The onset of Industry 4.0 brings a greater demand for Human-Robot Collaboration (HRC) in manufacturing. This has led to a critical need for bridging the sensing and AI with the mechanical-n-physical necessities to successfully augment the robot’s awareness and intelligence. In a HRC work cell, options for sensors to detect human joint locations vary greatly in complexity, usability, and cost. In this paper, the use of depth cameras is explored, since they are a relatively low-cost option that does not require users to wear extra sensing hardware. Herein, the Google Media Pipe (BlazePose) and OpenPose skeleton tracking software packages are used to estimate the pixel coordinates of each human joint in images from depth cameras. The depth at each pixel is then used with the joint pixel coordinates to generate the 3D joint locations of the skeleton. In comparing these skeleton trackers, this paper also presents a novel method of combining the skeleton that the trackers generate from each camera’s data utilizing a quaternion/link-length representation of the skeleton. Results show that the overall mean and standard deviation in position error between the fused skeleton and target locations was lower compared to the skeletons resulting directly from each camera’s data.

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