A Deep Neural Network-based method for estimation of 3D lifting motions
A Deep Neural Network-based method for estimation of 3D lifting motions
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DOI：
10.1016/j.jbiomech.2018.12.022
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发表时间：
2019-02-14
影响因子：
2.4
通讯作者：
Li, Kang
中科院分区：
文献类型：
--
作者：
Mehrizi, Rahil;Peng, Xi;Li, Kang
本研究的目的是开发和验证一种基于深度神经网络(DNN)的提升过程中三维位姿估计方法。所提出的基于DNN的方法解决了基于标记的运动捕获系统存在的准备时间过长、运动障碍和受控环境要求等问题。12名健康成年人参加了一项方案，并执行了9项不同垂直高度和不对称角度的举重任务。他们抬起一个板条箱，把它放在架子上，同时由两个摄像机和一个同步动作捕捉系统拍摄，该系统直接测量他们的身体运动。设计了一种具有两级级联结构的DNN，用于从摄像机拍摄的图像中估计受试者的三维身体姿势。我们的DNN扩展沙漏网络用于单目2D位姿估计，该网络使用了一种新的3D位姿生成器子网络，该网络综合了所有可用视点的信息来预测准确的3D位姿。我们以基于标记的运动捕捉系统为参考，验证了结果，并测试了方法在不同提升条件下的性能。在整个数据集上，估计的3D姿势和参考之间的平均欧几里德距离(3D姿势误差)为14.72+/-2.96 mm。重复测量方差分析表明，举升条件会影响方法的性能，例如，60度不对称角度和肩高的举升与其他举升条件相比，显示出更高的三维位姿误差。实验结果表明，该方法能够在不受基于标记的运动捕获系统限制的情况下，实现高精度的三维位姿估计。该方法可作为一种现场生物力学分析工具。(C)2018爱思唯尔有限公司。保留所有权利。
The aim of this study is developing and validating a Deep Neural Network (DNN) based method for 3D pose estimation during lifting. The proposed DNN based method addresses problems associated with marker-based motion capture systems like excessive preparation time, movement obstruction, and controlled environment requirement. Twelve healthy adults participated in a protocol and performed nine lifting tasks with different vertical heights and asymmetry angles. They lifted a crate and placed it on a shelf while being filmed by two camcorders and a synchronized motion capture system, which directly measured their body movement. A DNN with two-stage cascaded structure was designed to estimate subjects' 3D body pose from images captured by camcorders. Our DNN augmented Hourglass network for monocular 2D pose estimation with a novel 3D pose generator subnetwork, which synthesized information from all available views to predict accurate 3D pose. We validated the results against the marker based motion capture system as a reference and examined the method performance under different lifting conditions. The average Euclidean distance between the estimated 3D pose and reference (3D pose error) on the whole dataset was 14.72 +/- 2.96 mm. Repeated measures ANOVAs showed lifting conditions can affect the method performance e.g. 60 degrees asymmetry angle and shoulder height lifting showed higher 3D pose error compare to other lifting conditions. The results demonstrated the capability of the proposed method for 3D pose estimation with high accuracy and without limitations of marker-based motion capture systems. The proposed method may be utilized as an on-site biomechanical analysis tool. (C) 2018 Elsevier Ltd. All rights reserved.