Abstract

With the rise of biofeedback in gait training in cerebral palsy there is a need for real-time measurements of gait kinematics. The Human Body Model (HBM) is a recently developed model, optimized for the real-time computing of kinematics. This study evaluated differences between HBM and two commonly used models for clinical gait analysis: the Newington Model, also known as Plug-in-Gait (PiG), and the calibrated anatomical system technique (CAST). Twenty-five children with cerebral palsy participated. 3D instrumented gait analyses were performed in three laboratories across Europe, using a comprehensive retroreflective marker set comprising three models: HBM, PiG and CAST. Gait kinematics from the three models were compared using statistical parametric mapping, and RMSE values were used to quantify differences. The minimal clinically significant difference was set at 5°. Sagittal plane differences were mostly less than 5°. For frontal and transverse planes, differences between all three models for almost all segment and joint angles exceeded the value of minimal clinical significance. Which model holds the most accurate information remains undecided since none of the three models represents a ground truth. Meanwhile, it can be concluded that all three models are equivalent in representing sagittal plane gait kinematics in clinical gait analysis.

Highlights

  • Cerebral palsy (CP) is a common motor disorder affecting 2 in 1000 births in Europe (Johnson, 2002), often leading to an aberrant gait pattern (Davids, Rowan, & Davis, 2007)

  • This study evaluated differences between Human Body Model (HBM) and two commonly used models for clinical gait analysis: the Newington Model, known as Plug-in-Gait (PiG), and the calibrated anatomical system technique (CAST)

  • Kinematic curves for the three models and outcomes from Statistical Parametric Mapping (SPM) can be found in Figs. 2, 3 and 4

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Summary

Introduction

Cerebral palsy (CP) is a common motor disorder affecting 2 in 1000 births in Europe (Johnson, 2002), often leading to an aberrant gait pattern (Davids, Rowan, & Davis, 2007). Gait kinematics can be used as parameters in real-time gait specific biofeedback (Booth, Steenbrink, Buizer, Harlaar, & van der Krogt, 2016; Booth, Steenbrink, Buizer, Harlaar, & Van der Krogt, 2017; van Gelder et al, 2017). Such biofeedback can be used for functional gait training. The study further addressed that virtual reality and biofeedback seem promising tools to improve engagement and rehabilitation outcomes in children with CP (Booth et al, 2018). With the rise of biofeedback training, the need for accurate real-time measurements of kinematics is rising

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