-1076, 2011.
• [5] S. Raurale, J. McAllister, J. M. del Rincon, “Emg wrist-hand motion recognition system for real-time embedded platform,” In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2019, pp. 1523-1527.
• [6] F. V. Tenore, A. Ramos, A. Fahmy, S. Acharya, R. Etienne-Cummings, N. V. Thakor, “Decoding of individuated finger movements using surface electromyography,” IEEE transactions on biomedical engineering, IEEE, vol 56(5), pp.1427-1434, 2008.
• [7] R. N. Khushaba, S. Kodagoda, D. Liu, G. Dissanayake, “Muscle computer interfaces for driver distraction reduction,” Computer methods and programs in biomedicine, Elsevier, vol 110(2), pp. 137-149, 2013.
• [8] A. Phinyomark, R. N. Khushaba, E. Scheme, “Feature extraction and selection for myoelectric control based on wearable EMG sensors,” Sensors, MDPI, vol 18(5), pp. 1615, 2018.
• [9] H. Kataoka, K. Sugie, “Recent advancements in lateral trunk flexion in Parkinson disease,” Neurology: Clinical Practice, AAN Enterprises, vol 9(1), pp. 74-82, 2019.
• [10] F. H. Chan, Y. S. Yang, F. K. Lam, Y. T. Zhang, P. A. Parker, “Fuzzy EMG classification for prosthesis control,” IEEE transactions on rehabilitation engineering, IEEE, vol 8(3), pp. 305-311, 2000.
• [11] M. B. I. Reaz, M. S. Hussain, F. Mohd-Yasin, “Techniques of EMG signal analysis: detection, processing, classification and applications,” Biological procedures online, Springer, vol. 8(1), pp. 11-35, 2006.
• [12] A. Phinyomark, P. Phukpattaranont, C. Limsakul, “Feature reduction and selection for EMG signal classification,” Expert systems with applications, Elsevier, vol 39(8), pp. 7420-7431, 2012.
• [13] V. C. Dionisio, G. L. Almeida, M. Duarte, “Kinematic, Kinetic and EMG Patterns During Downward Squatting, ” Journal of Electromyography and Kinesiology, Elsevier, vol. 18(1), pp. 134-143, 2008.
• [14] M. A. Oskoei, H. Hu, "Myoelectric control systems-A survey," Biomedical Signal Processing and Control, Elsevier, vol. 2(4), pp.275-294, 2007.
• [15] C. Sapsanis, G. Georgoulas, A. Tzes, D. Lymberopoulos, “Improving EMG based classification of basic hand movements using EMD,” In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2013, pp. 5754-5757.
• [16] E. F. Delagi, Anatomical guide for the electromyographer: the limbs and trunk, C. C. Thomas, 2011.
• [17] T. S. Saponas, D. S. Tan, D. Morris, R. Balakrishnan, J. Turner, J. A. Landay, “Enabling always-available input with muscle-computer interfaces,” In Proceedings of the 22nd annual ACM symposium on User interface software and technology, 2009, pp. 167-176.
• [18] D. S. Saponas, D. Tan, R. Morris, R. Balakrishnan, J. Turner, J. A. Landay, “Enabling Always-Available Input with Muscle-Computer Interfaces,” Procedings of the 22nd annual ACM symposium on User interface software and technology, Association for Computing Machinery, New York, USA, 2009, pp 167-176.
• [19] A. M. Alaql, “Analysis and processing of human electroretinogram,” M.S. thesis, Science in Electrical Engineering Department, University of South Florida, Tampa, FL, USA, 2016
• [20] E. Kılıç, A. Erdmar, “Automatic classification of respiratory sounds during sleep,” 2018 26th Signal Processing and Communications Applications Conference (SIU), IEEE, Çeşme, İzmir, Türkiye, 2018, pp. 1-4.
• [21] A. Erdamar, “Uyku apnesinin öngörülmesi ve dil kasının uyarılması için model geliştirilmesi,” Doktora tezi, Fen Bilimleri Enstitüsü, Hacettepe Üniversitesi, Ankara, Türkiye, 2011.
• [22] B. K. Karaca, B. Oltu, T. Kantar, E. Kılıç, M. F. Akşahin, A. Erdamar, “Classication of heart sound recordings with continuous wavelet transform based algorithm,” 2018 26th Signal Processing and Communications Applications Conference (SIU), Çeşme, İzmir, Türkiye, 2018, pp. 1-4.
• [23] M. X. Cohen, “A better way to define and describe Morlet wavelets for time frequency analysis,” NeuroImage, Elsevier, vol. 199, pp. 81-86, 2019.
• [24] P. S. Addison, The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance, CRC press, 2017.
• [25] M. Lin, N. Li, “Scale-free network provides an optimal pattern for knowledge transfer,” Physica A: Statistical Mechanics and its Applications, Elsevier, vol.389(3), pp. 473-480, 2010.
• [26 ]Matlab [Online] Available:https:www.mathworks.com/products/matlab.html.