Saner Robert J, Washabaugh Edward P, Krishnan Chandramouli
Department of Physical Medicine and Rehabilitation, University of Michigan Medical School, Ann Arbor, MI, USA; School of Kinesiology, University of Michigan, Ann Arbor, MI, USA.
Department of Physical Medicine and Rehabilitation, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
Gait Posture. 2017 Jul;56:19-23. doi: 10.1016/j.gaitpost.2017.04.030. Epub 2017 Apr 30.
Three-dimensional (3-D) motion capture systems are commonly used for gait analysis because they provide reliable and accurate measurements. However, the downside of this approach is that it is expensive and requires technical expertise; thus making it less feasible in the clinic. To address this limitation, we recently developed and validated (using a high-precision walking robot) a low-cost, two-dimensional (2-D) real-time motion tracking approach using a simple webcam and LabVIEW Vision Assistant. The purpose of this study was to establish the repeatability and minimal detectable change values of hip and knee sagittal plane gait kinematics recorded using this system. Twenty-one healthy subjects underwent two kinematic assessments while walking on a treadmill at a range of gait velocities. Intraclass correlation coefficients (ICC) and minimal detectable change (MDC) values were calculated for commonly used hip and knee kinematic parameters to demonstrate the reliability of the system. Additionally, Bland-Altman plots were generated to examine the agreement between the measurements recorded on two different days. The system demonstrated good to excellent reliability (ICC>0.75) for all the gait parameters tested on this study. The MDC values were typically low (<5°) for most of the parameters. The Bland-Altman plots indicated that there was no systematic error or bias in kinematic measurements and showed good agreement between measurements obtained on two different days. These results indicate that kinematic gait assessments using webcam technology can be reliably used for clinical and research purposes.
三维（3-D）运动捕捉系统常用于步态分析，因为它们能提供可靠且准确的测量结果。然而，这种方法的缺点是成本高昂且需要专业技术知识，因此在临床应用中可行性较低。为解决这一局限性，我们最近开发并验证了（使用高精度步行机器人）一种低成本的二维（2-D）实时运动跟踪方法，该方法使用简单的网络摄像头和LabVIEW视觉助手。本研究的目的是确定使用该系统记录的髋部和膝部矢状面步态运动学的重复性和最小可检测变化值。21名健康受试者在跑步机上以一系列步态速度行走时接受了两次运动学评估。计算常用髋部和膝部运动学参数的组内相关系数（ICC）和最小可检测变化（MDC）值，以证明该系统的可靠性。此外，还生成了Bland-Altman图，以检查在两天记录的测量值之间的一致性。该系统在本研究中测试的所有步态参数上均显示出良好到优秀的可靠性（ICC>0.75）。大多数参数的MDC值通常较低（<5°）。Bland-Altman图表明运动学测量中没有系统误差或偏差，并且在两天获得的测量值之间显示出良好的一致性。这些结果表明，使用网络摄像头技术进行的运动学步态评估可可靠地用于临床和研究目的。
