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通过创新的基于视频的方法进行定量和定性跑步步态分析。

Quantitative and Qualitative Running Gait Analysis through an Innovative Video-Based Approach.

机构信息

Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37129 Verona, Italy.

IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143 Florence, Italy.

出版信息

Sensors (Basel). 2021 Apr 23;21(9):2977. doi: 10.3390/s21092977.

Abstract

Quantitative and qualitative running gait analysis allows the early identification and the longitudinal monitoring of gait abnormalities linked to running-related injuries. A promising calibration- and marker-less video sensor-based technology (i.e., ), recently validated for walking gait, may also offer a time- and cost-efficient alternative to the gold-standard methods for running. This study aim was to ascertain the validity of an improved version of Graal for quantitative and qualitative analysis of running. In 33 healthy recreational runners (mean age 41 years), treadmill running at self-selected submaximal speed was simultaneously evaluated by a validated photosensor system (i.e., -the reference methodology) and by the video analysis of a posterior 30-fps video of the runner through the optimized version of Graal. Graal is video analysis software that provides a spectral analysis of the brightness over time for each pixel of the video, in order to identify its frequency contents. The two main frequencies of variation of the pixel's brightness (i.e., F1 and F2) correspond to the two most important frequencies of gait (i.e., stride frequency and cadence). The Optogait system recorded step length, cadence, and its variability (vCAD, a traditional index of gait quality). Graal provided a direct measurement of F2 (reflecting cadence), an indirect measure of step length, and two indexes of global gait quality (harmony and synchrony index). The correspondence between quantitative indexes (Cadence vs. F2 and step length vs. Graal step length) was tested via paired t-test, correlations, and Bland-Altman plots. The relationship between qualitative indexes (vCAD vs. Harmony and Synchrony Index) was investigated by correlation analysis. Cadence and step length were, respectively, not significantly different from and highly correlated with F2 (1.41 Hz ± 0.09 Hz vs. 1.42 Hz ± 0.08 Hz, = 0.25, r = 0.81) and Graal step length (104.70 cm ± 013.27 cm vs. 107.56 cm ± 13.67 cm, = 0.55, r = 0.98). Bland-Altman tests confirmed a non-significant bias and small imprecision between methods for both parameters. The vCAD was 1.84% ± 0.66%, and it was significantly correlated with neither the Harmony nor the Synchrony Index (0.21 ± 0.03, = 0.92, r = 0.00038; 0.21 ± 0.96, = 0.87, r = 0.00122). These findings confirm the validity of the optimized version of Graal for the measurement of quantitative indexes of gait. Hence, Graal constitutes an extremely time- and cost-efficient tool suitable for quantitative analysis of running. However, its validity for qualitative running gait analysis remains inconclusive and will require further evaluation in a wider range of absolute and relative running intensities in different individuals.

摘要

定量和定性跑步步态分析可早期识别与跑步相关损伤相关的步态异常,并进行纵向监测。一种有前景的基于无校准和无标记视频传感器的技术(即),最近已在步行步态中得到验证,也可能为跑步的金标准方法提供一种更省时、更经济高效的替代方法。本研究旨在确定 Graal 新版本在定量和定性跑步分析中的有效性。在 33 名健康的娱乐跑者(平均年龄 41 岁)中,以自我选择的亚最大速度在跑步机上跑步,同时使用经过验证的光电传感器系统(即参考方法)和通过 Graal 的优化版本对跑步者的 30 fps 后向视频进行视频分析进行评估。Graal 是一种视频分析软件,它对视频中每个像素的亮度随时间的变化进行频谱分析,以识别其频率内容。像素亮度的两个主要变化频率(即 F1 和 F2)对应于步态的两个最重要频率(即步频和步频)。Optogait 系统记录步长、步频及其变异性(vCAD,步态质量的传统指标)。Graal 提供了 F2(反映步频)的直接测量值、步长的间接测量值以及两个整体步态质量指数(和谐指数和同步指数)。通过配对 t 检验、相关性和 Bland-Altman 图测试定量指标(步频与 F2 和步长与 Graal 步长)之间的一致性。通过相关分析研究定性指标(vCAD 与和谐指数和同步指数)之间的关系。步频和步长分别与 F2(1.41 Hz ± 0.09 Hz 与 1.42 Hz ± 0.08 Hz,= 0.25,r = 0.81)和 Graal 步长(104.70 cm ± 013.27 cm 与 107.56 cm ± 13.67 cm,= 0.55,r = 0.98)无显著差异且高度相关。Bland-Altman 检验证实两种方法在两种参数之间均无显著偏差和较小的不精确性。vCAD 为 1.84%±0.66%,与和谐指数或同步指数均无显著相关性(0.21±0.03,=0.92,r=0.00038;0.21±0.96,=0.87,r=0.00122)。这些发现证实了 Graal 新版本测量步态定量指标的有效性。因此,Graal 是一种非常节省时间和成本的工具,适用于跑步的定量分析。然而,其在定性跑步步态分析中的有效性仍不确定,需要在不同个体的更广泛的绝对和相对跑步强度范围内进一步评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8ea/8123008/ae85cdc379cc/sensors-21-02977-g001.jpg

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