Literature DB >> 33480993

Validation of a Commercially Available Markerless Motion-Capture System for Trunk and Lower Extremity Kinematics During a Jump-Landing Assessment.

Timothy C Mauntel1,2, Kenneth L Cameron2,3,4, Brian Pietrosimone5, Stephen W Marshall5,6,7, Anthony C Hackney5,8, Darin A Padua5.   

Abstract

CONTEXT: Field-based, portable motion-capture systems can be used to help identify individuals at greater risk of lower extremity injury. Microsoft Kinect-based markerless motion-capture systems meet these requirements; however, until recently, these systems were generally not automated, required substantial data postprocessing, and were not commercially available.
OBJECTIVE: To validate the kinematic measures of a commercially available markerless motion-capture system.
DESIGN: Descriptive laboratory study.
SETTING: Laboratory. PATIENTS OR OTHER PARTICIPANTS: A total of 20 healthy, physically active university students (10 males, 10 females; age = 20.50 ± 2.78 years, height = 170.36 ± 9.82 cm, mass = 68.38 ± 10.07 kg, body mass index = 23.50 ± 2.40 kg/m2). INTERVENTION(S): Participants completed 5 jump-landing trials. Kinematic data were simultaneously recorded using Kinect-based markerless and stereophotogrammetric motion-capture systems. MAIN OUTCOME MEASURE(S): Sagittal- and frontal-plane trunk, hip-joint, and knee-joint angles were identified at initial ground contact of the jump landing (IC), for the maximum joint angle during the landing phase of the initial landing (MAX), and for the joint-angle displacement from IC to MAX (DSP). Outliers were removed, and data were averaged across trials. We used intraclass correlation coefficients (ICCs [2,1]) to assess intersystem reliability and the paired-samples t test to examine mean differences (α ≤ .05).
RESULTS: Agreement existed between the systems (ICC range = -1.52 to 0.96; ICC average = 0.58), with 75.00% (n = 24/32) of the measures being validated (P ≤ .05). Agreement was better for sagittal- (ICC average = 0.84) than frontal-plane (ICC average = 0.35) measures. Agreement was best for MAX (ICC average = 0.77) compared with IC (ICC average = 0.56) and DSP (ICC average = 0.41) measures. Pairwise comparisons identified differences for 18.75% (6/32) of the measures. Fewer differences were observed for sagittal- (0.00%; 0/15) than for frontal-plane (35.29%; 6/17) measures. Between-systems differences were equivalent for MAX (18.18%; 2/11), DSP (18.18%; 2/11), and IC measures (20.00%; 2/10). The markerless system underestimated sagittal-plane measures (86.67%; 13/15) and overestimated frontal-plane measures (76.47%; 13/17). No trends were observed for overestimating or underestimating IC, MAX, or DSP measures.
CONCLUSIONS: Moderate agreement existed between markerless and stereophotogrammetric motion-capture systems. Better agreement existed for larger (eg, sagittal plane, MAX) than for smaller (eg, frontal plane, IC) joint angles. The DSP angles had the worst agreement. Markerless motion-capture systems may help clinicians identify individuals at greater risk of lower extremity injury. © by the National Athletic Trainers' Association, Inc.

Entities:  

Keywords:  biomechanics; injury screening; motion analysis; movement assessment

Year:  2021        PMID: 33480993      PMCID: PMC7901583          DOI: 10.4085/1062-6050-0023.20

Source DB:  PubMed          Journal:  J Athl Train        ISSN: 1062-6050            Impact factor:   2.860


  21 in total

1.  Validity of the Microsoft Kinect for assessment of postural control.

Authors:  Ross A Clark; Yong-Hao Pua; Karine Fortin; Callan Ritchie; Kate E Webster; Linda Denehy; Adam L Bryant
Journal:  Gait Posture       Date:  2012-05-23       Impact factor: 2.840

Review 2.  Do runners who suffer injuries have higher vertical ground reaction forces than those who remain injury-free? A systematic review and meta-analysis.

Authors:  Henk van der Worp; Jelte W Vrielink; Steef W Bredeweg
Journal:  Br J Sports Med       Date:  2016-01-04       Impact factor: 13.800

3.  Biomechanical measures of neuromuscular control and valgus loading of the knee predict anterior cruciate ligament injury risk in female athletes: a prospective study.

