Eltoukhy Moataz, Oh Jeonghoon, Kuenze Christopher, Signorile Joseph
Department of Kinesiology and Sport Sciences, School of Education & Human Development, University of Miami, Coral Gables, FL 33143, USA.
Department of Kinesiology, School of Education, Michigan State University, East Lansing, MI 48824, USA.
Gait Posture. 2017 Jan;51:77-83. doi: 10.1016/j.gaitpost.2016.10.001. Epub 2016 Oct 4.
A cost-effective, clinician friendly gait assessment tool that can automatically track patients' anatomical landmarks can provide practitioners with important information that is useful in prescribing rehabilitative and preventive therapies. This study investigated the validity and reliability of the Microsoft Kinect v2 as a potential inexpensive gait analysis tool. Ten healthy subjects walked on a treadmill at 1.3 and 1.6m·s, as spatiotemporal parameters and kinematics were extracted concurrently using the Kinect and three-dimensional motion analysis. Spatiotemporal measures included step length and width, step and stride times, vertical and mediolateral pelvis motion, and foot swing velocity. Kinematic outcomes included hip, knee, and ankle joint angles in the sagittal plane. The absolute agreement and relative consistency between the two systems were assessed using interclass correlations coefficients (ICC2,1), while reproducibility between systems was established using Lin's Concordance Correlation Coefficient (rc). Comparison of ensemble curves and associated 90% confidence intervals (CI90) of the hip, knee, and ankle joint angles were performed to investigate if the Kinect sensor could consistently and accurately assess lower extremity joint motion throughout the gait cycle. Results showed that the Kinect v2 sensor has the potential to be an effective clinical assessment tool for sagittal plane knee and hip joint kinematics, as well as some spatiotemporal temporal variables including pelvis displacement and step characteristics during the gait cycle.
一种经济高效、对临床医生友好且能自动跟踪患者解剖标志点的步态评估工具，可为从业者提供重要信息，有助于制定康复和预防治疗方案。本研究调查了微软Kinect v2作为一种潜在的廉价步态分析工具的有效性和可靠性。10名健康受试者在跑步机上以1.3米/秒和1.6米/秒的速度行走，同时使用Kinect和三维运动分析提取时空参数和运动学数据。时空测量包括步长和步宽、步幅和步频、骨盆垂直和内外侧运动以及足部摆动速度。运动学结果包括矢状面内的髋、膝和踝关节角度。使用组内相关系数（ICC2,1）评估两个系统之间的绝对一致性和相对一致性，同时使用林氏一致性相关系数（rc）确定系统之间的再现性。对髋、膝和踝关节角度的整体曲线及相关90%置信区间（CI90）进行比较，以研究Kinect传感器在整个步态周期中能否持续、准确地评估下肢关节运动。结果表明，Kinect v2传感器有潜力成为一种有效的临床评估工具，用于评估矢状面内的膝关节和髋关节运动学，以及一些时空变量，包括步态周期中的骨盆位移和步幅特征。
