Advertisement

Neurophysiology

, Volume 51, Issue 2, pp 132–140 | Cite as

Evaluation of the Complexity of Control of Simple Linear Hand Movements Using Principal Component Analysis

  • A. V. GorkovenkoEmail author
  • O. V. Lehedza
  • T. I. Abramovych
  • W. Pilewska
  • V. S. Mischenko
  • M. Zasada
Article
  • 5 Downloads

In tests on 10 healthy men, we recorded EMGs related to the performance of simple (with one degree of freedom) linear movements of the right hand; straight movement trajectories lay within the parafrontal planes, and principal component analysis (PCA) was used for the evaluation of complexity of the central control of such movements. The first principal component was discriminated from EMG activity of eight muscles involved in the movements, and the magnitude of this component was associated with complexity of the central control of the performed movement. As was believed, the control of the task performance by the CNS was more complex when the size of the first principal component was smaller. It was found that the complexity of the central control was significantly smaller for the movements performed within a proximal operational zone and gradually increased for more distal trajectories. With respect to the central control, the movements provided by cyclic contraction and elongation of the extensor muscles were simpler. Hysteresis of the muscle contractions exerted ambiguous effects on the complexity of the control. It is known from skeletal and muscular anatomy that even mechanically simple movements may have different complexities at the level of the musculoskeletal system, which is taken into account by the central nervous system when performing these movements. This raises the problem of creating methods for assessing the complexity with which the CNS collides with the realization of motor tasks.

