Ground reaction force and joint moment estimation during gait using an Azure Kinect-driven musculoskeletal modeling approach

Zachary Ripic, Christopher Kuenze, Michael Skipper Andersen, Ilias Theodorakos, Joseph Signorile, Moataz Eltoukhy

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Gait analysis is burdened by time and equipment costs, interpretation, and accessibility of three-dimensional motion analysis systems. Evidence suggests growing adoption of gait testing in the shift toward evidence-based medicine. Further developments addressing these barriers will aid its efficacy in clinical practice. Previous research aiming to develop gait analysis systems for kinetics estimation using the Kinect V2 have provided promising results yet modified approaches using the latest hardware may further aid kinetics estimation accuracy Research question: Can a single Azure Kinect sensor combined with a musculoskeletal modeling approach provide kinetics estimations during gait similar to those obtained from marker-based systems with embedded force platforms? Methods: Ten subjects were recruited to perform three walking trials at their normal speed. Trials were recorded using an eight-camera optoelectronic system with two embedded force plates and a single Azure Kinect sensor. Marker and depth data were both used to drive a musculoskeletal model using the AnyBody Modeling System. Predicted kinetics from the Azure Kinect-driven model, including ground reaction force (GRF) and joint moments, were compared to measured values using root meansquared error (RMSE), normalized RMSE, Pearson correlation, concordance correlation, and statistical parametric mapping Results: High to very high correlations were observed for anteroposterior GRF (ρ = 0.889), vertical GRF (ρ = 0.940), and sagittal hip (ρ = 0.805) and ankle (ρ = 0.876) moments. RMSEs were 1.2 ± 2.2 (%BW), 3.2 ± 5.7 (%BW), 0.7 ± 0.1.3 (%BWH), and 0.6 ± 1.0 (%BWH) Significance: The proposed approach using the Azure Kinect provided higher accuracy compared to previous studies using the Kinect V2 potentially due to improved foot tracking by the Azure Kinect. Future studies should seek to optimize ground contact parameters and focus on regions of error between predicted and measured kinetics highlighted currently for further improvements in kinetic estimations.

Original languageEnglish (US)
Pages (from-to)49-55
Number of pages7
JournalGait and Posture
Volume95
DOIs
StatePublished - Jun 2022
Externally publishedYes

Keywords

  • Gait analysis
  • GRF prediction
  • Kinect
  • Musculoskeletal modeling

ASJC Scopus subject areas

  • Biophysics
  • Orthopedics and Sports Medicine
  • Rehabilitation

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