Authors:  Timothy E Hewett; Gregory D Myer; Kevin R Ford; Robert S Heidt; Angelo J Colosimo; Scott G McLean; Antonie J van den Bogert; Mark V Paterno; Paul Succop
Journal:  Am J Sports Med       Date:  2005-02-08       Impact factor: 6.202

4.  Validity and reliability of the Kinect within functional assessment activities: comparison with standard stereophotogrammetry.

Authors:  B Bonnechère; B Jansen; P Salvia; H Bouzahouene; L Omelina; F Moiseev; V Sholukha; J Cornelis; M Rooze; S Van Sint Jan
Journal:  Gait Posture       Date:  2013-10-05       Impact factor: 2.840

5.  Reliability and concurrent validity of the Microsoft Xbox One Kinect for assessment of standing balance and postural control.

Authors:  Ross A Clark; Yong-Hao Pua; Cristino C Oliveira; Kelly J Bower; Shamala Thilarajah; Rebekah McGaw; Ksaniel Hasanki; Benjamin F Mentiplay
Journal:  Gait Posture       Date:  2015-04-20       Impact factor: 2.840

6.  Kinect-based assessment of lower limb kinematics and dynamic postural control during the star excursion balance test.

Authors:  Moataz Eltoukhy; Christopher Kuenze; Jeonghoon Oh; Savannah Wooten; Joseph Signorile
Journal:  Gait Posture       Date:  2017-09-09       Impact factor: 2.840

7.  The Landing Error Scoring System as a Screening Tool for an Anterior Cruciate Ligament Injury-Prevention Program in Elite-Youth Soccer Athletes.

Authors:  Darin A Padua; Lindsay J DiStefano; Anthony I Beutler; Sarah J de la Motte; Michael J DiStefano; Steven W Marshall
Journal:  J Athl Train       Date:  2015-03-26       Impact factor: 2.860

8.  Landing Technique and Performance in Youth Athletes After a Single Injury-Prevention Program Session.

Authors:  Hayley Root; Thomas Trojian; Jessica Martinez; William Kraemer; Lindsay J DiStefano
Journal:  J Athl Train       Date:  2015-11-02       Impact factor: 2.860

9.  Dual Kinect v2 system can capture lower limb kinematics reasonably well in a clinical setting: concurrent validity of a dual camera markerless motion capture system in professional football players.

Authors:  Argyro Kotsifaki; Rodney Whiteley; Clint Hansen
Journal:  BMJ Open Sport Exerc Med       Date:  2018-12-17

10.  Multicenter trial of motion analysis for injury risk prediction: lessons learned from prospective longitudinal large cohort combined biomechanical - epidemiological studies.

Authors:  Timothy E Hewett; Benjamin Roewer; Kevin Ford; Greg Myer
Journal:  Braz J Phys Ther       Date:  2015-10-06       Impact factor: 3.377

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  4 in total

1.  Automated Landing Error Scoring System Performance and the Risk of Bone Stress Injury in Military Trainees.

Authors:  Timothy G Eckard; Story F P Miraldi; Karen Y Peck; Matthew A Posner; Steven J Svoboda; Lindsay J DiStefano; Darin A Padua; Stephen W Marshall; Kenneth L Cameron
Journal:  J Athl Train       Date:  2022-04-01       Impact factor: 3.824

2.  The Relationship between Landing Error Scoring System Performance and Injury in Female Collegiate Athletes.

Authors:  Peter Lisman; Joshua N Wilder; Joshua Berenbach; Enric Jiao; Bethany Hansberger
Journal:  Int J Sports Phys Ther       Date:  2021-12-01

Review 3.  A SWOT Analysis of Portable and Low-Cost Markerless Motion Capture Systems to Assess Lower-Limb Musculoskeletal Kinematics in Sport.

Authors:  Cortney Armitano-Lago; Dominic Willoughby; Adam W Kiefer
Journal:  Front Sports Act Living       Date:  2022-01-25

4.  Validity of the frame subtraction method in dynamic postural stability.

Authors:  Megumi Ota; Hiroshige Tateuchi; Takaya Hashiguchi; Karen Fujiwara; Ayano Sasaki; Kiseki Okumura; Noriaki Ichihashi
Journal:  BMC Sports Sci Med Rehabil       Date:  2022-09-26
  4 in total

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