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Characterization of Spastic Ankle Flexors Based on Viscoelastic Modeling for Accurate Diagnosis

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Abstract

Characterization of the musculoskeletal system is essential for diagnosis providing the implications for therapy corresponding to causes of the diseases. This paper presents a characterization of an ankle neuromuscular system of patients with spasticity, to provide quantitative pathological level of the ankle spasticity with biomechanical and neurological disorders. Measurements from manual spasticity evaluation combined with a suggested neuromuscular model and parameter optimization process enabled a reliable characterization of the spastic ankle flexors. The model included two non-neural parameters representing the viscoelasticity of the muscle and four neural parameters showing the dynamics of muscle activation and corresponding force only using the measured joint angle and resistance torque. Torque contributions from non-neural parameters especially elastic properties of muscle was greater than 50% of the overall torque, common in both patients with spasticity and healthy controls. Among subgroups of the patients, subjects with short post diseases period less than 5 years, had higher torque contribution level from neural components more than 50% of the overall torque compared to the patients with longer post diseases period more than 10 years who had overall torque less than 30% of the total estimated torque. We concluded that proposed model based ankle flexor characterization served as a tools for diagnosing the patients with spasticity corresponding to their causes of diseases with both quantified neural and non-neural parameters.

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References

  1. J. W. Lance, “Spasticity: disordered motor control,” 1980.

  2. A. Gorgie, A. L. Rosenbloom, F. T. Weber, B. Giordano, J. I. Malone, and J. J. Shuster, “Joint contracture-common manifestation of childhood diabetes mellitus,” The Journal of Pediatrics, vol. 88, no. 4, pp. 584–588, 1976.

    Article  Google Scholar 

  3. J. M. Gregson, M. J. Leathley, and A. P. Moore, “Reliability of measurements of muscle tone and muscle power in stroke patients,” Age and Ageinh, vol. 29, no. 3, pp. 223–228, 2000.

    Article  Google Scholar 

  4. A. L. Fosang, “Measures of muscle and joint performance in the lower limb of children with cerebral palsy,” Developmental Medicine & Child Neurology, vol. 45, no. 10, pp. 664–670, 2003.

    Article  Google Scholar 

  5. M. Blackburn, P. van Vliet, S. P. Mockett, “Reliability of measurements obtained with the modified ashworth scale in the lower extremities of people with stroke,” Physical Therapy, vol. 82, no. 1, pp. 25–34, 2002.

    Article  Google Scholar 

  6. Y. N. Wu, Y. Ren, A. Goldsmith, D. Gaebler, S. Q. Liu, and L. Q. Zhang, “Characterization of spasticity in cerebral palsy: dependence of catch angle on velocity,” Dev. Med. Child Neurol., vol. 52, no. 6, pp. 563–569, 2010.

    Article  Google Scholar 

  7. S.-H. Schless, K. Desloovere, E. Aertbeliën, G. Molenaers, C. Huenaerts, L. Bar-On, “The intra- and inter-rater reliability of anInstrumented spasticity assessment in children with cerebral palsy,” PLoS One, vol. 10, no. 7, 2015.

    Article  Google Scholar 

  8. H. Lee and N. Hogan, “Time-varying ankle mechanical impedance during human locomotion,” IEEE Transanctions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 5, pp. 755–764, September 2015.

    Article  Google Scholar 

  9. H. Lee, P. Ho, M. A. Rastgaa, H. I. Krebs, and N. Hogan, “Multivariable static ankle mechanical impedance with relaxed muscles,” Journal of Biomechanics, vol. 44, no. 10, pp. 1901–1908, 2011.

    Article  Google Scholar 

  10. H. Lee, P. Ho, M. A. Rastgaar, H. I. Krebs, and N. Hogan, “Multivariable static ankle mechanical impedance with active muscles,” IEEE Transactions of Neural Systems and Rehabilitation Engineering, vol. 22, no. 1, pp. 44–52, 2014.

    Article  Google Scholar 

  11. H. Lee, H. I. Krebs, and N. Hogan, “Multivariable dynamic ankle mechanical impedance with relaxed muscles,” IEEE Transactions of Neural Systems and Rehabilitation Enginerring, vol. 22, no. 6, pp. 1104–1114, 2014.

    Article  Google Scholar 

  12. H. Lee, H. I. Krebs, and N. Hogan, “Multivariable dynamic ankle mechanical impedance with active muscles,” IEEE Transactions of Neural Systems and Rehabilitation Enginerring, vol. 22, no. 5, pp. 971–981, 2014.

    Article  Google Scholar 

  13. H. Lee, Quantitative Characterization of Multi-Variable Human Ankle Mechanical Impedance, Doctor of Philosophy at the Massachusetts Institute of Technology, 2013.

