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DWT-Based Hand Movement Identification of EMG Signals Using SVM

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Proceedings of International Conference on Communication and Artificial Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 192))

Abstract

Electromyogram (EMG) signals are widely used in rehabilitation, medical, engineering, robotic, and industrial fields. For amputee’s residual muscles control, EMG signals have been utilized. Multiple-level approximation and detail coefficients of EMG signals were studied here. In this study, EMG signals were extracted from 20 healthy human subjects by multilevel coefficients of wavelet. After the EMG data acquisition, preprocessing was done followed by discrete wavelet transform (DWT) de-noising and feature extraction by db4 wavelet. Three-level decomposition was achieved with DWT technique, and higher classification accuracy was observed by support vector machine (SVM) classifier with principal component analysis. This paper proposed a combination of a highly accurate algorithm for the pattern recognition of EMG signal with different movements. The finding of this paper could be utilized with real-time applications.

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References

  1. Côté-Allard U, Campbell E, Phinyomark A, Laviolette F, Gosselin B, Scheme E (2020) Interpreting deep learning features for myoelectric control: a comparison with handcrafted features. Front Bioeng Biotechnol 8:1–30

    Article  Google Scholar 

  2. Toledo-Perez DC, Rodriguez-Resendiz J, Gomez-Loenzo RA (2020) A study of computing zero crossing methods and an improved proposal for EMG signals. IEEE Access 8:8783–8790

    Article  Google Scholar 

  3. Rabin N, Kahlon M, Malayev S, Ratnovsky A (2020) Classification of human hand movements based on EMG signals using nonlinear dimensionality reduction and data fusion techniques. Expert Syst Appl 149(7):113281

    Article  Google Scholar 

  4. Tuncer T, Dogan S, Subasi A (2020) Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition. Biomed Signal Process Control 58:101872

    Article  Google Scholar 

  5. Arteaga MV, Castiblanco JC, Mondragon IF, Colorado JD, Alvarado-Rojas C (2020) EMG-driven hand model based on the classification of individual finger movements. Biomed Signal Process Control 58:101834

    Article  Google Scholar 

  6. Mukhopadhyay AK, Samui S (2020) An experimental study on upper limb position invariant EMG signal classification based on deep neural network. Biomed Signal Process Control 55(5):1–8

    Google Scholar 

  7. Narayan Y, Ahlawat V, Kumar S (2020) Pattern recognition of sEMG signals using DWT based feature and SVM Classifier. Int J Adv Sci Technol 29(10):2243–2256

    Google Scholar 

  8. Narayan Y, Kumar D, Kumar S (2020) Comparative analysis of sEMG signal classification using different K-NN algorithms. Int J Adv Sci Technol 29(10):2257–2266

    Google Scholar 

  9. Ahlawat V, Thakur R, Narayan Y (2018) Support vector machine based classification improvement for EMG signals using principle component analysis. J Eng Appl Sci 13(8):6341–6345

    Google Scholar 

  10. Narayan Y, Mathew L, Chatterji S (2017) sEMG signal classification using discrete wavelet transform and decision tree classifier. Int J Control Theory Appl 10(6):511–517

    Google Scholar 

  11. Kumari P, Narayan Y, Ahlawat V, Mathew L (2017) Advance approach towards elbow movement classification using discrete wavelet transform and quadratic support vector machine. In: Communication and Computing Systems © 2017 Taylor & Francis Group, London, Advance, pp 839–844. ISBN 978-1-138-02952-1

    Google Scholar 

  12. Zhang L, Shi Y, Wang W, Chu Y, Yuan X (2019) Real-time and user-independent feature classification of forearm using EMG signals. J Soc Inf Disp 27(2):101–107

    Article  Google Scholar 

  13. Turlapaty AC, Gokaraju B (2019) Feature analysis for classification of physical actions using surface EMG data. IEEE Sens J 19(24):12196–12204

    Article  Google Scholar 

  14. Simão M, Neto P, Gibaru O (2019) EMG-based online classification of gestures with recurrent neural networks. Pattern Recogn Lett 128(12):45–51

    Article  Google Scholar 

  15. Narayan Y, Singh RM, Mathew L, Chatterji S (2019) Surface EMG signal classification using ensemble algorithm, PCA and DWT for robot control. In: International conference on advanced informatics for computing research, vol 10. Springer, Singapore, pp 424–434

    Google Scholar 

  16. Virdi P, Narayan Y, Kumari P, Mathew L (2017) Discrete wavelet packet based elbow movement classification using fine Gaussian SVM. In: 1st IEEE international conference on power electronics, intelligent control and energy systems, ICPEICES 2016, pp 1–5

    Google Scholar 

  17. Narayan Y, Kumari P, Garima, Mathew L (2017) Elbow movement classification of a robotic arm using wavelet packet and cubic SVM. In: Communication and computing systems, pp 605–610

