Motor Commands for Planar Movements of the Upper Limb: Modeling with Taking into Account Realistic Osteo-Muscular Relations

Studies of motor control in humans require the formation of adequate concepts about the structure and metric characteristics of the musculoskeletal system, which provides the performance of the respective movements; such representations, in turn, are necessary prerequisites for further modeling of the phenomenology of motor control, theoretical analysis of the relevant processes, and interpretation of the obtained experimental data. We measured the length and shape of the muscles of the shoulder girdle (deltoideus pars scapilaris and pectoralis major pars clavicularis) of a healthy person performing planar (realized within the horizontal plane) movements of the upper limb, in which the hand moved via a straight line in the parafrontal plane at the level of the shoulder joint, and also calculated the corresponding arms of torques developed by these muscles. Such measurements were performed based on the results of computed tomography and 3D prints of the bones (scapula, clavicula, and humerus). According to these data, we built regression formulas for the dependence of metric parameters of the muscles on values of the angle in the shoulder joint. The results of modeling of descending activation of these muscles during the mentioned movements are also presented; EMG signals recorded from the corresponding muscles were considered correlates of the descending commands. The results of such simulation demonstrated a satisfactory similarity between the characteristics of the modeled motor commands and those recorded in real experiments. The results obtained show that we have developed a realistic model of the musculoskeletal system of the upper extremity, which can be effectively used in studies of motor control in humans. Moreover, there are reasons to believe that our proposed method of estimating the parameters of the musculoskeletal system of the human arm is significantly more adequate than those previously proposed, in which the original data underwent excessive simplification.

This is a preview of subscription content, access via your institution.

References

  1. 1.

    M. Dornowski, Ye. V. Kolosova, and A. V. Gorkovenko, “Gender and age-related peculiarities of the H-reflex indices in sportsmen,” Neurophysiology, 49, No. 6, 458–461 (2017). doi: https://doi.org/10.1007/s11062-018-9709-3

    Article  Google Scholar 

  2. 2.

    M. Dornowski, V. S. Mishchenko, and A. V. Gorkovenko, “Functional connections in the human cerebral cortex at repetitive flexions and extensions of the fingers,” Neurophysiology, 50, No. 4, 286–291 (2018). doi: https://doi.org/10.1007/s11062-018-9750-2

    Article  Google Scholar 

  3. 3.

    M. Dornowski, A. Gorkovenko, T. Tomiak, et al. “Cyclic movement execution and its influence on motor programmes,” Ann. Agric. Environ. Med., 26, No. 2, 361–368 (2019). doi: https://doi.org/10.26444/aaem/94881

    Article  PubMed  Google Scholar 

  4. 4.

    А. I. Kostyukov, O. V. Lehedza, A. V. Gorkovenko, et al., “Hysteresis and synergy of the central commands to muscles participating in parafrontal upper limb movements,” Front. Physiol., 10, 1441 (2019). doi: https://doi.org/10.3389/fphys.2019.01441

  5. 5.

    A. I. Kostyukov, “Theoretical analysis of the force and position synergies in two-joint movements,” Neurophysiology, 48, No. 4, 287–296 (2016). doi: https://doi.org/10.1007/s11062-016-9601-y

    Article  Google Scholar 

  6. 6.

    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. Neurorobotics, 12, 77 (2018). doi: https://doi.org/10.3389/fnbot.2018.00077

  7. 7.

    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). doi: https://doi.org/10.1007/s11062-018-9753-z

    Article  Google Scholar 

  8. 8.

    A. V. Gorkovenko, T. Tomiak, W. Pilewska, and A. I. Kostyukov, “Synergetic control during generation of a maximal isometric effort by the human arm,” Neurophysiology, 52, No. 1, 49–59 (2020). doi: https://doi.org/10.1007/s11062-020-09850-9

    Article  Google Scholar 

  9. 9.

    V. Gritsenko, R. L. Hardesty, M. T. Boots, and S. Yakovenko, “Biomechanical constraints underlying motor primitives derived from the musculoskeletal anatomy of the human arm,” PLoS One, 11, e0164050 (2016). doi: https://doi.org/10.1371/journal.pone.0164050

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    M. Asghari, S. Behzadipour, and G. Taghizadeh, “A planar neuro-musculoskeletal arm model in post-stroke patients,” Biol. Cybern., 112, 483–494 (2018). doi: https://doi.org/10.1007/s00422-018-0773-y

    Article  PubMed  Google Scholar 

  11. 11.

