  Human-robot collaboration has gained a notable prominence in Industry 4.0, as
the use of collaborative robots increases efficiency and productivity in the
automation process. However, it is necessary to consider the use of mechanisms
that increase security in these environments, as the literature reports that
risk situations may exist in the context of human-robot collaboration. One of
the strategies that can be adopted is the visual recognition of the
collaboration environment using machine learning techniques, which can
automatically identify what is happening in the scene and what may happen in
the future. In this work, we are proposing a new framework that is capable of
detecting robotic arm keypoints commonly used in Industry 4.0. In addition to
detecting, the proposed framework is able to predict the future movement of
these robotic arms, thus providing relevant information that can be considered
in the recognition of the human-robot collaboration scenario. The proposed
framework is based on deep and extreme learning machine techniques. Results
show that the proposed framework is capable of detecting and predicting with
low error, contributing to the mitigation of risks in human-robot
collaboration.
