Estimation and Assessment of Upper Limb Movements During Exercises of Children with Musculoskeletal Disorders

  • Aleksander PalkowskiEmail author
  • Grzegorz Redlarski
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 73)


Musculoskeletal disorders can completely take away the possibility of one’s locomotion, and in most cases require intensive rehabilitation. Medical services are still one of the least automated, while in the era of increasing emphasis on personalized medicine, the only effective way to overcome most problems can be to automate the rehabilitation process. This paper presents parts of a methodological basis for an automatic expert platform assisting in the process of rehabilitation. We test four machine learning models in tasks that involve assessment of limb exercises and joint rotation estimation, solely based on electromyography signals. In the best case, the models achieved 72% of accuracy in the former, and 0.08 of mean absolute error in the later. The level of errors qualifies these models as acceptable for further development for rehabilitation systems.


biomechanics cerebral palsy classification electromyography osteogenesis imperfecta regression 


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  1. 1.
    Altman, N.S.: An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. Am. Stat. 46(3), 175–185 (1992). DOI URL
  2. 2.
    Białasiewicz, J.T.: Falki i aproksymacje, 1 edn. Wydawnictwa Naukowo-Techniczne, Warsaw, Poland (2004)Google Scholar
  3. 3.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proc. fifth Annu. Work. Comput. Learn. theory - COLT ’92, pp. 144–152. ACM Press, New York (1992). DOI URL
  4. 4.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees, 1 edn. Chapman & Hall/CRC, Boca Raton, FL (1984)Google Scholar
  5. 5.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). DOI URL
  6. 6.
    Gokgoz, E., Subasi, A.: Comparison of decision tree algorithms for EMG signal classification using DWT. Biomed. Signal Process. Control 18, 138–144 (2015). DOI URL
  7. 7.
    Grisel, O., et al.: scikit-learn/scikit-learn: 0.19.1. DOI
  8. 8.
    Hariharan, M., Fook, C.Y., Sindhu, R., Ilias, B., Yaacob, S.: A comparative study of wavelet families for classification of wrist motions. Comput. Electr. Eng. 38(6), 1798–1807 (2012). DOI URL
  9. 9.
    Hunter, J.D.: Matplotlib: A 2D Graphics Environment. Comput. Sci. Eng. 9(3), 90–95 (2007). DOI URL
  10. 10.
    Jones, E., Oliphant, T., Peterson, P., Others: SciPy: Open source scientific tools for Python (2001). URL
  11. 11.
    Lee, G., Wasilewski, F., Gommers, R., Wohlfahrt, K., O’Leary, A., Nahrstaedt, H., Contributors: PyWavelets - Wavelet Transforms in Python. URL
  12. 12.
    Levac, D., McCormick, A., Levin, M.F., Brien, M., Mills, R., Miller, E., Sveistrup, H.: Active Video Gaming for Children with Cerebral Palsy: Does a Clinic-Based Virtual Reality Component Offer an Additive Benefit? A Pilot Study. Phys. Occup. Ther. Pediatr. 38(1), 74–87 (2018). DOI URL
  13. 13.
    Machado, F.R.C., Antunes, P.P., Souza, J.D.M., Dos Santos, A.C., Levandowski, D.C., De Oliveira, A.A.: Motor Improvement Using Motion Sensing Game Devices for Cerebral Palsy Rehabilitation. J. Mot. Behav. 49(3), 273–280 (2017). DOI URL
  14. 14.
    Marsland, S.: Machine Learning: An Algorithmic Perspective, 2 edn. Chapman & Hall/CRC, Boca Raton, FL (2015)Google Scholar
  15. 15.
    McKinney, W.: Data Structures for Statistical Computing in Python. In: S. van der Walt, J. Millman (eds.) Proc. 9th Python Sci. Conf., pp. 51–56 (2010)Google Scholar
  16. 16.
    Oskoei, M.A., Hu, H.: Myoelectric control systems—A survey. Biomed. Signal Process. Control 2(4), 275–294 (2007). DOI URL
  17. 17.
    Oskoei, M.A., Hu, H.: Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb. IEEE Trans. Biomed. Eng. 55(8), 1956–1965 (2008). DOI URL
  18. 18.
    Palkowski, A.: KinectMotionCapture: A simple software for capturing human body movements using the Kinect camera. DOI URL
  19. 19.
    Pałkowski, A.: Automatyzacja procesu rehabilitacji dzieci z paralysis cerebralis infantium oraz osteogenesis imperfecta [Automation of the rehabilitation process for children with cerebral palsy and osteogenesis imperfecta]. Ph.D. thesis, Gdansk University of Technology, Gdansk, Poland (2018)Google Scholar
  20. 20.
    Palkowski, A., Redlarski, G.: Basic Hand Gestures Classification Based on Surface Electromyography. Comput. Math. Methods Med. 2016, 6481,282 (2016). DOI URL
  21. 21.
    Palkowski, A., Redlarski, G., Rzyman, G., Krawczuk, M.: Basic evaluation of limb exercises based on electromyography and classification methods. In: 2018 Int. Interdiscip. PhD Work., pp. 323–325. IEEE, Swinoujscie, Poland (2018). DOI URL
  22. 22.
    Pedregosa, F., et al.: Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011). URL
  23. 23.
    Phinyomark, A., Phukpattaranont, P., Limsakul, C.: Feature reduction and selection for EMG signal classification. Expert Syst. Appl. 39(8), 7420–7431 (2012). DOI URL
  24. 24.
    Tofallis, C.: A better measure of relative prediction accuracy for model selection and model estimation. J. Oper. Res. Soc. 66(8), 1352–1362 (2015). DOI URL
  25. 25.
    Tofallis, C.: Erratum: A better measure of relative prediction accuracy for model selection and model estimation. J. Oper. Res. Soc. 66(3), 524–524 (2015). DOI URL
  26. 26.
    van der Walt, S., Colbert, S.C., Varoquaux, G.: The NumPy Array: A Structure for Efficient Numerical Computation. Comput. Sci. Eng. 13(2), 22–30 (2011). DOI

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Electrical and Control EngineeringGdansk University of TechnologyGdanskPoland

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