International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

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DOI Prefix: 10.47001/IRJIET

A Comparative Study Investigating Machine Learning Methods for EMG Data Classification in Post-Stroke Rehabilitation

Rahma M. AbdulazizComputer Engineering Department, North Technical University, Mosul-IraqMohanned LoqmanComputer Engineering Department, North Technical University, Mosul-Iraq

Vol 8 No 3 (2024): Volume 8, Issue 3, March 2024 | Pages: 128-136

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 26-03-2024

doi Logo doi.org/10.47001/IRJIET/2024.803016

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Abstract

Physiotherapy is essential for boosting recovery and enhancing quality of life after a stroke. Individualized therapies are required for stroke rehabilitation. This work explores machine learning techniques for electromyography (EMG) data classification in the context of post-stroke rehabilitation, which is important for comprehending and improving motor function. Our analysis covers a wide range of methods, such as Gradient Boosting (GB), Histogram-Based Gradient Boosting, Cat Boost, K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Linear Discriminant Analysis (LDA), and LDA. We measure their performance using criteria like accuracy, precision, recall, and F1-score through a thorough evaluation procedure. Our results show that DT and RF perform consistently better than other algorithms, proving their reliability and effectiveness in the classification of EMG data. But KNN is equally promising, with relatively lesser precision than LDA. The work emphasizes how decision-based algorithms and ensemble methods may effectively classify EMG data, which has ramifications for improving stroke rehabilitation techniques. These discoveries can be used by scholars and professionals to further the creation of machine learning-based instruments for accurate gesture recognition in post-stroke rehabilitation.

Keywords

Stroke Rehabilitation, EMG Data Classification, Machine Learning, Electromyography


Citation of this Article

Rahma M. Abdulaziz, Mohanned Loqman, “A Comparative Study Investigating Machine Learning Methods for EMG Data Classification in Post-Stroke Rehabilitation” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 8, Issue 3, pp 128-136, March 2024. Article DOI https://doi.org/10.47001/IRJIET/2024.803016