Keywords

movements of the hand EMG activity principal component analysis (PCA) synergic central control estimation of the movement complexity 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    M. Bruton and N. O’Dwyer, “Synergies in coordination: a comprehensive overview of neural, computational and behavioral approaches,” J. Neurophysiol., 120, No. 6, 2761–2774 (2018). doi: https://doi.org/10.1152/jn.00052.2018 CrossRefGoogle Scholar
  2. 2.
    M. C. Tresch, V. C. K. Cheung, and A. d’Avella, “Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets,” J. Neurophysiol., 95, No. 4, 2199–2212 (2006). doi: https://doi.org/10.1152/jn.00222.2005 CrossRefGoogle Scholar
  3. 3.
    N. Lambert-Shirzad and H. F. M. Van der Loos, “On identifying kinematic and muscle synergies: a comparison of matrix factorization methods using experimental data from the healthy population,” J. Neurophysiol., 117, No. 1, 290–302 (2016). doi: https://doi.org/10.1152/jn.00435.2016 CrossRefGoogle Scholar
  4. 4.
    A. T. Reader, “Optimal motor synergy extraction for novel actions and virtual environments,” J. Neurophysiol., 118, No. 2, 652–654 (2017). doi: https://doi.org/10.1152/jn.00165.2017 CrossRefGoogle Scholar
  5. 5.
    T. Bockemühl, N. F. Troje, and V. Dürr, “Inter-joint coupling and joint angle synergies of human catching movements,” Hum. Mov. Sci., 29, No. 1, 73–93 (2010). doi: https://doi.org/10.1016/j.humov.2009.03.003 CrossRefGoogle Scholar
  6. 6.
    J. Sandlund, D. Srinivasan, M. Heiden, and S. E. Ma-thiassen, “Differences in motor variability among individuals performing a standardized short-cycle manual task,” Hum. Mov. Sci., 51, 17–26 (2017). doi: https://doi.org/10.1016/j.humov.2016.10.009 CrossRefGoogle Scholar
  7. 7.
    A. Longo, T. Haid, R. Meulenbroek, and P. Federolf, “Biomechanics in posture space: Properties and relevance of principal accelerations for characterizing movement control,” J. Biomech., 82, 397–403 (2019). doi: https://doi.org/10.1016/j.jbiomech.2018.11.031 CrossRefGoogle Scholar
  8. 8.
    J.-H. Ko, J. H. Challis, and K. M. Newell, “Postural coordination patterns as a function of rhythmical dynamics of the surface of support,” Exp. Brain Res., 226, No. 2, 183–191 (2013). doi: https://doi.org/10.1007/s00221-013-3424-5 CrossRefGoogle Scholar
  9. 9.
    X. Wang, N. O’Dwyer, and M. Halaki, “A review on the coordinative structure of human walking and the application of principal component analysis,” Neural Regen. Res., 8, No. 7, 662–670 (2013). doi:https://doi.org/10.3969/j. issn.1673-5374.2013.07.011Google Scholar
  10. 10.
    D. P. Soares, M. P. de Castro, E. Mendes, and L. Machado, “Influence of wedges on lower limbs’ kinematics and net joint moments during healthy elderly gait using principal component analysis,” Hum. Mov. Sci., 38, 319–330 (2014). doi:https://doi.org/10.1016/j. humov.2014.09.007Google Scholar
  11. 11.
    M. M. Ardestani and M. A. Wimmer, “Can a linear combination of gait principal component vectors identify hip OA stages?,” J. Biomech., 49, No. 10, 2023–2030 (2016). doi: https://doi.org/10.1016/j.jbiomech.2016.04.040 CrossRefGoogle Scholar
  12. 12.
    S. Shaharudin, D. Zanotto, and S. Agrawal, “Muscle synergies of untrained subjects during 6 min maximal rowing on slides and fixed ergometer,” J. Sports Sci. Med., 13, No. 4, 793–800 (2014).Google Scholar
  13. 13.
    J. J. Kutch and F. J. Valero-Cuevas, “Challenges and new approaches to proving the existence of muscle synergies of neural origin,” PLoS Comput. Biol., 8, e1002434 (2012). doi: https://doi.org/10.1371/journal.pcbi.1002434 CrossRefGoogle Scholar
  14. 14.
    E. Bizzi and V. C. K. Cheung, “The neural origin of muscle synergies,” Front. Comput. Neurosci., 7, No. 51 (2013). doi: https://doi.org/10.3389/fncom.2013.00051
  15. 15.
    K. M. Steele, M. C. Tresch, and E. J. Perreault, “Consequences of biomechanically constrained tasks in the design and interpretation of synergy analyses,” J. Neurophysiol., 113, No. 7, 2102–2113 (2015). doi: https://doi.org/10.1152/jn.00769.2013 CrossRefGoogle Scholar
  16. 16.
    J. Taborri, V. Agostini, P. K. Artemiadis, et al., “Feasibility of muscle synergy outcomes in clinics, robotics, and sports: a systematic review,” App. Bionics Biomech., 2018, 3934698 (2018). doi: https://doi.org/10.1155/2018/3934698
  17. 17.
    A. Gallina, S. J. Garland, and J. M. Wakeling, “Identification of regional activation by factorization of high-density surface EMG signals: a comparison of principal component analysis and non-negative matrix factorization,” J. Electromyogr. Kinesiol., 41, 116–123 (2018). doi: https://doi.org/10.1016/j.jelekin.2018.05.002 CrossRefGoogle Scholar
  18. 18.
    I. V. Vereshchaka, A. V. Gorkovenko, O. V. Lehedza, et al., “The EMG patterns in the elbow and shoulder muscles of the human arm in slow para-frontal movements under isotonic loading,” Neurophysiology, 50, No. 6, 466–474 (2018). doi: https://doi.org/10.1007/s11062-019-09779-8 CrossRefGoogle Scholar
  19. 19.
