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The effects of anatomical errors on shoulder kinematics computed using multi-body models
Authors:Lavaill  Maxence  Martelli  Saulo  Gilliland  Luke  Gupta  Ashish  Kerr  Graham  Pivonka  Peter
Affiliation:1.School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
;2.Queensland Unit for Advanced Shoulder Research, Brisbane, QLD, Australia
;3.Medical Device Research Institute, College of Science and Engineering, Flinders University, Tonsley, SA, Australia
;4.Movement Neuroscience Group, School of Exercise & Nutrition Sciences, Queensland University of Technology, Brisbane, QLD, Australia
;5.Greenslopes Private Hospital, Brisbane, QLD, Australia
;
Abstract:

Joint motion calculated using multi-body models and inverse kinematics presents many advantages over direct marker-based calculations. However, the sensitivity of the computed kinematics is known to be partly caused by the model and could also be influenced by the participants’ anthropometry and sex. This study aimed to compare kinematics computed from an anatomical shoulder model based on medical images against a scaled-generic model and quantify the effects of anatomical errors and participants’ anthropometry on the calculated joint angles. Twelve participants have had planar shoulder movements experimentally captured in a motion lab, and their shoulder anatomy imaged using an MRI scanner. A shoulder multi-body dynamics model was developed for each participant, using both an image-based approach and a scaled-generic approach. Inverse kinematics have been performed using the two different modelling procedures and the three different experimental motions. Results have been compared using Bland–Altman analysis of agreement and further analysed using multi-linear regressions. Kinematics computed via an anatomical and a scaled-generic shoulder models differed in average from 3.2 to 5.4 degrees depending on the task. The MRI-based model presented smaller limits of agreement to direct kinematics than the scaled-generic model. Finally, the regression model predictors, including anatomical errors, sex, and BMI of the participant, explained from 41 to 80% of the kinematic variability between model types with respect to the task. This study highlighted the consequences of modelling precision, quantified the effects of anatomical errors on the shoulder kinematics, and showed that participants' anthropometry and sex could indirectly affect kinematic outcomes.

Keywords:
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