This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The conventional gait model (CGM) is a widely used biomechanical model which has been validated over many years. The CGM relies on retro-reflective markers placed along anatomical landmarks, a static calibration pose, and subject measurements as inputs for joint angle calculations. While past literature has shown the possible errors caused by improper marker placement, studies on the effects of inaccurate subject measurements are lacking. Moreover, as many laboratories rely on the commercial version of the CGM, released as the Plug-in Gait (Vicon Motion Systems Ltd, Oxford, UK), integrating improvements into the CGM code is not easily accomplished. This paper introduces a Python implementation for the CGM, referred to as pyCGM, which is an open-source, easily modifiable, cross platform, and high performance computational implementation. The aims of pyCGM are to (1) reproduce joint kinematic outputs from the Vicon CGM and (2) be implemented in a parallel approach to allow integration on a high performance computer. The aims of this paper are to (1) demonstrate that pyCGM can systematically and efficiently examine the effect of subject measurements on joint angles and (2) be updated to include new calculation methods suggested in the literature. The results show that the calculated joint angles from pyCGM agree with Vicon CGM outputs, with a maximum lower body joint angle difference of less than 10 -5 degrees. Through the hierarchical system, the ankle joint is the most vulnerable to subject measurement error. Leg length has the greatest effect on all joints as a percentage of measurement error. When compared to the errors previously found through inter-laboratory measurements, the impact of subject measurements is minimal, and researchers should rather focus on marker placement. Finally, we showed that code modifications can be performed to include improved hip, knee, and ankle joint centre estimations suggested in the existing literature. The pyCGM code is provided in open source format and available at https://github.com/cadop/pyCGM.
Schwartz, M., & Dixon, P. C. (2018). The effect of subject measurement error on joint kinematics in the conventional gait model: Insights from the open-source pyCGM tool using high performance computing methods. PLoS ONE, 13(1). https://doi.org/10.1371/journal.pone.0189984