Principal component analysis of whole-body kinematics using markerless motion capture during static balance tasks

J Biomech. 2023 May:152:111556. doi: 10.1016/j.jbiomech.2023.111556. Epub 2023 Mar 27.

Abstract

Balance tests have clinical utility in identifying balance deficits and supporting recommendations for appropriate treatments. Motion capture technology can be used to measure whole-body kinematics during balance tasks, but to date the high technical and financial costs have limited uptake of traditional marker-based motion capture systems for clinical applications. Markerless motion capture technology using standard video cameras has the potential to provide whole-body kinematic assessments with clinically accessible technology. Our aim was to quantify poses and movement strategies during static balance tasks (tandem stance, single limb stance, standing hip abduction, and quiet standing on foam with eyes closed) using video-based markerless motion capture software (Theia3D) and principal component analysis to examine the associations with age, body mass index (BMI) and sex. In 30 healthy adults, the mean poses for all balance tasks had at least one principal component (PC) that differed significantly by sex. Age was significantly associated with the PC describing leg height for the hip abduction task and erect posture for the quiet standing task. BMI was significantly associated with the PC capturing knee flexion in the single leg stance task. The movement strategies used to maintain balance showed significant differences by sex for the tandem stance pose. BMI was correlated with PCs for movement strategies for hip abduction and quiet standing tasks. Results from this study demonstrate how markerless motion capture technology could be used to augment analyses of balance both in the clinic and in the field.

Keywords: Balance; Markerless motion capture; Movement strategies; Principal component analysis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Biomechanical Phenomena
  • Humans
  • Lower Extremity
  • Motion Capture*
  • Movement*
  • Principal Component Analysis