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Open AccessArticle

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

1
Connected Healthcare, Digital Health Center, Hasso Plattner Institute, University of Potsdam, 14482 Potsdam, Germany
2
Division of Training and Movement Sciences, University of Potsdam, 14469 Potsdam, Germany
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(18), 5104; https://doi.org/10.3390/s20185104
Received: 14 July 2020 / Revised: 1 September 2020 / Accepted: 3 September 2020 / Published: 8 September 2020
(This article belongs to the Special Issue Mobile Sensors for Healthcare)
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. View Full-Text
Keywords: motion capture; evaluation; human motion; RGB-D cameras; digital health motion capture; evaluation; human motion; RGB-D cameras; digital health
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MDPI and ACS Style

Albert, J.A.; Owolabi, V.; Gebel, A.; Brahms, C.M.; Granacher, U.; Arnrich, B. 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. Sensors 2020, 20, 5104.

AMA Style

Albert JA, Owolabi V, Gebel A, Brahms CM, Granacher U, Arnrich B. 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. Sensors. 2020; 20(18):5104.

Chicago/Turabian Style

Albert, Justin A.; Owolabi, Victor; Gebel, Arnd; Brahms, Clemens M.; Granacher, Urs; Arnrich, Bert. 2020. "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" Sensors 20, no. 18: 5104.

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