  14. R. E. Kearney and I. W. Hunter, “Dynamics of human ankle stiffness: variation with displacement amplitude,” Journal of Biomechanics, vol. 15, no. 10, pp. 753–756, 1982.

    Article  Google Scholar 

  15. R. E. Kearney and I. W. Hunter, “Dynamics of human ankle stiffness: variation with mean ankle torque,” Journal of Biomechanics, vol. 15, no. 10, pp. 747–752, 1982.

    Article  Google Scholar 

  16. P. L. Weiss, W. Hunter, and R. E. Kearny, “Human ankle joint stiffness over the full range of muscle activation levels,” Journal of Biomechanics, vol. 21, no. 7, pp. 539–544, 1988.

    Article  Google Scholar 

  17. J. B. MacNeil, R. E. Kearney, and I. W. Hunter, “Identification of time-varying biological systems from ensemble data,” IEEE Transanctions on Biomedical Engineering, vol. 39 no. 12, pp. 1213–1225, December 1992.

    Article  Google Scholar 

  18. R. E. Kearney and I. W. Humter, “Nonlinear identification of stretch reflex dynamics,” Annals of Biomedical Engineering, vol. 16, no. 1, pp. 79–94, 1988.

    Article  Google Scholar 

  19. P. L. Weiss, W. Hunter, and R. E. Kearny, “Position dependency of ankle joint dynamics — I. passive mechanics,” Journal of Biomechanics, vol. 19, no. 9, pp. 727–735, 1986.

    Article  Google Scholar 

  20. P. L. Weiss, W. Hunter, and R. E. Kearny, “Position dependency of ankle joint dynamics-II. passive mechanics,” Journal of Biomechanics, vol. 19, no. 9, pp. 737–751, 1986.

    Article  Google Scholar 

  21. M. J. Korenberg and I. W. Humter, “The identification of nonlinear biological systems: LNL cascade models,” Bio. Cybern., vol. 55, no. 2–3, pp. 125–134, 1986.

    MathSciNet  MATH  Google Scholar 

  22. R. E. Kearney, R. B. Stein, and L. Parameswaran, “Identification of intrinsic and reflex contributions to human ankle stiffness dynamics,” IEEE Transanctions on Biomedical Engineering, vol. 44, no. 6, pp. 463–504, June 1997.

    Google Scholar 

  23. E. de Vlugt, J. H. de Groot, K. E. Schenkeveld, J. H. Arendzen, F. C. T. van der Helm, and C. G. M. Meskers, “The relation between neuromechanical parameters and Ashworth score in stroke patients,” Journal of NeuroEngineering and Rehabilitation, vol. 7, no. 35, 2010.

  24. C. N. Maganaris, V. Baltzopoulis, and A. J. Sargeant, “In vivo measurement-based estimations of the human achilles tendon moment arm,” European Journal of Applied Physiology, vol. 83, no. 4–5, pp. 363–369, 2000.

    Article  Google Scholar 

  25. M. G. Hoy, F. E. Zajac, and M. E. Gordon, “A musculoskeletal model of the human lower extremity: the eject of muscle, tendon, and moment arm on the moment angle relationship of musculotendon actuators at the hip, knee and ankle,” Journal of Biomechanics, vol. 23, no. 2, pp. 157–169, 1990.

    Article  Google Scholar 

  26. A. Esteki and J. M. Mansour, “An experimentally based nonlinear viscoelastic model of joint passive moment,” J. Biomechanics, vol. 209, no. 4, pp 443–450, 1996.

    Article  Google Scholar 

  27. J. M. Winters and L. Stark, “Analysis of fundamental human movement patterns through the use of in-depth antagonistic muscle models,” IEEE Trans. Biomed. Eng., vol. BME-32, no. 10, pp. 826–839, 1985.

    Article  Google Scholar 

  28. L. Wilson, S. Gandevia, J. Inglis, J. Gracies, D. Burke, “Muscle spindle activity in the affected upper limb after a unilateral stroke,” Brain J. Neurol., vol. 122, no. 11, pp. 2079–2088, 1999.

    Article  Google Scholar 

  29. A. Fuglevand, D. Winter, A. Patla, “Models of recruitment and rate coding organization in motor-unit pools,” J. Neurophys, vol. 70, no. 6, pp. 2470–2488, 1993.

    Article  Google Scholar 

  30. W. J. Chen and R. E. Poppele, “Small-signal analysis of response of mammalian muscle spindles with fusimotor stimulation and a comparison with large-signal properties,” Journal of Neurophysiology, vol. 41, no. 1, pp. 15–27, 1978.

    Article  Google Scholar 

  31. P. B. C. Matthews and R. B. Stein, “The sensitivity of muscle spindle afferents to small sinusoidal changes of length,” Journal of Physiology, vol. 200, no. 3, pp. 723–743, 1969.