    A. I. Kostyukov, “Theoretical analysis of the force and position synergies in two-joint movements,” Neurophysiology, 48, No. 4, 287-296 (2016).  https://doi.org/10.1007/s11062-016-9601-y CrossRefGoogle Scholar
  20. 20.
    A. I. Kostyukov and T. Tomiak, “The force generation in a two-joint arm model: analysis of the joint torques in the working space,” Front. Neurorobot., 12, 77 (2018).  https://doi.org/10.3389/fnbot.2018.00077
  21. 21.
    A. V. Gorkovenko, “Theoretical analysis of the peculiarities of motor control at generation of two-joint isometric efforts by the human upper limb,” Neurophysiology, 50, No. 4, 309-321 (2018).  https://doi.org/10.1007/s11062-018-9753-z CrossRefGoogle Scholar
  22. 22.
    N. Klopcar, M. Tomsic, and J. Lenarcic, “A kinematic model of the shoulder complex to evaluate the armreachable workspace,” J. Biomech., 40, No. 1, 86–91 (2007). doi: https://doi.org/10.1177/0954411916659894 CrossRefGoogle Scholar
  23. 23.
    B. Bolsterlee, D. H. E. J. Veeger, and E. K. Chadwick, “Clinical applications of musculoskeletal modelling for the shoulder and upper limb,” Med. Boil. Eng. Comput., 51, 953–963 (2013). doi: https://doi.org/10.1007/s11517-013-1099-5 CrossRefGoogle Scholar
  24. 24.
    F. Heinen, M. E. Lund, J. Rasmussen, and M. de Zee, “Muscle-tendon unit scaling methods of Hill-type musculoskeletal models: An overview,” Proc. Inst. Mech. Eng. H, 230, 976–984 (2016). doi: https://doi.org/10.1177/095441-1916659894 CrossRefGoogle Scholar
  25. 25.
    J. J. Kutch and T. S. Buchanan, “Human elbow joint torque is linearly encoded in electromyographic signals from multiple muscles,” Neurosci. Lett., 311, No. 2, 97–100 (2001).CrossRefGoogle Scholar
  26. 26.
    A. Daffertshofer, C. J. C. Lamoth, O. G. Meijer, and P. J. Beek, “PCA in studying coordination and variability: a tutorial,” Clin. Biomech. (Bristol, Avon), 19, No. 4, 415–428 (2004). doi: https://doi.org/10.1016/j.clinbiomech.2004.01.005 CrossRefGoogle Scholar
  27. 27.
    M. M. Ardestani, P. Malloy, D. Nam, et al., “TKA patients with unsatisfying knee function show changes in neuromotor synergy pattern but not joint biomechanics,” J. Electromyogr. Kinesiol., 37, 90–100 (2017). doi: https://doi.org/10.1016/j.jelekin.2017.09.006 CrossRefGoogle Scholar
  28. 28.
    M. L. Latash, “The bliss (not the problem) of motor abundance (not redundancy),” Exp. Brain Res., 217, No. 1, 1–5 (2012). doi: https://doi.org/10.1007/s00221-012-3000-4 CrossRefGoogle Scholar
  29. 29.
    M. L. Latash, “Biomechanics as a window into the neural control of movement,” J. Hum. Kinet., 52, No. 1, 7–20 (2016). doi: https://doi.org/10.1515/hukin-2015-0190 CrossRefGoogle Scholar
  30. 30.
    A. I. Kostyukov, “Muscle hysteresis and movement control: a theoretical study,” Neuroscience, 83, No. 1, 303–320 (1998).CrossRefGoogle Scholar
  31. 31.
    A. V. Gorkovenko, S. Sawczyn, N. V. Bulgakova, et al., “Muscle agonist-antagonist interactions in an experimental joint model,” Exp. Brain Res., 222, No. 4, 399–414 (2012). doi: https://doi.org/10.1007/s00221-012-3227-0 CrossRefGoogle Scholar
  32. 32.
    A. V. Gorkovenko, O. V. Legedza, I. V. Vereschaka, et al., “Erratum to: Hysteresis properties of EMG activity of the shoulder belt and shoulder muscles at the development of isometric efforts by the human arm,” Neurophysiology, 47, No. 2 (2015). doi: https://doi.org/10.1007/s11062-015-9516-z
  33. 33.
    M. Dornowski, O. V. Lehedza, V. S. Mishchenko, et al., “Hysteresis in EMG activity of muscles of the human upper limb at rotations of the isometric effort vector,” Neurophysiology, 49, No. 4, 308–312 (2017). doi: https://doi.org/10.1007/s11062-017-9688-9 CrossRefGoogle Scholar
  34. 34.
    C. J. van Groeningen, E. J. Nijhof, F. M. Vermeule, and C. J. Erkelens, “Relation between torque history, firing frequency, decruitment levels and force balance in two flexors of the elbow,” Exp. Brain Res., 129, No. 4, 592–604 (1999).CrossRefGoogle Scholar
  35. 35.
    S.-W. Park, H. Marino, S. K. Charles, et al., “Moving slowly is hard for humans: limitations of dynamic primitives,” J. Neurophysiol., 118, 69–83 (2017). doi: https://doi.org/10.1152/jn.00643.2016 CrossRefGoogle Scholar
  36. 36.
    S. L. Delp, F. C. Anderson, A. S. Arnold, et al., “OpenSim: open-source software to create and analyze dynamic simulations of movement,” IEEE Trans. Biomed. Eng., 54, 1940–50 (2007). doi: https://doi.org/10.1109/TBME.2007.901024 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • A. V. Gorkovenko
    • 1
    Email author
  • O. V. Lehedza
    • 1
  • T. I. Abramovych
    • 1
  • W. Pilewska
    • 2
  • V. S. Mischenko
    • 3
  • M. Zasada
    • 2
  1. 1.Department of Movement Physiology, Bogomolets Institute of Physiology, National Academy of SciencesKyivUkraine
  2. 2.Faculty of Physical Education, Health, and Tourism, Institute of Physical CultureKazimierz Wielki UniversityBydgoszczPoland
  3. 3.Department of Physical EducationGdansk University of Physical Education and SportGdanskPoland

Personalised recommendations