Skip to main content
SpringerLink
Log in

sEMG-based deep learning framework for the automatic detection of knee abnormality

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Knee abnormality is a vital issue that can be diagnosed utilizing a sEMG signal to detect muscle abnormalities. Manually analyzing EMG data is time-consuming and requires skilled doctors. Hence, this paper aims to provide an automated system for the diagnosis of knee abnormality. Here, sEMG signal acquired from four different lower limbs muscles of 22 volunteers with three activities: walking, sitting, and standing, of which 11 seem healthy, and the rest were diagnosed clinically with knee abnormality. Noises are present during the sEMG signal recording, so a multi-step classification approach is proposed here. At first, wavelet denoising was implemented to denoise the sEMG signals. Further, the overlapping windowing method with a window size of 256 ms along with an overlapping of 25% was utilized to minimize the computational complexity. Afterward, a hybrid convolutional neural network with long short-term memory (Conv-LSTM) model is used for screening abnormal subjects. In this hybrid approach, a convolutional neural network (CNN) is used for temporal learning, while long short-term memory (LSTM) is for sequence learning. The results exhibit that the proposed wavelet-based denoising followed by Conv-LSTM model is the most precise and convenient model used for the detection of knee abnormality using sEMG signals so far.

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
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Arthritis Foundation. Arthritis By The Numbers. https://www.arthritis.org/getmedia/e1256607-fa87-4593-aa8a-8db4f291072a/2019-abtn-final-march-2019.pdf (2019)

  2. Richebé, P., Capdevila, X., Rivat, C.: Persistent postsurgical pain: pathophysiology and preventative pharmacologic considerations. Anesthesiology 129(3), 590–607 (2018)

    Article  Google Scholar 

  3. Bedson, J., Jordan, K., Croft, P.: How do gps use x rays to manage chronic knee pain in the elderly? a case study. Ann. Rheum. Dis. 62(5), 450–454 (2003)

    Article  Google Scholar 

  4. Hussain, T., Maqbool, H.F., Iqbal, N., Salman, M.K., Dehghani-Sanij, A.A.: Computational model for the recognition of lower limb movement using wearable gyroscope sensor. Int J Sensor Netw 30(1), 35–45 (2019)

    Article  Google Scholar 

  5. Merletti, R., De Luca, C.J.: New techniques in surface electromyography. Comput. Aided Electromyogr. Expert Syst. 9(3), 115–124 (1989)

    Google Scholar 

  6. Vijayvargiya, A., Singh, P.L., Verma, S.M., Kumar, R., Bansal, S.: Performance comparison analysis of different classifier for early detection of knee osteoarthritis. In: Sensors for Health Monitoring. Elsevier (2019)

  7. Vijayvargiya, A., Kumar, R., Dey, N., Manuel, J., Tavares, R.S. Comparative analysis of machine learning techniques for the classification of knee abnormality. In: IEEE 5th International Conference on Computing Communication and Automation (ICCCA). IEEE (2020)

  8. Dhanka, B., Vijayvargiya, A., Kumar, R., Ghanshyam, S.: A comparative assessment of machine learning techniques for epilepsy detection using eeg signal. In: IEEE 7th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE (2020)

  9. Da Silva, U.S.L.G., Villagra, H.A., Oliva, L.L., Marconi, N.F.: Emg activity of upper limb on spinal cord injury individuals during whole-body vibration. Physiol. Int. (Acta Physiologica Hungarica) 103(3), 361–367 (2016)

    Google Scholar 

  10. Chen, J., Zhang, X., Cheng, Y., Xi, N.: Surface emg based continuous estimation of human lower limb joint angles by using deep belief networks. Biomed. Signal Process. Control 40, 335–342 (2018)

    Article  Google Scholar 

  11. Varol, H.A., Sup, F., Goldfarb, M.: Multiclass real-time intent recognition of a powered lower limb prosthesis. IEEE Trans. Biomed. Eng. 57(3), 542–551 (2009)

    Article  Google Scholar 

  12. Choi, H.K., Jeong, J.H., Hwang, S.H., Choi, H.C. and Hak C.W.: Feature evaluation and pattern recognition of lower limb muscle emg during postural balance control. In: Key Engineering Materials, vol. 326, pp. 867–870. Trans Tech Publ (2006)

