Neurorehabilitation and Brain Research Group, Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain.
NEURORHB, Servicio de Neurorrehabilitación de Hospitales Vithas, Río Tajo 1, 46011, Valencia, Spain.
J Neuroeng Rehabil. 2019 Jul 26;16(1):97. doi: 10.1186/s12984-019-0568-y.
Gait is usually assessed by clinical tests, which may have poor accuracy and be biased, or instrumented systems, which potentially solve these limitations at the cost of being time-consuming and expensive. The different versions of the Microsoft Kinect have enabled human motion tracking without using wearable sensors at a low-cost and with acceptable reliability. This study aims: First, to determine the sensitivity of an open-access Kinect v2-based gait analysis system to motor disability and aging; Second, to determine its concurrent validity with standardized clinical tests in individuals with stroke; Third, to quantify its inter and intra-rater reliability, standard error of measurement, minimal detectable change; And, finally, to investigate its ability to identify fall risk after stroke.
The most widely used spatiotemporal and kinematic gait parameters of 82 individuals post-stroke and 355 healthy subjects were estimated with the Kinect v2-based system. In addition, participants with stroke were assessed with the Dynamic Gait Index, the 1-min Walking Test, and the 10-m Walking Test.
The system successfully characterized the performance of both groups. Significant concurrent validity with correlations of variable strength was detected between all clinical tests and gait measures. Excellent inter and intra-rater reliability was evidenced for almost all measures. Minimal detectable change was variable, with poorer results for kinematic parameters. Almost all gait parameters proved to identify fall risk.
Results suggest that although its limited sensitivity to kinematic parameters, the Kinect v2-based gait analysis could be used as a low-cost alternative to laboratory-grade systems to complement gait assessment in clinical settings.
步态通常通过临床测试进行评估,但这些测试可能准确性较差且存在偏差,或者使用仪器系统进行评估,但这些系统可能会解决这些限制,但代价是耗时且昂贵。不同版本的 Microsoft Kinect 无需使用可穿戴传感器即可以低成本和可接受的可靠性实现人体运动跟踪。本研究旨在:首先,确定基于开放访问的 Kinect v2 步态分析系统对运动障碍和衰老的敏感性;其次,确定其在中风患者中与标准化临床测试的同时效度;第三,量化其组内和组间可靠性、测量误差标准、最小可检测变化;最后,研究其在中风后识别跌倒风险的能力。
使用基于 Kinect v2 的系统估计了 82 名中风后个体和 355 名健康个体的最广泛使用的时空和运动学步态参数。此外,对中风患者进行了动态步态指数、1 分钟步行测试和 10 米步行测试评估。
该系统成功地描述了两组的表现。所有临床测试与步态测量之间都检测到了具有不同强度相关性的高度同时效度。几乎所有测量值都具有极好的组内和组间可靠性。最小可检测变化是可变的,运动学参数的结果较差。几乎所有步态参数都证明可以识别跌倒风险。
结果表明,尽管基于 Kinect v2 的步态分析系统对运动学参数的敏感性有限,但它可以作为实验室级系统的低成本替代方案,用于补充临床环境中的步态评估。