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Orthopaedic Proceedings
Vol. 94-B, Issue SUPP_XL | Pages 162 - 162
1 Sep 2012
Scheys L Wong P Callewaert B Leffler J Franz A Vandenneucker H Labey L Leardini A Desloovere K
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INTRODUCTION

In patients with neural disorders such as cerebral palsy, three-dimensional marker-based motion analysis has evolved to become a well standardized procedure with a large impact on the clinical decision-making process. On the other hand, in knee arthroplasty research, motion analysis has been little used as a standard tool for objective evaluation of knee joint function. Furthermore, in the available literature, applied methodologies are diverse, resulting in inconsistent findings [1]. Therefore we developed and evaluated a new motion analysis framework to enable standardized quantitative assessment of knee joint function.

MATERIALS AND METHODS

The proposed framework integrates a custom-defined motion analysis protocol with associated reference database and a standardized post-processing step including statistical analysis. Kinematics are collected using a custom-made marker set defined by merging two existing protocols and combine them with a knee alignment device. Following a standing trial, a star-arc hip motion pattern and a set of knee flexion/extension cycles allowing functional, subject-specific calibration of the underlying kinematic model, marker trajectories are acquired for three trials of a set of twelve motor tasks: walking, walking with crossover turn, walking with sidestep turn, stair ascent, stair descent, stair descent with crossover turn, stair descent with sidestep turn, trunk rotations, chair rise, mild squat, deep squat and lunge. This specific set of motor tasks was selected to cover as much as possible common daily life activities. Furthermore, some of these induce greater motion at the knee joint, thus improving the measurement-to-error ratio. Kinetics are acquired by integrating two forceplates in the walkway. Bilateral muscle activity of 8 major muscles is monitored with a 16 channel wireless electromyography (EMG) system. Finally, custom-built software with an associated graphical user interface was created for automated and flexible analysis of gait lab data, including repeatability analysis, analysis of specific kinematic, kinetic and spatiotemporal parameters and statistical comparisons.