Abstract
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.
Highlights
1.1 Conventional gait modelHuman locomotion has been a central theme of biomechanics research for centuries (c.f. [1] for a historical perspective)
Fundamental to the evaluation of human motion is the precise quantification of three-dimensional joint kinematics; as the technology to record motion grew over time, the translation between engineering and clinical approaches to measure this motion became a challenge [2]
The results show that both the upper and lower body joint angle estimations from pyCGM agree with the Vicon conventional gait model (CGM) outputs within 10-5 degrees
Summary
1.1 Conventional gait modelHuman locomotion has been a central theme of biomechanics research for centuries (c.f. [1] for a historical perspective). Biomechanical models with clinical relevant measures have been developed to overcome this problem. There are many biomechanical models available for kinematic analyses [3,4,5,6,7,8]. The CGM, known as the Newington, Davis, Gage, Helen Hayes, or Kadaba model, has been used extensively in clinical and research settings for many years. This paper focuses on the Vicon CGM implementation. This model began to be defined by Kadaba, first with experiments in repeatability [8] and as a method to calculate lower-limb joint angles [12], based on the orthopedic knee angle definitions of Grood and Suntay [13]
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