Rate Evaluation of Electromyography Signals for Knee in Gait

Authors

  • Sara W. Almola Department of Medical Instrumentation,Techniques Engineering Technical Engineering College Northern Technical University Mosul, Iraq
  • Enas T. Shabkhoon Department of Medical Instrumentation, Techniques Engineering Technical Engineering College Northern Technical University Mosul, Iraq

Keywords:

EMG, Caminar, DWT, Am, DFT, SEMG, 3D

Abstract

Based on open data from Universidad Militar Nueva Granada, the shape of the electromyogram for gait (Caminar) state is investigated, and the shape leads to the use of Discrete Fourier Transform (DFT) for analysis because it is an AM homologue. Discrete Frequency The spectrum of normal muscle signal has been found based on the polyfit technique for mean values, then the bandwidth of the spectrum is estimated. As a result, even though it has no expected value, it can be described as the imprint stamp of a standard case. The spectrum of muscle signal is covered by five samples, so it can be studied by dwt while the strength of signal is divided into five levels according to normal cases. This division qualifies the strength of abnormal cases.

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References

M. B. I. Reaz, M. S. Hussain, and F. Mohd-Yasin, “Techniques of EMG signal analysis: Detection, processing, classification and applications,” Biol. Proced. Online, vol. 8, no. 1, pp. 11–35, 2006, doi: 10.1251/bpo115.

J. Wang, L. Tang, and J. E Bronlund, “Surface EMG Signal Amplification and Filtering,” Int. J. Comput. Appl., vol. 82, no. 1, pp. 15–22, 2013, doi: 10.5120/14079-2073.

R. Hippenstiel, “Introduction to Signal Processing,” Detection Theory. pp. 37–62, 2001. doi: 10.1201/9781420042047.ch3.

Z. Taghizadeh, S. Rashidi, and A. Shalbaf, “Finger movements classification based on fractional Fourier transform coefficients extracted from surface EMG signals,” Biomed. Signal Process. Control, vol. 68, no. February, p. 102573, 2021, doi: 10.1016/j.bspc.2021.102573.

N. Rabin, M. Kahlon, S. Malayev, and A. Ratnovsky, “Classification of human hand movements based on EMG signals using nonlinear dimensionality reduction and data fusion techniques,” Expert Syst. Appl., vol. 149, p. 113281, 2020, doi: 10.1016/j.eswa.2020.113281.

N. Alexander and H. Schwameder, “Effect of sloped walking on lower limb muscle forces,” Gait Posture, vol. 47, pp. 62–67, 2016, doi: 10.1016/j.gaitpost.2016.03.022.

M. Akhtaruzzaman, A. A. Shafie, and M. R. Khan, “GAIT ANALYSIS: SYSTEMS, TECHNOLOGIES, and IMPORTANCE,” J. Mech. Med. Biol., vol. 16, no. 7, 2016, doi: 10.1142/S0219519416300039.

S. J. Preece and W. Alghamdi, “The effect of increasing trunk flexion during normal walking,” Gait Posture, vol. 83, no. February 2020, pp. 250–255, 2021, doi: 10.1016/j.gaitpost.2020.10.021.

S. R. Devasahayam, “Signals and systems in biomedical engineering: Physiological systems modeling and signal processing: Third edition,” Signals and Systems in Biomedical Engineering: Physiological Systems Modeling and Signal Processing: Third Edition. pp. 1–468, 2019. doi: 10.1007/978-981-13-3531-0.

A. Del Vecchio, A. Holobar, D. Falla, F. Felici, R. M. Enoka, and D. Farina, “Tutorial: Analysis of motor unit discharge characteristics from high-density surface EMG signals,” J. Electromyogr. Kinesiol., vol. 53, p. 102426, 2020, doi: 10.1016/j.jelekin.2020.102426.

U. Frisch and D. Sornette, “Extreme Deviations and Applications,” J. Phys. II, vol. 7, no. 9, pp. 1155–1171, 1997, doi: 10.1051/jp1:1997114.

U. Ozkaya, O. Coskun, and S. Comlekci, “Frequency analysis of EMG signals with Matlab sptool,” 9th WSEAS Int. Conf. Signal Process. SIP ’10, no. May 2010, pp. 83–88, 2010.

Q. N. Warren, “Production Engineering,” Oil Gas Eng. Non-Engineers, pp. 61–67, 2022, doi: 10.1201/9781003100461-6.

Ms. Ritika Dhabalia, Ms. Kritika Dhabalia. (2012). An Intelligent Auto-Tracking Vehicle. International Journal of New Practices in Management and Engineering, 1(02), 08 - 13. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/5

Kuna, S. L. ., & Prasad, A. K. . (2023). Deep Learning Empowered Diabetic Retinopathy Detection and Classification using Retinal Fundus Images. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 117–127. https://doi.org/10.17762/ijritcc.v11i1.6058

Anand, R., Ahamad, S., Veeraiah, V., Janardan, S.K., Dhabliya, D., Sindhwani, N., Gupta, A. Optimizing 6G wireless network security for effective communication (2023) Innovative Smart Materials Used in Wireless Communication Technology, pp. 1-20.

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Published

27.10.2023

How to Cite

Almola, S. W. ., & Shabkhoon, E. T. . (2023). Rate Evaluation of Electromyography Signals for Knee in Gait. International Journal of Intelligent Systems and Applications in Engineering, 12(2s), 195–203. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3571

Issue

Vol. 12 No. 2s (2024)

Section

Research Article

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