Inter-session repeatability of markerless motion capture gait kinematics

Kanko, Robert M.; Laende, Elise; Selbie, W. Scott; Deluzio, Kevin J.

Affiliations

¹Mechanical and Materials Engineering, Queen’s University, Canada

²Theia Markerless Inc, Kingston, Canada

Abstract and keywords

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

Links

DOI: 10.1016/j.jbiomech.2021.110422

PubMed: 33873117

WoS: 000652652700021

History

Accepted: 2021-03-29

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Cited By (5)

Year	Entry
2021	Kanko RM, Laende EK, Strutzenberger G, Brown M, Selbie WS, DePaul V, Scott SH, Deluzio KJ. Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system. J Biomech. June 9, 2021;122:110414.
2023	Eveleigh KJ, Deluzio KJ, Scott SH, Laende EK. Principal component analysis of whole-body kinematics using markerless motion capture during static balance tasks. J Biomech. May 2023;152:111556.
2023	Song K, Hullfish TJ, Scattone Silva R, Silbernagel KG, Baxter JR. Markerless motion capture estimates of lower extremity kinematics and kinetics are comparable to marker-based across 8 movements. J Biomech. August 2023;157:111751.
2023	Horsak B, Eichmann A, Lauer K, Prock K, Krondorfer P, Siragy T, Dumphart B. Concurrent validity of smartphone-based markerless motion capturing to quantify lower-limb joint kinematics in healthy and pathological gait. J Biomech. October 2023;159:111801.
2022	Young C. 3D Printed Wearable Devices for Out-of-Lab Joint Performance Assessment: Design, Validation and Applications [PhD thesis]. University of Guelph; August 2022.