Literature DB >> 29530500

Cluster-based upper body marker models for three-dimensional kinematic analysis: Comparison with an anatomical model and reliability analysis.

Quinn A Boser1, Aïda M Valevicius1, Ewen B Lavoie2, Craig S Chapman2, Patrick M Pilarski3, Jacqueline S Hebert4, Albert H Vette5.   

Abstract

Quantifying angular joint kinematics of the upper body is a useful method for assessing upper limb function. Joint angles are commonly obtained via motion capture, tracking markers placed on anatomical landmarks. This method is associated with limitations including administrative burden, soft tissue artifacts, and intra- and inter-tester variability. An alternative method involves the tracking of rigid marker clusters affixed to body segments, calibrated relative to anatomical landmarks or known joint angles. The accuracy and reliability of applying this cluster method to the upper body has, however, not been comprehensively explored. Our objective was to compare three different upper body cluster models with an anatomical model, with respect to joint angles and reliability. Non-disabled participants performed two standardized functional upper limb tasks with anatomical and cluster markers applied concurrently. Joint angle curves obtained via the marker clusters with three different calibration methods were compared to those from an anatomical model, and between-session reliability was assessed for all models. The cluster models produced joint angle curves which were comparable to and highly correlated with those from the anatomical model, but exhibited notable offsets and differences in sensitivity for some degrees of freedom. Between-session reliability was comparable between all models, and good for most degrees of freedom. Overall, the cluster models produced reliable joint angles that, however, cannot be used interchangeably with anatomical model outputs to calculate kinematic metrics. Cluster models appear to be an adequate, and possibly advantageous alternative to anatomical models when the objective is to assess trends in movement behavior.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Kinematic model; Marker cluster; Motion capture; Three-dimensional joint kinematics; Upper body

Mesh:

Year:  2018        PMID: 29530500     DOI: 10.1016/j.jbiomech.2018.02.028

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  4 in total

1.  Quantitative Eye Gaze and Movement Differences in Visuomotor Adaptations to Varying Task Demands Among Upper-Extremity Prosthesis Users.

Authors:  Jacqueline S Hebert; Quinn A Boser; Aïda M Valevicius; Hiroki Tanikawa; Ewen B Lavoie; Albert H Vette; Patrick M Pilarski; Craig S Chapman
Journal:  JAMA Netw Open       Date:  2019-09-04

2.  Myoelectric prosthesis users and non-disabled individuals wearing a simulated prosthesis exhibit similar compensatory movement strategies.

Authors:  Heather E Williams; Craig S Chapman; Patrick M Pilarski; Albert H Vette; Jacqueline S Hebert
Journal:  J Neuroeng Rehabil       Date:  2021-05-01       Impact factor: 4.262

Review 3.  Optical Motion Capture Systems for 3D Kinematic Analysis in Patients with Shoulder Disorders.

Authors:  Umile Giuseppe Longo; Sergio De Salvatore; Arianna Carnevale; Salvatore Maria Tecce; Benedetta Bandini; Alberto Lalli; Emiliano Schena; Vincenzo Denaro
Journal:  Int J Environ Res Public Health       Date:  2022-09-23       Impact factor: 4.614

4.  Gaze and Movement Assessment (GaMA): Inter-site validation of a visuomotor upper limb functional protocol.

Authors:  Heather E Williams; Craig S Chapman; Patrick M Pilarski; Albert H Vette; Jacqueline S Hebert
Journal:  PLoS One       Date:  2019-12-30       Impact factor: 3.240

  4 in total

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