    J. Zhou, N. C. Benson, K. Kay, and J. Winawer, “Predicting neuronal dynamics with a delayed gain control model,” PLoS Comput. Biol., 15, e1007484 (2019). doi: https://doi.org/10.1371/journal.pcbi.1007484

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    P. Pigeon, L. Yahia, and A. G. Feldman, “Moment arms and lengths of human upper limb muscles as functions of joint angles,” J. Biomech., 29, No. 10, 1365–1370 (1996). doi: https://doi.org/10.1016/0021-9290(96)00031-0

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    B. A. Garner and M. G. Pandy, “The obstacle-set method for representing muscle paths in musculoskeletal models,” Comput. Methods Biomech. Biomed. Engin., 3, 1–30 (2000). doi: https://doi.org/10.1080/10255840008915251

    Article  PubMed  Google Scholar 

  14. 14.

    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, 1940–1950 (2007). doi: https://doi.org/10.1109/TBME.2007.901024

    Article  PubMed  Google Scholar 

  15. 15.

    I. V. Vereshchaka, A. V. Gorkovenko, O. V. Lehedza, et al., “EMG patterns of the elbow- and shoulderoperating muscles in slow parafrontal upper limb movements under isotonic loading,” Neurophysiology, 50, No. 6, 466–474 (2018). doi: https://doi.org/10.1007/s11062-019-09779-8

    Article  Google Scholar 

  16. 16.

    А. V. Gorkovenko, O. V. Lehedza, T. I. Abramovych, et al., “Evaluation of the complexity of control of simple linear hand movements using principal component analysis,” Neurophysiology, 51, No. 2, 132–140 (2019).

    Article  Google Scholar 

  17. 17.

    M. Winters, “Hill-based muscle models: a systems engineering perspective,” in: J. M. Winters and S. L. Y. Woo (eds.), Multiple Muscle Systems, Springer, New York, pp. 69–93 (1990).

    Google Scholar 

  18. 18.

    T. S. Buchanan, D. G. Lloyd, K. Manal, and T. F. Besier, “Estimation of muscle forces and joint moments using a forward-inverse dynamics model,” Med. Sci. Sports Exerc., 37, 1911–1916 (2005). doi: https://doi.org/10.1249/01.mss.0000176684.24008.6f

    Article  PubMed  Google Scholar 

  19. 19.

    T. Siebert, N. Stutzig, and C. Rode, “A Hill-type muscle model expansion accounting for effects of varying transverse muscle load,” J. Biomech., 66, 57–62 (2018). doi: https://doi.org/10.1016/j.jbiomech.2017.10.043

    Article  PubMed  Google Scholar 

  20. 20.

    D. Stanev and K. Moustakas, “Stiffness modulation of redundant musculoskeletal systems,” J. Biomech., 85, 101–107 (2019). doi: https://doi.org/10.1016/j.jbiomech.2019.01.017

    Article  PubMed  Google Scholar 

  21. 21.

    А. Seth, M. Dong, R. Matias, and S. Delp, “Muscle contributions to upper-extremity movement and work from a musculoskeletal model of the human shoulder,” Front. Neurorobot., 13, 90 (2019). doi: https://doi.org/10.3389/fnbot.2019.00090

  22. 22.

    D. Buongiorno, M. Barsotti, F. Barone, et al., “A linear approach to optimize an EMG-driven neuromusculoskeletal model for movement intention detection in myo-control: a case study on shoulder and elbow joints,” Front. Neurorobot., 12, 74 (2018). doi: https://doi.org/10.3389/fnbot.2018.00074

  23. 23.

    F. Hik and D. C. Ackland, “The moment arms of the muscles spanning the glenohumeral joint: a systematic review,” J. Anat., 234, No. 1, 1–15 (2019). doi: https://doi.org/10.1111/joa.12903

    Article  PubMed  Google Scholar 

  24. 24.

