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On the Accuracy of Low-cost Motion Capture Systems for Range of Motion Measurements

机译：低成本运动捕捉系统在运动测量范围内的精度

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In this paper, we study low-cost motion tracking systems for range of motion (RoM) measurements in the tele-rehabilitation context using Augmented Reality. We propose simple yet effective extensions of the Microsoft Kinect SDK 2.0 skeleton tracking algorithm. Our extensions consist of temporal smoothing of the joint estimates as well as an intuitive, patient-specific adjustment of the bone lengths that is implemented as a quick, one-time calibration performed by the therapist.We compare our system to the Kinect vl, the non-modified Kinect v2, a marker-based optical tracking system,and the clinical gold standard set by two subject-matter-experts using a goniometer. We study the accuracy of all systems in RoM measurement on the elbow joints. We quantitatively compare angular deviation from the expert measurements and perform analysis on statistical confidence.The results indicate, that the proposed personalized setup substantially outperforms all competing systems and effectively corrects for the systematic error of the skeleton tracking, particularly at full flexion. The improved system matched the observations of both experts with a mean error of 3.78° We conclude, that the proposed, personalized method for RoM measurement with Augmented Reality feedback is promising for tele-rehabilitation scenarios. Future work will investigate whether similar strategies can be applied to more complex joints, such as the shoulder.
机译：在本文中，我们研究了使用增强现实技术在远程康复环境中用于运动范围（RoM）测量的低成本运动跟踪系统。我们提出了Microsoft Kinect SDK 2.0骨架跟踪算法的简单而有效的扩展。我们的扩展包括关节估计的时间平滑以及对患者骨长度的直观，特定于患者的调整，该调整由治疗师进行的一次性快速校准实现。我们将我们的系统与Kinect vl，未修改的Kinect v2（一种基于标记的光学跟踪系统），以及由两名主题专家使用测角计设置的临床金标准。我们研究了肘关节RoM测量中所有系统的准确性。我们定量地比较了专家测量的角度偏差并进行了统计置信度分析。结果表明，所提出的个性化设置明显优于所有竞争系统，并有效地校正了骨骼跟踪的系统误差，尤其是在完全屈曲时。改进后的系统使两位专家的观察结果相符，平均误差为3.78°。我们得出结论，所提出的具有增强现实反馈的RoM测量个性化方法对于远程康复方案很有希望。未来的工作将研究类似的策略是否可以应用于更复杂的关节，例如肩膀。

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