A low cost real-time motion tracking approach using webcam technology

被引:48
作者
Krishnan, Chandramouli [1 ]
Washabaugh, Edward P. [1 ]
Seetharaman, Yogesh [1 ]
机构
[1] Univ Michigan, Sch Med, Dept Phys Med & Rehabil, Ann Arbor, MI 48108 USA
基金
美国国家卫生研究院;
关键词
Real time; Motion tracking; Motor learning; Gait; Kinematics; INERTIAL MEASUREMENT UNIT; MICROSOFT KINECT; STROKE SURVIVOR; GAIT; REHABILITATION; VALIDITY; RELIABILITY; ACCURACY; ORTHOSIS; LOKOMAT;
D O I
10.1016/j.jbiomech.2014.11.048
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
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. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:544 / 548
页数:5
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