Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard: A Pilot Study.
Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard: A Pilot Study.
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DOI：
10.3390/s20185104
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发表时间：
2020-09-08
期刊：
影响因子：
--
通讯作者：
Arnrich B
中科院分区：
文献类型：
--
作者：
Albert JA;Owolabi V;Gebel A;Brahms CM;Granacher U;Arnrich B
步态分析是早期发现神经系统疾病和评估老年人跌倒风险的重要工具。当今市场上低成本摄像头硬件的可用性以及机器学习的最新进展，使广泛的临床和健康相关应用成为可能，例如患者监测或在家运动识别。在这项研究中，我们评估了最新一代微软Kinect摄像头Azure Kinect的运动跟踪性能，与其前身Kinect v2相比，使用黄金标准的Vicon多摄像头运动捕捉系统和39标记插件步态模型在跑步机上行走。五名年轻健康的受试者以三种不同的速度在跑步机上行走，同时用所有三种相机系统记录数据。一个易于管理的相机校准方法，这里开发的空间对齐的3D骨架数据从Kinect相机和Vicon系统。通过该校准，评估了两个Kinect相机和参考系统之间的关节位置的空间一致性。此外，我们比较了某些时空步态参数的准确性，即，步长，步时间，步宽，和步幅时间从Kinect数据计算，与黄金标准系统。我们的研究结果表明，Azure Kinect摄像头的改进硬件和运动跟踪算法导致空间步态参数的准确性明显高于前一代Kinect v2，而时间参数之间没有显着差异。此外，我们详细解释了如何使用这个实验装置来连续监测老年人步态康复过程中的进展。
Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people.
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DOI：
10.3390/s140203362
发表时间：
2014-02-19
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Muro-de-la-Herran A;Garcia-Zapirain B;Mendez-Zorrilla A
通讯作者：
Mendez-Zorrilla A
影响因子：
2.4
作者：
Eltoukhy, Moataz;Oh, Jeonghoon;Signorile, Joseph
通讯作者：
Signorile, Joseph
影响因子：
2.4
作者：
Asaeda, Makoto;Kuwahara, Wataru;Adachi, Nobuo
通讯作者：
Adachi, Nobuo
影响因子：
2.4
作者：
Clark, Ross A.;Pua, Yong-Hao;Bryant, Adam L.
通讯作者：
Bryant, Adam L.
影响因子：
3.7
作者：
Otte K;Kayser B;Mansow-Model S;Verrel J;Paul F;Brandt AU;Schmitz-Hübsch T
通讯作者：
Schmitz-Hübsch T