Faculty of Applied Engineering Department Electromechanics, Universiteit Antwerpen, Groenenborgerlaan 171, 2020 Antwerpen, Belgium.
Sensors (Basel). 2022 Feb 23;22(5):1729. doi: 10.3390/s22051729.
To automatically evaluate the ergonomics of workers, 3D skeletons are needed. Most ergonomic assessment methods, like REBA, are based on the different 3D joint angles. Thanks to the huge amount of training data, 2D skeleton detectors have become very accurate. In this work, we test three methods to calculate 3D skeletons from 2D detections: using the depth from a single RealSense range camera, triangulating the joints using multiple cameras, and combining the triangulation of multiple camera pairs. We tested the methods using recordings of a person doing different assembly tasks. We compared the resulting joint angles to the ground truth of a VICON marker-based tracking system. The resulting RMS angle error for the triangulation methods is between 12° and 16°, showing that they are accurate enough to calculate a useful ergonomic score from.
为了自动评估工人的工效学，需要 3D 骨骼。大多数工效学评估方法，如 REBA，都是基于不同的 3D 关节角度。由于大量的训练数据，2D 骨骼探测器已经变得非常精确。在这项工作中，我们测试了三种从 2D 检测中计算 3D 骨骼的方法：使用单个 RealSense 距离相机的深度，使用多个相机对关节进行三角测量，以及组合多个相机对的三角测量。我们使用一个人执行不同装配任务的记录来测试这些方法。我们将得到的关节角度与基于 VICON 标记的跟踪系统的真实值进行了比较。三角测量方法的 RMS 角度误差在 12°到 16°之间，这表明它们足够精确，可以从计算出有用的工效学评分。
