Kuster Roman P, Heinlein Bernd, Bauer Christoph M, Graf Eveline S
Zurich University of Applied Sciences, School of Engineering, Institute of Mechanical Systems, Biomechanical Engineering, Technikumstrasse 9, 8401 Winterthur, Switzerland.
Zurich University of Applied Sciences, School of Health Professions, Institute of Physiotherapy, Technikumstrasse 71, 8401 Winterthur, Switzerland.
Gait Posture. 2016 Jun;47:80-5. doi: 10.1016/j.gaitpost.2016.04.004. Epub 2016 Apr 13.
Motion analysis systems deliver quantitative information, e.g. on the progress of rehabilitation programs aimed at improving range of motion. Markerless systems are of interest for clinical application because they are low-cost and easy to use. The first generation of the Kinect™ sensor showed promising results in validity assessment compared to an established marker-based system. However, no literature is available on the validity of the new 'Kinect™ for Xbox one' (KinectOne) in tracking upper body motion. Consequently, this study was conducted to analyze the accuracy and reliability of the KinectOne in tracking upper body motion. Twenty subjects performed shoulder abduction in frontal and scapula plane, flexion, external rotation and horizontal flexion in two conditions (sitting and standing). Arm and trunk motion were analyzed using the KinectOne and compared to a marker-based system. Comparisons were made using Bland Altman statistics and Coefficient of Multiple Correlation. On average, differences between systems of 3.9±4.0° and 0.1±3.8° were found for arm and trunk motion, respectively. Correlation was higher for the arm than for the trunk motion. Based on the observed bias, the accuracy of the KinectOne was found to be adequate to measure arm motion in a clinical setting. Although trunk motion showed a very low absolute bias between the two systems, the KinectOne was not able to track small changes over time. Before the KinectOne can find clinical application, further research is required analyzing whether validity can be improved using a customized tracking algorithm or other sensor placement, and to analyze test-retest reliability.
运动分析系统可提供定量信息,例如关于旨在改善关节活动范围的康复计划进展情况的信息。无标记系统因其低成本且易于使用而在临床应用中受到关注。与已建立的基于标记的系统相比,第一代Kinect™传感器在有效性评估方面显示出有前景的结果。然而,尚无关于新型“Xbox one的Kinect™”(KinectOne)跟踪上身运动有效性的文献。因此,本研究旨在分析KinectOne跟踪上身运动的准确性和可靠性。20名受试者在两种条件下(坐姿和站姿)进行了肩部在额状面和肩胛面的外展、屈曲、外旋和水平屈曲动作。使用KinectOne分析手臂和躯干运动,并与基于标记的系统进行比较。使用Bland Altman统计和多重相关系数进行比较。平均而言,手臂和躯干运动在系统间的差异分别为3.9±4.0°和0.1±3.8°。手臂运动的相关性高于躯干运动。基于观察到的偏差,发现KinectOne的准确性足以在临床环境中测量手臂运动。尽管两个系统之间躯干运动的绝对偏差非常小,但KinectOne无法跟踪随时间的微小变化。在KinectOne能够找到临床应用之前,需要进一步研究分析是否可以使用定制的跟踪算法或其他传感器放置方式来提高有效性,并分析重测可靠性。