Krishnan Chandramouli, Washabaugh Edward P, Seetharaman Yogesh
Department of Physical Medicine and Rehabilitation, University of Michigan Medical School, Ann Arbor, MI, USA.
Department of Physical Medicine and Rehabilitation, University of Michigan Medical School, Ann Arbor, MI, USA.
J Biomech. 2015 Feb 5;48(3):544-8. doi: 10.1016/j.jbiomech.2014.11.048. Epub 2014 Dec 10.
Physical therapy is an important component of gait recovery for individuals with locomotor dysfunction. There is a growing body of evidence that suggests that incorporating a motor learning task through visual feedback of movement trajectory is a useful approach to facilitate therapeutic outcomes. Visual feedback is typically provided by recording the subject's limb movement patterns using a three-dimensional motion capture system and displaying it in real-time using customized software. However, this approach can seldom be used in the clinic because of the technical expertise required to operate this device and the cost involved in procuring a three-dimensional motion capture system. In this paper, we describe a low cost two-dimensional real-time motion tracking approach using a simple webcam and an image processing algorithm in LabVIEW Vision Assistant. We also evaluated the accuracy of this approach using a high precision robotic device (Lokomat) across various walking speeds. Further, the reliability and feasibility of real-time motion-tracking were evaluated in healthy human participants. The results indicated that the measurements from the webcam tracking approach were reliable and accurate. Experiments on human subjects also showed that participants could utilize the real-time kinematic feedback generated from this device to successfully perform a motor learning task while walking on a treadmill. These findings suggest that the webcam motion tracking approach is a feasible low cost solution to perform real-time movement analysis and training.
物理治疗是运动功能障碍患者步态恢复的重要组成部分。越来越多的证据表明，通过运动轨迹的视觉反馈纳入运动学习任务是促进治疗效果的一种有用方法。视觉反馈通常通过使用三维运动捕捉系统记录受试者的肢体运动模式，并使用定制软件实时显示来提供。然而，由于操作该设备所需的技术专长以及采购三维运动捕捉系统的成本，这种方法在临床中很少使用。在本文中，我们描述了一种使用简单网络摄像头和LabVIEW视觉助手图像处理算法的低成本二维实时运动跟踪方法。我们还使用高精度机器人设备（Lokomat）在不同步行速度下评估了这种方法的准确性。此外，在健康人类参与者中评估了实时运动跟踪的可靠性和可行性。结果表明，网络摄像头跟踪方法的测量结果可靠且准确。对人类受试者的实验还表明，参与者可以利用该设备生成的实时运动学反馈在跑步机上行走时成功执行运动学习任务。这些发现表明，网络摄像头运动跟踪方法是进行实时运动分析和训练的一种可行的低成本解决方案。
