Predicting Gait Patterns of Children With Spasticity by Simulating Hyperreflexia

Journal Article (2023)
Authors

K. Veerkamp (Amsterdam Movement Sciences, Vrije Universiteit Amsterdam, Griffith University)

Christopher P. Carty (Griffith University)

Niels F.J. Waterval (Amsterdam Movement Sciences, Universiteit van Amsterdam, Vrije Universiteit Amsterdam)

Thomas Geijtenbeek (TU Delft - Biomechatronics & Human-Machine Control)

Annemieke Irene Buizer (Universiteit van Amsterdam, Amsterdam Movement Sciences, Vrije Universiteit Amsterdam)

David G. Lloyd (Griffith University)

J Harlaar (Erasmus MC, TU Delft - Biomechatronics & Human-Machine Control)

M.M. van der Krogt (Vrije Universiteit Amsterdam, Amsterdam Movement Sciences)

Research Group
Biomechatronics & Human-Machine Control
Copyright
© 2023 Kirsten Veerkamp, Christopher P. Carty, Niels F.J. Waterval, T. Geijtenbeek, Annemieke I. Buizer, David G. Lloyd, J. Harlaar, Marjolein M. van der Krogt
To reference this document use:
https://doi.org/10.1123/jab.2023-0022
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Kirsten Veerkamp, Christopher P. Carty, Niels F.J. Waterval, T. Geijtenbeek, Annemieke I. Buizer, David G. Lloyd, J. Harlaar, Marjolein M. van der Krogt
Research Group
Biomechatronics & Human-Machine Control
Issue number
5
Volume number
39
Pages (from-to)
333-346
DOI:
https://doi.org/10.1123/jab.2023-0022
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Abstract

Spasticity is a common impairment within pediatric neuromusculoskeletal disorders. How spasticity contributes to gait deviations is important for treatment selection. Our aim was to evaluate the pathophysiological mechanisms underlying gait deviations seen in children with spasticity, using predictive simulations. A cluster analysis was performed to extract distinct gait patterns from experimental gait data of 17 children with spasticity to be used as comparative validation data. A forward dynamic simulation framework was employed to predict gait with either velocity- or force-based hyperreflexia. This framework entailed a generic musculoskeletal model controlled by reflexes and supraspinal drive, governed by a multiobjective cost function. Hyperreflexia values were optimized to enable the simulated gait to best match experimental gait patterns. Three experimental gait patterns were extracted: (1) increased knee flexion, (2) increased ankle plantar flexion, and (3) increased knee flexion and ankle plantar flexion when compared with typical gait. Overall, velocity-based hyperreflexia outperformed force-based hyperreflexia. The first gait pattern could mostly be explained by rectus femoris and hamstrings velocity-based hyperreflexia, the second by gastrocnemius velocity-based hyperreflexia, and the third by gastrocnemius, soleus, and hamstrings velocity-based hyperreflexia. This study shows how velocity-based hyperreflexia from specific muscles contributes to different spastic gait patterns, which may help in providing targeted treatment.

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