Abstract
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.
Highlights
The importance of good ergonomics for workers is hard to overstate
Bad body postures can lead to musculoskeletal disorders (MSDs) which have a large impact on both the workers lives and the economy
We describe and test 3D skeletonization techniques based on the depth data of a single
Summary
The importance of good ergonomics for workers is hard to overstate. Bad body postures can lead to musculoskeletal disorders (MSDs) which have a large impact on both the workers lives and the economy. To prevent MSDs, the ergonomics must be monitored so that problems are detected early. Most ergonomic assessment methods are done manually. They could be performed automatically, based on the joint angles of the 3D skeletons. Three of the most used [1] ergonomic assessment methods—RULA [2], REBA [3] and OWAS [4]—can be calculated largely based on the joint angles.
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