Predicting Gait Patterns of Children With Spasticity by Simulating Hyperreflexia

Author:

Veerkamp Kirsten12345ORCID,Carty Christopher P.3456ORCID,Waterval Niels F.J.127ORCID,Geijtenbeek Thomas8ORCID,Buizer Annemieke I.129ORCID,Lloyd David G.345ORCID,Harlaar Jaap810ORCID,van der Krogt Marjolein M.12ORCID

Affiliation:

1. Department of Rehabilitation Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

2. Rehabilitation & Development, Amsterdam Movement Sciences, Amsterdam, The Netherlands

3. School of Health Sciences and Social Work, Griffith University, Gold Coast, QLD, Australia

4. Griffith Centre of Biomedical & Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD, Australia

5. Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia

6. Department of Orthopaedics, Children’s Health Queensland Hospital and Health Service, Queensland Children’s Hospital, Brisbane, QLD, Australia

7. Department of Rehabilitation Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands

8. Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands

9. Emma Children’s Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands

10. Department of Orthopedics and Sports Medicine, Erasmus Medical Center, Rotterdam, The Netherlands

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.

Publisher

Human Kinetics

Subject

Rehabilitation,Orthopedics and Sports Medicine,Biophysics

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