Liang Shengyun, Zhang Yu, Diao Yanan, Li Guanglin, Zhao Guoru
CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Research Center for Neural Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China.
Front Bioeng Biotechnol. 2022 Aug 10;10:857975. doi: 10.3389/fbioe.2022.857975. eCollection 2022.
Quantifying kinematic gait for elderly people is a key factor for consideration in evaluating their overall health. However, gait analysis is often performed in the laboratory using optical sensors combined with reflective markers, which may delay the detection of health problems. This study aims to develop a 3D markerless pose estimation system using OpenPose and 3DPoseNet algorithms. Moreover, 30 participants performed a walking task. Sample entropy was adopted to study dynamic signal irregularity degree for gait parameters. Paired-sample t-test and intra-class correlation coefficients were used to assess validity and reliability. Furthermore, the agreement between the data obtained by markerless and marker-based measurements was assessed by Bland-Altman analysis. ICC (C, 1) indicated the test-retest reliability within systems was in almost complete agreement. There were no significant differences between the sample entropy of knee angle and joint angles of the sagittal plane by the comparisons of joint angle results extracted from different systems ( > 0.05). ICC (A, 1) indicated the validity was substantial. This is supported by the Bland-Altman plot of the joint angles at maximum flexion. Optical motion capture and single-camera sensors were collected simultaneously, making it feasible to capture stride-to-stride variability. In addition, the sample entropy of angles was close to the ground_truth in the sagittal plane, indicating that our video analysis could be used as a quantitative assessment of gait, making outdoor applications feasible.
对老年人的运动步态进行量化是评估其整体健康状况时需要考虑的关键因素。然而，步态分析通常在实验室中使用光学传感器结合反光标记来进行，这可能会延迟对健康问题的检测。本研究旨在使用OpenPose和3DPoseNet算法开发一种三维无标记姿态估计系统。此外，30名参与者执行了一项行走任务。采用样本熵来研究步态参数的动态信号不规则程度。使用配对样本t检验和组内相关系数来评估有效性和可靠性。此外，通过Bland-Altman分析评估无标记测量和基于标记测量所获得数据之间的一致性。ICC（C，1）表明系统内的重测可靠性几乎完全一致。通过比较从不同系统提取的关节角度结果，膝关节角度和矢状面关节角度的样本熵之间没有显著差异（>0.05）。ICC（A，1）表明有效性很高。最大屈曲时关节角度的Bland-Altman图支持了这一点。同时采集了光学运动捕捉数据和单摄像头传感器数据，使得捕捉步幅间的变异性成为可能。此外，角度的样本熵在矢状面接近真实值，这表明我们的视频分析可用于步态的定量评估，使户外应用成为可能。