    Google Scholar 

  18. Babita PK, Narayan Y, Mathew L (2017) Binary movement classification of sEMG signal using linear SVM and wavelet packet transform. In: 1st IEEE international conference on power electronics, intelligent control and energy systems, ICPEICES 2016

    Google Scholar 

  19. Narayan Y, Mathew L, Chatterji S (2018) SEMG signal classification with novel feature extraction using different machine learning approaches. J Intell Fuzzy Syst 35(5):5099–5109

    Article  Google Scholar 

  20. Sui X, Wan K, Zhang Y (2019) Pattern recognition of SEMG based on wavelet packet transform and improved SVM. Optik (Stuttg) 176:228–235

    Article  Google Scholar 

  21. Li C, Li G, Jiang G, Chen D, Liu H (2018) Surface EMG data aggregation processing for intelligent prosthetic action recognition. Neural Comput Appl 0123456789(8):1–12

    Google Scholar 

  22. Waris A et al (2018) The effect of time on EMG classification of hand motions in able-bodied and transradial amputees. J Electromyogr Kinesiol 40(4):72–80

    Article  Google Scholar 

  23. Garg C, Narayan Y, Mathew L (2015) Development of a software module for feature extraction and classification of EMG signals. In: 2015 communication, control and intelligent systems (CCIS), vol 1, pp 250–254

    Google Scholar 

  24. Oskoei MA, Hu HHH (2008) Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans Biomed Eng 55(8):1956–1965

    Google Scholar 

  25. Phinyomark A, Nuidod A, Phukpattaranont P, Limsakul C (2012) Feature extraction and reduction of wavelet transform coefficients for EMG pattern classification. Elektron Ir Elektrotechnika 122(6):27–32

    Article  Google Scholar 

  26. Phinyomark A, Limsakul C, Phukpattaranont P (2011) Application of wavelet analysis in EMG feature extraction for pattern classification. Meas Sci Rev 11(2):45–52

    Article  Google Scholar 

  27. Samuel OW et al (2017) Pattern recognition of electromyography signals based on novel time domain features for amputees ’ limb motion. Comput Electr Eng 67(10):646–655

    Google Scholar 

  28. Subasi A (2012) Classification of EMG signals using combined features and soft computing techniques. Appl Soft Comput J 12(8):2188–2198

    Article  Google Scholar 

  29. Phinyomark A, Phukpattaranont P, Limsakul C (2012) The usefulness of wavelet transform to reduce noise in the SEMG signal. EMG Methods Eval Muscle Nerve Func, no. February 2016, pp 107–132

    Google Scholar 

  30. Hortal E et al (2015) SVM-based brain-machine interface for controlling a robot arm through four mental tasks. Neurocomputing 151(10):116–121

    Article  Google Scholar 

  31. Subasi A (2013) Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput Biol Med 43(5):576–586

    Article  Google Scholar 

  32. Alomari F, Liu G (2015) Novel hybrid soft computing pattern recognition system SVM-GAPSO for classification of eight different hand motions. Optik (Stuttg) 126(23):4757–4762

    Article  Google Scholar 

  33. Alkan A, Günay M (2012) Identification of EMG signals using discriminant analysis and SVM classifier. Expert Syst Appl 39(1):44–47

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of EE, Sri Ram Group of Colleges, Muzaffarnagar, Uttar Pradesh, India

    Vivek Ahlawat

  2. Department of ECE, Chandigarh University, Chandigarh, Punjab, India

    Yogendra Narayan

  3. Department of ECE, GLA University, Mathura, Uttar Pradesh, India

    Divesh Kumar

Authors
  1. Vivek Ahlawat
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  2. Yogendra Narayan
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  3. Divesh Kumar
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Corresponding author

Correspondence to Divesh Kumar .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, GLA University, Mathura, India

    Vishal Goyal

  2. Department of Electronics and Communication Engineering, GLA University, Mathura, India

    Manish Gupta

  3. Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior, Madhya Pradesh, India

    Aditya Trivedi

  4. Faculty of Engineering and Science, University of Agder, Kristiansand, Norway

    Mohan L. Kolhe

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Ahlawat, V., Narayan, Y., Kumar, D. (2021). DWT-Based Hand Movement Identification of EMG Signals Using SVM. In: Goyal, V., Gupta, M., Trivedi, A., Kolhe, M.L. (eds) Proceedings of International Conference on Communication and Artificial Intelligence. Lecture Notes in Networks and Systems, vol 192. Springer, Singapore. https://doi.org/10.1007/978-981-33-6546-9_47

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  • DOI: https://doi.org/10.1007/978-981-33-6546-9_47

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-6545-2

  • Online ISBN: 978-981-33-6546-9

  • eBook Packages: EngineeringEngineering (R0)

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