Evidence map›Paper›PMID 39512656›Full record

ArticleFrontiers in bioengineering and biotechnology2024

Evaluation of drop vertical jump kinematics and kinetics using 3D markerless motion capture in a large cohort.

Tylan Templin, Christopher D Riehm, Travis Eliason, Tessa C Hulburt, Samuel T Kwak, Omar Medjaouri, David Chambers, Manish Anand, Kase Saylor, Gregory D Myer and 1 more

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Tylan TemplinSouthwest Research Institute, San Antonio, TX, United States.
Christopher D RiehmEmory Sports Performance And Research Center (SPARC), Flowery Branch, GA, United States.
Travis EliasonSouthwest Research Institute, San Antonio, TX, United States.
Tessa C HulburtEmory Sports Performance And Research Center (SPARC), Flowery Branch, GA, United States.
Samuel T KwakEmory Sports Performance And Research Center (SPARC), Flowery Branch, GA, United States.
Omar MedjaouriSouthwest Research Institute, San Antonio, TX, United States.
David ChambersSouthwest Research Institute, San Antonio, TX, United States.
Manish AnandEmory Sports Performance And Research Center (SPARC), Flowery Branch, GA, United States.
Kase SaylorSouthwest Research Institute, San Antonio, TX, United States.
Gregory D MyerEmory Sports Performance And Research Center (SPARC), Flowery Branch, GA, United States.
Daniel P NicolellaSouthwest Research Institute, San Antonio, TX, United States.

Funding

Real-Time Sensorimotor Feedback for Injury Prevention Assessed in Virtual RealityU01AR067997 · NIAMS · EMORY UNIVERSITY · PI MYER, GREGORY DONALD · 2016 to 2022
$3.6M
Neuroplastic Mechanisms for Acquisition and Transfer of Injury-Resistant Movement Patterns Assessed in VR Simulated SportR01AR076153 · NIAMS · EMORY UNIVERSITY · PI GROOMS, DUSTIN ROBERT, MYER, GREGORY DONALD · 2020 to 2022
$1.1M
Discovery of the Neural Drivers Underlying Injury-Risk BiomechanicsR01AR077248 · NIAMS · OHIO UNIVERSITY ATHENS · PI GROOMS, DUSTIN ROBERT · 2021 to 2023
$557k
AHRQ HHS T32 HS029585NIAMS NIH HHS R01 AR076153NIAMS NIH HHS R01 AR077248NIAMS NIH HHS U01 AR067997
6 · The paper itself

Abstract

Introduction: 3D Markerless motion capture technologies have advanced significantly over the last few decades to overcome limitations of marker-based systems, which require significant cost, time, and specialization. As markerless motion capture technologies develop and mature, there is increasing demand from the biomechanics community to provide kinematic and kinetic data with similar levels of reliability and accuracy as current reference standard marker-based 3D motion capture methods. The purpose of this study was to evaluate how a novel markerless system trained with both hand-labeled and synthetic data compares to lower extremity kinematic and kinetic measurements from a reference marker-based system during the drop vertical jump (DVJ) task. Methods: Synchronized video data from multiple camera views and marker-based data were simultaneously collected from 127 participants performing three repetitions of the DVJ. Lower limb joint angles and joint moments were calculated and compared between the markerless and marker-based systems. Root mean squared error values and Pearson correlation coefficients were used to quantify agreement between the systems. Results: Root mean squared error values of lower limb joint angles and joint moments were ≤ 9.61 degrees and ≤ 0.23 N×m/kg, respectively. Pearson correlation values between markered and markerless systems were 0.67-0.98 hip, 0.45-0.99 knee and 0.06-0.99 ankle for joint kinematics. Likewise, Pearson correlation values were 0.73-0.90 hip, 0.61-0.95 knee and 0.74-0.95 ankle for joint kinetics. Discussion: These results highlight the promising potential of markerless motion capture, particularly for measures of hip, knee and ankle rotations. Further research is needed to evaluate the viability of markerless ankle measures in the frontal plane to determine if differences in joint solvers are inducing unanticipated error.

Indexed as

biomechanicsdrop jumpmarkerless motion capturesynthetic datavalidation

Identifiers

PMID39512656
PMCPMC11540714

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.