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. S. Sehatzadeh, “Effect of increased intensity of physiotherapy on patient outcomes after stroke: An evidence-based analysis,” Ont. Health Technol. Assess. Ser., vol. 15, no. 6, pp. 1–42, 2015.
  2. C. Boehme, T. Toell, W. Lang, M. Knoflach, and S. Kiechl, “Longer term patient management following stroke: A systematic review,” Int. J. Stroke, vol. 16, no. 8, pp. 917–926, 2021, doi: 10.1177/17474930211016963.
  3. V. L. Feigin et al., “Global, regional, and national burden of stroke and its risk factors, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019,” Lancet Neurol., vol. 20, no. 10, pp. 1–26, 2021, doi: 10.1016/S1474-4422(21)00252-0.
  4. M. O. Owolabi et al., “The state of stroke services across the globe: Report of World Stroke Organization–World Health Organization surveys,” Int. J. Stroke, vol. 16, no. 8, pp. 889–901, 2021, doi: 10.1177/17474930211019568.
  5. P. Langhorne, O. Wu, H. Rodgers, A. Ashburn, and J. Bernhardt, “A very early rehabilitation trial after stroke (AVERT): a Phase III, multicentre, randomised controlled trial,” Health Technol. Assess. (Rockv)., vol. 21, no. 54, pp. 1–119, 2017, doi: 10.3310/hta21540.
  6. B. Hordacre et al., “Evidence for a Window of Enhanced Plasticity in the Human Motor Cortex Following Ischemic Stroke,” Neurorehabil. Neural Repair, vol. 35, no. 4, pp. 307–320, 2021, doi: 10.1177/1545968321992330.
  7. A.B. Conforto, S. L. Liew, A. R. Luft, T. Kitago, J. Bernhardt, and J. F. Arenillas, “Editorial: Understanding stroke recovery to improve outcomes: From acute care to chronic rehabilitation,” Front. Neurol., vol. 13, 2022, doi: 10.3389/fneur.2022.1021033.
  8. M. F. Levin and M. Demers, “Motor learning in neurological rehabilitation,” Disabil. Rehabil., vol. 43, no. 24, pp. 3445–3453, 2021, doi: 10.1080/09638288.2020.1752317.
  9. G. Kwakkel et al., “Standardized Measurement of Sensorimotor Recovery in Stroke Trials: Consensus-Based Core Recommendations from the Stroke Recovery and Rehabilitation Roundtable,” Neurorehabil. Neural Repair, vol. 31, no. 9, pp. 784–792, 2017, doi: 10.1177/1545968317732662.
  10. O. Adeoye et al., “Recommendations for the Establishment of Stroke Systems of Care: A 2019 Update: A Policy Statement from the American Stroke Association,” Stroke, vol. 50, no. 7, pp. e187–e210, 2019, doi: 10.1161/STR.0000000000000173.
  11. K. Scrivener, C. Dean, I. D. Cameron, and L. Ada, “Stroke in Australia: long term survivors have fallen into a black hole,” Med. J. Aust., vol. 217, no. 6, pp. 290–291, 2022, doi: 10.5694/mja2.51691.
  12. A.Pollock et al., “Physical rehabilitation approaches for the recovery of function and mobility after stroke: Major update,” Stroke, vol. 45, no. 10, p. e202, 2014, doi: 10.1161/STROKEAHA.114.006275.
  13. A.J. Ferreira, L. T. Aguiar, J. C. Martins, and C. D. C. de M. Faria, “Stroke survivors with the same levels of exercise as healthy individuals have lower levels of physical activity,” Neurol. Sci., vol. 43, no. 6, pp. 3729–3735, 2022, doi: 10.1007/s10072-021-05578-4.
  14. A.W. Alexandrov et al., “Measurement of patients’ perceptions of the quality of acute stroke services development and validation of the stroke perception report,” J. Neurosci. Nurs., vol. 51, no. 5, pp. 208–216, 2019, doi: 10.1097/JNN.0000000000000471.
  15. H. Roenn-Smidt, M. Jensen, and H. Pallesen, “Body and identity in physiotherapy after stroke,” Physiother. Theory Pract., vol. 37, no. 10, pp. 1067–1079, 2021, doi: 10.1080/09593985.2019.1681041.
  16. K. Monte-Silva, D. Piscitelli, N. Norouzi-Gheidari, M. A. P. Batalla, P. Archambault, and M. F. Levin, “Electromyogram-Related Neuromuscular Electrical Stimulation for Restoring Wrist and Hand Movement in Poststroke Hemiplegia: A Systematic Review and Meta-Analysis,” Neurorehabil. Neural Repair, vol. 33, no. 2, pp. 96–111, 2019, doi: 10.1177/1545968319826053.
  17. M. H. Lee, D. P. Siewiorek, A. Smailagic, A. Bernardino, and S. B. i Badia, “Opportunities of a Machine Learning-based Decision Support System for Stroke Rehabilitation Assessment,” no. Figure 2, 2020, [Online]. Available: http://arxiv.org/abs/2002.12261
