Literature DB >> 27618245

Two-Dimensional Video Analysis of Youth and Adolescent Pitching Biomechanics: A Tool For the Common Athlete.

Steven F DeFroda1, Charles A Thigpen, Peter K Kriz.   

Abstract

Three-dimensional (3D) motion analysis is the gold standard for analyzing the biomechanics of the baseball pitching motion. Historically, 3D analysis has been available primarily to elite athletes, requiring advanced cameras, and sophisticated facilities with expensive software. The advent of newer technology, and increased affordability of video recording devices, and smartphone/tablet-based applications has led to increased access to this technology for youth/amateur athletes and sports medicine professionals. Two-dimensional (2D) video analysis is an emerging tool for the kinematic assessment and observational measurement of pitching biomechanics. It is important for providers, coaches, and players to be aware of this technology, its application in identifying causes of arm pain and preventing injury, as well as its limitations. This review provides an in-depth assessment of 2D video analysis studies for pitching, a direct comparison of 2D video versus 3D motion analysis, and a practical introduction to assessing pitching biomechanics using 2D video analysis.

Entities:  

Mesh:

Year:  2016        PMID: 27618245     DOI: 10.1249/JSR.0000000000000295

Source DB:  PubMed          Journal:  Curr Sports Med Rep        ISSN: 1537-890X            Impact factor:   1.733


  3 in total

1.  Stride Length and Torso Biomechanics As They Relate To Medial Elbow Injuries In Adolescent Aged Baseball pitchers: A Clinical Commentary.

Authors:  Zachary Dietz; Dylan DeWeese; Neil Shaw; Cody Huth; Jacob Ball; Victoria Reeves; Ryan Monti; Ryan Bitzel
Journal:  Int J Sports Phys Ther       Date:  2022-06-01

2.  Reliability of an Observational Biomechanical Analysis Tool in Adolescent Baseball Pitchers.

Authors:  Steven F DeFroda; Dai Sugimoto; Steven J Staffa; Donald S Bae; Ellen Shanley; Charles A Thigpen; Peter K Kriz
Journal:  Int J Sports Phys Ther       Date:  2021-12-01

3.  Sports Video Athlete Detection Based on Associative Memory Neural Network.

Authors:  Jingwei Yang
Journal:  Comput Intell Neurosci       Date:  2022-02-15
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.