    Article  Google Scholar 

  32. R. Wang, P. Herman, Ö. Ekeberg, J. Gäverth, A. Fagergren, and H. Forssberg, “Neural and non-neural related properties in the spastic wrist flexors: An optimization study,” Med. Eng. Phys., vo. 47, pp. 198–209, 2017.

    Article  Google Scholar 

  33. T. K. K. Koo and A. F. T. Mak, “A neuromusculoskeletal model to simulate the constant angular velocity elbow extension test of spasticity,” Medical Engineering & Physics, vol. 28, no. 1, pp. 60–69, 2006.

    Article  Google Scholar 

  34. H. K. Khalil, Nonlinear Systems, 3rd ed., Pearson New International Edition, 2014.

  35. E. S. Tehrani, K. Jalaleddini, and R. E. Kearney, “Ankle joint intrinsic dynamics is more complex than a massspring-damper model,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 9, pp. 1568–1580, 2017.

    Article  Google Scholar 

  36. T. K. Koo and A. F. Mak, “A neuromusculoskeletal model to simulate the constant angular velocity elbow extension test of spasticity,” Med. Eng. Phys., vol. 28, no. 1, pp. 60–69, 2006.

    Article  Google Scholar 

  37. E. de Vlugt, J. H. de Groot, K. E. Schenkeveld, J. H. Arendzen, F. C. T. van der Helm, C. G. M. Meskers, “The relation between neuromechanical parameters and Ashworth score in stroke patients,” Journal of NeuroEngineering and Rehabilitation, vol. 7, pp. 35–50, 2010.

    Article  Google Scholar 

  38. C. E. Clauser, J. T. McConville, and J. W. Young, Weight, Volumne, and Center of Mass of Segments of the Human Body, AMRL-TR-69–70, Aerospace Medical Research Laboratory, Aerospace Medical Division, Air Force Systems Command, Wright-Patterson Air Force Base, Ohio, August 1969.

    Book  Google Scholar 

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Authors and Affiliations

  1. department of mechanical engineering, KAIST, Guseong-dong, Yuseonggu, Daejon, 305-701, Korea

    Won-Seok Shin, Sangjoon J. Kim & Jung Kim

  2. Department of Mechanical Engineering, Incheon National University, 119 Academy-ro, Songdo 1(il)-dong, Ueonsu-gu, Incheon, Korea

    Handdeut Chang

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Correspondence to Jung Kim.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Guest Editors Doo Yong Lee (KAIST) and Jaesoon Choi (Asan Medical Center). This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. NRF-2017M3A9E2063101).

Won-Seok Shin received his B.S. degree in Mechanical Engineering from Hanyang University in 2016, and the M.S. degree in mechanical engineering in 2018 from the Korea Advanced Institute of Science and Technologies (KAIST), Korea, where he is currently working toward a Ph.D. degree in mechanical engineering. His research interests include biomechanics, physical human robot interaction, and wearable robotics.

Handdeut Chang received his B.S. and M.S. degrees from the Department of Mechanical Engineering from Osaka University, Japan, in 2011 and 2013, respectively. He also holds a Ph.D. degree in Mechanical Engineering from Korea Advanced Institute of Science and Technology (KAIST), Korea which he earned in 2019. He is an Assistant Professor in the Department of Mechanical Engineering, Incheon National University, Incheon, Korea. His research interests include biomimetic robotics, variable impedance actuator, nonlinear dynamic system control, physical human robot interaction, and myoprocessor.

Sangjoon J. Kim received his B.S. degree in electrical engineering from the Department of Electrical and Computer Engineering, University of Wisconsin, Madison, USA, in 2012. He has received his M.S. and Ph.D. degree in mechanical engineering from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 2014 and 2019, respectively. His research interests include wearable robotics, physical human robot interaction, magnetic resonance (MR)-compatible robots and rehabilitation robotics.

Jung Kim received his B.S. and M.S. degrees in mechanical engineering from the Department of Mechanical Engineering, Korea Advanced Institute of Science and Technologies (KAIST), Korea, in 1991 and 1993, respectively, and the Ph.D. degree in mechanical engineering from the Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, in 2003. He is currently a Professor in the Department of Mechanical Engineering, KAIST. His current research interests include medical robotics, haptics, biomechanical signals, and assistive robotics.

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Shin, WS., Chang, H., Kim, S.J. et al. Characterization of Spastic Ankle Flexors Based on Viscoelastic Modeling for Accurate Diagnosis. Int. J. Control Autom. Syst. 18, 102–113 (2020). https://doi.org/10.1007/s12555-019-0245-8

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  • DOI: https://doi.org/10.1007/s12555-019-0245-8

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