Literature DB >> 33873117

Inter-session repeatability of markerless motion capture gait kinematics.

Robert M Kanko1, Elise Laende2, W Scott Selbie3, Kevin J Deluzio2.   

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.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

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

Year:  2021        PMID: 33873117     DOI: 10.1016/j.jbiomech.2021.110422

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


  6 in total

1.  Feasibility of Markerless Motion Capture for Three-Dimensional Gait Assessment in Community Settings.

Authors:  Theresa E McGuirk; Elliott S Perry; Wandasun B Sihanath; Sherveen Riazati; Carolynn Patten
Journal:  Front Hum Neurosci       Date:  2022-06-09       Impact factor: 3.473

2.  Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains.

Authors:  Sherveen Riazati; Theresa E McGuirk; Elliott S Perry; Wandasun B Sihanath; Carolynn Patten
Journal:  Front Hum Neurosci       Date:  2022-06-16       Impact factor: 3.473

3.  The accuracy of several pose estimation methods for 3D joint centre localisation.

Authors:  Laurie Needham; Murray Evans; Darren P Cosker; Logan Wade; Polly M McGuigan; James L Bilzon; Steffi L Colyer
Journal:  Sci Rep       Date:  2021-10-19       Impact factor: 4.379

4.  Biomechanical Analysis of the Throwing Athlete and Its Impact on Return to Sport.

Authors:  Nicholas A Trasolini; Kristen F Nicholson; Joseph Mylott; Garrett S Bullock; Tessa C Hulburt; Brian R Waterman
Journal:  Arthrosc Sports Med Rehabil       Date:  2022-01-28

5.  Applications and limitations of current markerless motion capture methods for clinical gait biomechanics.

Authors:  Logan Wade; Laurie Needham; Polly McGuigan; James Bilzon
Journal:  PeerJ       Date:  2022-02-25       Impact factor: 2.984

6.  Accuracy of a markerless motion capture system in estimating upper extremity kinematics during boxing.

Authors:  Bhrigu K Lahkar; Antoine Muller; Raphaël Dumas; Lionel Reveret; Thomas Robert
Journal:  Front Sports Act Living       Date:  2022-07-25
  6 in total

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