Inter-session repeatability of markerless motion capture gait kinematics

J Biomech. 2021 May 24:121:110422. doi: 10.1016/j.jbiomech.2021.110422. Epub 2021 Apr 8.

Abstract

The clinical uptake and influence of gait analysis has been hindered by inherent limitations of marker-based motion capture systems, which have long been the standard method for the collection of gait data including kinematics. Markerless motion capture offers an alternative method for the collection of gait kinematics that presents several practical benefits over marker-based systems. This work aimed to determine the reliability of lower limb gait kinematics from video based markerless motion capture using an established experimental protocol for testing reliability. Eight healthy adult participants performed three sessions of five over-ground walking trials in their own self-selected clothing, separated by an average of 8.5 days, while eight synchronized and calibrated cameras recorded video. Three-dimensional pose estimates from the video data were used to compute lower limb joint angles. Inter-session variability, inter-trial variability, and the variability ratio were used to assess the reliability of the gait kinematics. Compared to repeatability studies based on marker-based motion capture, inter-trial variability was slightly greater than previously reported for some angles, with an average across all joint angles of 2.5°. Inter-session variability was smaller on average than all previously reported values, with an average across all joint angles of 2.8°. Variability ratios were all smaller than those previously reported with an average of 1.1, indicating that the multi-session protocol increased the total variability of joint angles by 10% of the inter-trial variability. These results indicate that gait kinematics can be reliably measured using markerless motion capture.

Keywords: Deep learning; Gait analysis; Kinematics; Markerless motion capture; Repeatability.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Biomechanical Phenomena
  • Gait*
  • Humans
  • Motion
  • Reproducibility of Results
  • Walking*