Two-dimensional video-based analysis of human gait using pose estimation.
Two-dimensional video-based analysis of human gait using pose estimation.
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DOI：
10.1371/journal.pcbi.1008935
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发表时间：
2021-04
影响因子：
4.3
通讯作者：
Roemmich RT
中科院分区：
文献类型：
--
作者：
Stenum J;Rossi C;Roemmich RT
人体步态分析通常在临床和基础研究中进行，但许多常见的方法(如三维运动捕捉、可穿戴设备)昂贵、固定、数据有限且需要专业知识。基于视频的姿势估计的最新进展表明，使用从容易获取的设备(例如，智能手机)收集的二维视频进行步态分析具有潜在的潜力。到目前为止，已经有几个研究使用无标记姿势估计来提取人体步态特征。然而，我们目前缺乏对基于视频的方法的评估，该方法使用人体步态数据集来逐步地针对广泛的步态参数进行评估，并且缺乏从视频执行步态分析的工作流程。在这里，我们将OpenPose(基于开源视频的人体姿势估计)测量的时空和矢状面运动学步态参数与同时记录的健康成年人地面行走的三维运动捕获进行比较。在评估竞走比赛中的所有个人步数时，我们观察到在时间步态参数(即步态时间、站立时间、摆动时间和双支撑时间)方面，动作捕捉与OpenPose之间的平均绝对误差为0.02 S，步长为0.049 m。当时空步态参数计算为个体参与者平均值时，精度提高：时间步态参数的平均绝对误差为0.01S，步长的平均绝对误差为0.018米。动作捕捉与OpenPose的最大步速差小于0.10m S−1。动作捕捉与OpenPose的髋关节、膝关节和踝关节矢状面角度的平均绝对误差分别为4.0°、5.6°和7.4°。我们的分析工作流程是免费提供的，只需要最少的用户输入，并且不需要事先的步态分析专业知识。最后，对姿态估计在人体步态分析中的应用提出了建议和思考。临床医生和研究人员越来越感兴趣的是使用新的姿势估计算法来自动跟踪人体运动来分析人体步态。步态分析通常在有专门设备的指定实验室进行。另一方面，姿势估计依赖于可以从智能手机等家用设备上录制的数字视频。因此，这些新技术使人们有可能走出实验室，在家庭或诊所等其他环境中进行步态分析。在采用这些技术之前，我们确定了将结果参数与三维运动捕捉进行比较的迫切需要，并评估摄像机视点如何影响结果参数。我们使用了健康人体的同步运动捕捉和左右两侧的视频记录，并计算了时空步态参数和下肢关节角度。我们发现，我们提供的工作流估计时空步态参数以及臀部和膝部角度，具有检测步态模式变化所需的准确性和精确度。我们论证了参与者相对于摄像机的位置影响诸如步长等空间测量，并讨论了当前方法的局限性。
Human gait analysis is often conducted in clinical and basic research, but many common approaches (e.g., three-dimensional motion capture, wearables) are expensive, immobile, data-limited, and require expertise. Recent advances in video-based pose estimation suggest potential for gait analysis using two-dimensional video collected from readily accessible devices (e.g., smartphones). To date, several studies have extracted features of human gait using markerless pose estimation. However, we currently lack evaluation of video-based approaches using a dataset of human gait for a wide range of gait parameters on a stride-by-stride basis and a workflow for performing gait analysis from video. Here, we compared spatiotemporal and sagittal kinematic gait parameters measured with OpenPose (open-source video-based human pose estimation) against simultaneously recorded three-dimensional motion capture from overground walking of healthy adults. When assessing all individual steps in the walking bouts, we observed mean absolute errors between motion capture and OpenPose of 0.02 s for temporal gait parameters (i.e., step time, stance time, swing time and double support time) and 0.049 m for step lengths. Accuracy improved when spatiotemporal gait parameters were calculated as individual participant mean values: mean absolute error was 0.01 s for temporal gait parameters and 0.018 m for step lengths. The greatest difference in gait speed between motion capture and OpenPose was less than 0.10 m s−1. Mean absolute error of sagittal plane hip, knee and ankle angles between motion capture and OpenPose were 4.0°, 5.6° and 7.4°. Our analysis workflow is freely available, involves minimal user input, and does not require prior gait analysis expertise. Finally, we offer suggestions and considerations for future applications of pose estimation for human gait analysis. There is a growing interest among clinicians and researchers to use novel pose estimation algorithms that automatically track human movement to analyze human gait. Gait analysis is routinely conducted in designated laboratories with specialized equipment. On the other hand, pose estimation relies on digital videos that can be recorded from household devices such as a smartphone. As a result, these new techniques make it possible to move beyond the laboratory and perform gait analysis in other settings such as the home or clinic. Before such techniques are adopted, we identify a critical need for comparing outcome parameters against three-dimensional motion capture and to evaluate how camera viewpoint affect outcome parameters. We used simultaneous motion capture and left- and right-side video recordings of healthy human gait and calculated spatiotemporal gait parameters and lower-limb joint angles. We find that our provided workflow estimates spatiotemporal gait parameters together with hip and knee angles with the accuracy and precision needed to detect changes in the gait pattern. We demonstrate that the position of the participant relative to the camera affect spatial measures such as step length and discuss the limitations posed by the current approach.
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