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

机译:基于Kinect的下肢运动学和动态姿势控制的基于Kinect的评估

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摘要

Abstract Assessments using dynamic postural control tests, like the Star Excursion Balance Test (SEBT), in combination with three-dimensional (3D) motion analysis can yield critical information regarding a subject’s lower limb movement patterns. 3D analysis can provide a clear understanding of the mechanisms that lead to specific outcome measures on the SEBT. Currently, the only technology for 3D motion analysis during such tests is expensive marker-based motion analysis systems, which are impractical for use in clinical settings. In this study we validated the use of the Microsoft Kinect as a cost-effective and marker-less alternative to more complex and expensive gold-standard motion analysis systems. Ten healthy subjects performed the SEBT while their lower limb kinematics were measured concurrently using a traditional motion capture system and a single Kinect v2 sensor. Analyses revealed errors in lower limb kinematics of less than 5°, except for the knee frontal-plane angle (5.7°) in the posterior-lateral direction. Ensemble curve analyses supported these findings, showing minimal between-system differences in all directions. Additionally, we found that the Kinect displayed excellent agreement (ICC 3,k =0.99) and consistency (ICC 2,k =0.99) when assessing reach distances in all directions. These results indicate that this low-cost and easy to implement technology may provide to clinicians a simple tool to simultaneously assess reach distances while developing a clearer understanding of the lower extremity movement patterns associated with SEBT performance in healthy and injured populations.
机译:摘要使用动态姿势控制测试的评估,如星形偏移平衡测试(SEBT),与三维(3D)运动分析组合可以产生关于受试者的下肢运动模式的关键信息。 3D分析可以清楚地了解导致SEBT的特定结果措施的机制。目前,这种测试期间的3D运动分析的唯一技术是昂贵的基于标记的运动分析系统,这对于临床环境中使用不切实际。在本研究中,我们验证了Microsoft Kinect的使用作为更复杂和昂贵的金标运动分析系统的成本效益和标记的替代方案。十个健康的受试者在使用传统的运动捕获系统和单个Kinect V2传感器上同时测量其下肢运动学时进行SEBT。除侧向方向上的膝关节额平面(5.7°)外,分析显示小于5°的下肢运动学中的误差。合奏曲线分析支持这些调查结果,显示了所有方向的系统之间的最小差异。此外,我们发现,当评估所有方向的距离时,Kinect显示出优秀的协议(ICC 3,K = 0.99)和一致性(ICC 2,K = 0.99)。这些结果表明,这种低成本且易于实现的技术可以向临床医生提供一个简单的工具,同时评估距离的距离,同时开发更清楚地了解与健康和受伤的人群中的STBT性能相关的下肢运动模式。

著录项

  • 来源
    《Gait & posture》 |2017年第2017期|共7页
  • 作者单位

    Department of Kinesiology and Sport Sciences School of Education &

    Human Development University;

    Department of Kinesiology School of Education Michigan State University;

    Department of Kinesiology and Sport Sciences School of Education &

    Human Development University;

    Department of Kinesiology and Sport Sciences School of Education &

    Human Development University;

    Department of Kinesiology and Sport Sciences School of Education &

    Human Development University;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 人体形态学;
  • 关键词

    Postural control; SEBT; Microsoft kinect;

    机译:姿势控制;SEBT;Microsoft Kinect;

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