Moving outside the lab: Markerless motion capture accurately quantifies sagittal plane kinematics during the vertical jump.

Autor: Drazan JF; Department of Orthopedic Surgery, University of Pennsylvania, Philadelphia, PA, United States., Phillips WT; Electrical and Computer Engineering Department, University of Rochester, University of Rochester, Rochester, NY, United States., Seethapathi N; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States., Hullfish TJ; Department of Orthopedic Surgery, University of Pennsylvania, Philadelphia, PA, United States., Baxter JR; Department of Orthopedic Surgery, University of Pennsylvania, Philadelphia, PA, United States. Electronic address: josh.baxter@uphs.upenn.edu.
Jazyk: angličtina
Zdroj: Journal of biomechanics [J Biomech] 2021 Aug 26; Vol. 125, pp. 110547. Date of Electronic Publication: 2021 Jun 13.
DOI: 10.1016/j.jbiomech.2021.110547
Abstrakt: Markerless motion capture using deep learning approaches have potential to revolutionize the field of biomechanics by allowing researchers to collect data outside of the laboratory environment, yet there remain questions regarding the accuracy and ease of use of these approaches. The purpose of this study was to apply a markerless motion capture approach to extract lower limb angles in the sagittal plane during the vertical jump and to evaluate agreement between the custom trained model and gold standard motion capture. We performed this study using a large open source data set (N = 84) that included synchronized commercial video and gold standard motion capture. We split these data into a training set for model development (n = 69) and test set to evaluate capture performance relative to gold standard motion capture using coefficient of multiple correlations (CMC) (n = 15). We found very strong agreement between the custom trained markerless approach and marker-based motion capture within the test set across the entire movement (CMC > 0.991, RMSE < 3.22°), with at least strong CMC values across all trials for the hip (0.853 ± 0.23), knee (0.963 ± 0.471), and ankle (0.970 ± 0.055). The strong agreement between markerless and marker-based motion capture provides evidence that markerless motion capture is a viable tool to extend data collection to outside of the laboratory. As biomechanical research struggles with representative sampling practices, markerless motion capture has potential to transform biomechanical research away from traditional laboratory settings into venues convenient to populations that are under sampled without sacrificing measurement fidelity.
(Copyright © 2021. Published by Elsevier Ltd.)
Databáze: MEDLINE
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