  13. Vijayvargiya, A., Kumar, R., Dey, N., Tavares, J.M.R.S.: Comparative analysis of machine learning techniques for the classification of knee abnormality. In: IEEE 5th International Conference on Computing Communication and Automation (ICCCA). IEEE (2020)

  14. Vijayvargiya, A., Prakash, C., Kumar, R., Bansal, S., Tavares, J.M.R.S.: Human knee abnormality detection from imbalanced sEMG data. Biomed. Signal Process. Control 66, 102406 (2021)

    Article  Google Scholar 

  15. Ertuğrul, Ö.F., Kaya, Y., Tekin, R.: A novel approach for semg signal classification with adaptive local binary patterns. Med. Biol. Eng. Comput. 54(7), 1137–1146 (2016)

    Article  Google Scholar 

  16. Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100, 270–278 (2018)

    Article  Google Scholar 

  17. Sanchez, O.F.A., Sotelo, J.L.R., Gonzales, M.H., Hernandez, G.A.M.: Emg dataset in lower limb data set. UCI Mach. Learn. Repos. 2 (2014)

  18. Lichman, M. : UCI Machine Learning Repository, School Inf. Comput. Sci., Univ. California, Irvine, CA, USA, Tech. Rep., 2013. [Online]. Available: http://archive.ics.uci.edu/ml

  19. Chowdhury, R.H., Reaz, M.B., Ali, M.A., Bakar, A.A., Chellappan, K., Chang, T.G.: Surface electromyography signal processing and classification techniques. Sensors 13(9), 12431–12466 (2013)

  20. Jiang, C.-F., Kuo, S.-L.: A comparative study of wavelet denoising of surface electromyographic signals. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE (2007)

  21. Phinyomark, A., Phukpattaranont, P., Limsakul, C.: Wavelet-based denoising algorithm for robust EMG pattern recognition. Fluct. Noise Lett. 10(02), 157–167 (2011)

    Article  Google Scholar 

  22. Graps, A.: An introduction to wavelets. IEEE Comput. Sci. Eng. 2(2), 50–61 (1995)

    Article  Google Scholar 

  23. He, C., Xing, J., Li, J., Yang, Q., Wang, R.: A new wavelet threshold determination method considering interscale correlation in signal denoising. Math. Probl. Eng. (2015)

  24. Banos, O., Galvez, J.-M., Damas, M., Pomares, H., Rojas, I.: Window size impact in human activity recognition. Sensors 14(4), 6474–6499 (2014)

    Article  Google Scholar 

  25. Naik, G.R., Selvan, S.E., Arjunan, S.P., Acharyya, A., Kumar, D.K., Ramanujam, A., Nguyen, H.T.: An ICA-EBM-based sEMG classifier for recognizing lower limb movements in individuals with and without knee pathology. IEEE Trans. Neural Syst. Rehabili Eng. 26(3), 675–686 (2018)

    Article  Google Scholar 

  26. Kiranyaz, S., Ince, T., Gabbouj, M.: Real-time patient-specific ECG classification by 1-d convolutional neural networks. IEEE Trans. Biomed. Eng. 63(3), 664–675 (2015)

    Article  Google Scholar 

  27. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  28. Kingma, D.P., Adam, J.B.: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

Download references

Acknowledgements

This publication is supported by Visvesvaraya PhD Scheme, Meity, Govt. of India, MEITY-PHD-2942.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, India

    Ankit Vijayvargiya, Bharat Singh & Rajesh Kumar

  2. Department of Electrical Engineering, Swami Keshvanand Institute of Technology, Jaipur, India

    Ankit Vijayvargiya & Nidhi Kumari

Authors
  1. Ankit Vijayvargiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bharat Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nidhi Kumari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rajesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ankit Vijayvargiya.

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

Vijayvargiya, A., Singh, B., Kumari, N. et al. sEMG-based deep learning framework for the automatic detection of knee abnormality. SIViP 17, 1087–1095 (2023). https://doi.org/10.1007/s11760-022-02315-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02315-y

Keywords

Search

Navigation