    A. Garner and M. G. Pandy, “Musculoskeletal model of the upper limb based on the visible human male dataset,” Comput. Methods Biomech. Biomed. Eng., 4, No. 2, 93–126 (2001). doi: https://doi.org/10.1080/10255840008908000

    CAS  Article  Google Scholar 

  25. 25.

    B. K. Kuechle, S. R. Newman, E. Itoi, et al., “Shoulder muscle moment arms during horizontal flexion and elevation,” J. Shoulder Elbow Surg., 6, No. 5, 429–439 (1997). doi: https://doi.org/10.1016/s1058-2746(97)70049-1

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    P. L. Cheng, “Simulation of Codman’s paradox reveals a general law of motion,” J Biomech, 39, No. 7, 1201–1207 (2006). doi: https://doi.org/10.1016/j.jbiomech.2005.03.017

    Article  PubMed  Google Scholar 

  27. 27.

    S. I. Wolf, L. Fradet, and O. Rettig, “Conjunct rotation: Codman’s paradox revisited,” Med. Biol. Eng. Comput., 47, No. 5, 551–556 (2009). doi: https://doi.org/10.1007/s11517-009-0484-6

    Article  PubMed  Google Scholar 

  28. 28.

    E. Pennestri, R. Stefanelli, P. P. Valentini, and L. Vita, “Virtual musculo-skeletal model for the biomechanical analysis of the upper limb,” J. Biomech., 40, 1350–1361 (2007). doi: https://doi.org/10.1016/j.jbiomech.2006.05.013

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    M. Hayashibe and D. Guiraud, “Voluntary EMG-toforce estimation with a multi-scale physiological muscle model,” Biomed. Eng. Online, 12, 86 (2013). doi: https://doi.org/10.1186/1475-925X-12-86

    Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    A. Falisse, S. V. Rossom, I. Jonkers, and F. D. Groote, “EMG-driven optimal estimation of subject-specific Hill model muscle-tendon parameters of the knee joint actuators,” IEEE Trans. Biomed. Eng., 64, No. 9, 2253–2262 (2017). doi: https://doi.org/10.1109/TBME.2016.2630009

    Article  PubMed  Google Scholar 

  31. 31.

    W. Herzog, “Passive force enhancement in striated muscle,” J. Appl. Physiol. (1985), 126, No. 6, 1782–1789 (2019). doi: https://doi.org/10.1152/japplphysiol.00676.2018

    CAS  Article  Google Scholar 

  32. 32.

    R. A. M. Pinnell, P. Mashouri, N. Mazara, et al., “Residual force enhancement and force depression in human single muscle fibres,” J. Biomech., 91, 164–169 (2019). doi: https://doi.org/10.1016/j.jbiomech.2019.05.025

    Article  PubMed  Google Scholar 

  33. 33.

    A. I. Kostyukov, “Muscle hysteresis and movement control: a theoretical study,” Neuroscience, 83, No. 1, 303–320 (1998). doi: https://doi.org/10.1016/s0306-4522(97)00379-5.

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    A. I. Kostyukov, F. Hellstrom, O. E. Korchak, et al., “Fatigue effects in the cat gastrocnemius during frequency-modulated efferent stimulation,” Neuroscience, 97, No. 4, 789–799 (2000). doi: https://doi.org/10.1016/s0306-4522(00)00066-x

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    A. P. Mel’nichouk, N. V. Bulgakova, A. N. Tal’nov, et al., “Movement-dependent positioning errors in human elbow joint movements,” Exp. Brain Res., 176, No. 2, 237–247 (2007). doi: https://doi.org/10.1007/s00221-006-0612-6

    Article  PubMed  Google Scholar 

  36. 36.

    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. doi: https://doi.org/10.1007/s11062-015-9516-z

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to A. V. Gorkovenko.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gorkovenko, A.V., Strafun, S.S., Kulyk, Y.A. et al. Motor Commands for Planar Movements of the Upper Limb: Modeling with Taking into Account Realistic Osteo-Muscular Relations. Neurophysiology 52, 222–233 (2020). https://doi.org/10.1007/s11062-020-09874-1

Download citation

Keywords

  • upper limb movement
  • central motor commands
  • tomography
  • musculoskeletal model
  • Hill model
  • m. deltoideus
  • m. pectoralis