  18. M. Gao and J. Mao, “A Novel Active Rehabilitation Model for Stroke Patients Using Electroencephalography Signals and Deep Learning Technology,” Front. Neurosci., vol. 15, no. October, pp. 1–10, 2021, doi: 10.3389/fnins.2021.780147.
  19. B. N. Zia Ur Rahman , Syed Irfan Ullah, Abdus Salam , Taj Rahman , Inayat Khan, “Retracted: Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model,” J. Healthc. Eng., vol. 2023, p. 9860360, 2022, doi: 10.1155/2023/9860360.
  20. J. C. Castiblanco, S. Ortmann, I. F. Mondragon, C. Alvarado-Rojas, M. Jöbges, and J. D. Colorado, “Myoelectric pattern recognition of hand motions for stroke rehabilitation,” Biomed. Signal Process. Control, vol. 57, 2020, doi: 10.1016/j.bspc.2019.101737.
  21. Y. Miao et al., “BCI-Based Rehabilitation on the Stroke in Sequela Stage,” Neural Plast., vol. 2020, 2020, doi: 10.1155/2020/8882764.
  22. K. Guo, “Multi-mode Stroke Rehabilitation System Using Signal-Controlled Human Machine Interface Kairui Guo,” no. January, 2021.
  23. R. R. Kumar et al., “Report on the Follow-Up To the Regional Implementation Strategy of the Madrid International Plan of Action on Ageing in Lithuania,” Front. Neurosci., vol. 14, no. 1, pp. 1–13, 2021.
  24. A.Kumar, E. Pirogova, S. S. Mahmoud, and Q. Fang, “Classification of error-related potentials evoked during stroke rehabilitation training,” J. Neural Eng., vol. 18, no. 5, 2021, doi: 10.1088/1741-2552/ac1d32.
  25. A.Kumar, Q. Fang, and E. Pirogova, “The influence of psychological and cognitive states on error-related negativity evoked during post-stroke rehabilitation movements,” Biomed. Eng. Online, vol. 20, no. 1, pp. 1–15, 2021, doi: 10.1186/s12938-021-00850-2.
  26. I.Boukhennoufa, X. Zhai, V. Utti, J. Jackson, and K. D. McDonald-Maier, “Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review,” Biomed. Signal Process. Control, vol. 71, no. PB, p. 103197, 2022, doi: 10.1016/j.bspc.2021.103197.
  27. K. Wu, B. Jelfs, K. Neville, and J. Q. Fang, “fMRI-based Static and Dynamic Functional Connectivity Analysis for Post-stroke Motor Dysfunction Patient: A Review,” 2022, [Online]. Available: http://arxiv.org/abs/2301.07171
  28. L. Guo et al., “Wearable Intelligent Machine Learning Rehabilitation Assessment for Stroke Patients Compared with Clinician Assessment,” J. Clin. Med., vol. 11, no. 24, 2022, doi: 10.3390/jcm11247467.
  29. Z. Arif Ali, Z. H. Abduljabbar, H. A. Tahir, A. Bibo Sallow, and S. M. Almufti, “eXtreme Gradient Boosting Algorithm with Machine Learning: a Review,” Acad. J. Nawroz Univ., vol. 12, no. 2, pp. 320–334, 2023, doi: 10.25007/ajnu.v12n2a1612.
  30. M. R. Machado, S. Karray, and I. T. De Sousa, “LightGBM: An effective decision tree gradient boosting method to predict customer loyalty in the finance industry,” 14th Int. Conf. Comput. Sci. Educ. ICCSE 2019, no. Nips, pp. 1111–1116, 2019, doi: 10.1109/ICCSE.2019.8845529.
  31. F. Song, D. Mei, and H. Li, Feature Selection Based on Linear Discriminant Analysis, vol. 1. 2010. doi: 10.1109/ISDEA.2010.311.
  32. T. Denœux, “A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory,” IEEE Trans. Syst. Man. Cybern., vol. 25, no. 5, pp. 804–813, 1995, doi: 10.1109/21.376493.
  33. P. Linardatos, V. Papastefanopoulos, and S. Kotsiantis, “Explainable ai: A review of machine learning interpretability methods,” Entropy, vol. 23, no. 1, pp. 1–45, 2021, doi: 10.3390/e23010018.
  34. D. Kocev, C. Vens, J. Struyf, and S. Džeroski, “Ensembles of multi-objective decision trees,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4701 LNAI, no. August, pp. 624–631, 2007, doi: 10.1007/978-3-540-74958-5_61.
  35. Y. Zhang, Z. Zhao, and J. Zheng, “CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China,” J. Hydrol., vol. 588, p. 125087, May 2020, doi: 10.1016/j.jhydrol.2020.125087.
  36. Ž. Vujović, “Classification Model Evaluation Metrics,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 6, pp. 599–606, 2021, doi: 10.14569/IJACSA.2021.0120670.
  37. I.C. Dipto, M. A. Rahman, T. Islam, and H. M. M. Rahman, “Prediction of Accident Severity Using Artificial Neural Network: A Comparison of Analytical Capabilities between Python and R,” J. Data Anal. Inf. Process., vol. 08, no. 03, pp. 134–157, 2020, doi: 10.4236/jdaip.2020.83008.

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