首页> 外文期刊>Neural Systems and Rehabilitation Engineering, IEEE Transactions on >Validation of a Method for Real Time Foot Position and Orientation Tracking With Microsoft Kinect Technology for Use in Virtual Reality and Treadmill Based Gait Training Programs
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Validation of a Method for Real Time Foot Position and Orientation Tracking With Microsoft Kinect Technology for Use in Virtual Reality and Treadmill Based Gait Training Programs

机译:使用Microsoft Kinect技术验证用于虚拟现实和基于跑步机的步态训练程序中的实时脚部位置和方向跟踪的方法的验证

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摘要

The use of virtual reality for the provision of motor-cognitive gait training has been shown to be effective for a variety of patient populations. The interaction between the user and the virtual environment is achieved by tracking the motion of the body parts and replicating it in the virtual environment in real time. In this paper, we present the validation of a novel method for tracking foot position and orientation in real time, based on the Microsoft Kinect technology, to be used for gait training combined with virtual reality. The validation of the motion tracking method was performed by comparing the tracking performance of the new system against a stereo-photogrammetric system used as gold standard. Foot position errors were in the order of a few millimeters (average $RMSD$ from 4.9 to 12.1 mm in the medio–lateral and vertical directions, from 19.4 to 26.5 mm in the anterior-posterior direction); the foot orientation errors were also small (average $%RMSD$ from 5.6% to 8.8% in the medio–lateral and vertical directions, from 15.5% to 18.6% in the anterior–posterior direction). The results suggest that the proposed method can be effectively used to track feet motion in virtual reality and treadmill-based gait training programs.
机译:使用虚拟现实提供运动认知步态训练已被证明对多种患者有效。用户和虚拟环境之间的交互是通过跟踪身体部位的运动并将其实时复制到虚拟环境中来实现的。在本文中,我们基于Microsoft Kinect技术提出了一种用于实时跟踪脚的位置和方向的新颖方法的验证,该方法可用于结合虚拟现实的步态训练。通过将新系统的跟踪性能与用作黄金标准的立体摄影测量系统进行比较,来进行运动跟踪方法的验证。足部位置误差约为几毫米(在中外侧,平均 $ RMSD $ 从4.9到12.1 mm垂直方向,前后方向为19.4至26.5毫米);脚的方向误差也很小(在中外侧和垂直方向平均 $%RMSD $ 从5.6%到8.8%方向,前后方向从15.5%到18.6%)。结果表明,该方法可有效地跟踪虚拟现实和基于跑步机的步态训练程序中的脚部运动。

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