InHARD-DT - Industrial Human Action Recognition Dataset - Digital Twin

This paper explores the use of a Digital Twin of a real industrial workstation involving assembly tasks with a robotic arm interfaced with Virtual Reality (VR) to extract a digital human model. The DT simulates assembly operations performed by humans aiming to generate self-labeled data. Thereby, a Human Action Recognition dataset named InHARD-DT was created to validate a real use case in which we use the acquired auto-labeled DT data of the virtual representation of the InHARD dataset to train a Spatial–Temporal Graph Convolutional Neural Network with skeletal data on one hand. On the other hand, the Physical Twin (PT) data of the InHARD dataset was used for testing. Therefore, we introduce a RGB+S dataset named “Industrial Human Action Recognition Dataset - Digital Twin” (InHARD-DT) from a real-world setting for industrial human action recognition. 

We invited 12 distinct subjects from the LINEACT laboratory (4 females and 8 males) for the DT data collection to perform the same assembly tasks of the InHARD dataset (link below) in Virtual Reality via a VR application of an industrial real workstation. This dataset contains 13 different industrial action classes and over 4800 action samples. The introduction of this dataset should allow us the study and development of various learning techniques for the task of human actions analysis inside industrial environments involving human robot collaborations. It can be used also in cross-validation scenarios where the training phase can be done using the Physical Twin (PT) data of the InHARD dataset (real world scenarios) and then test using Digital Twin (DT) data of the InHARD-DT dataset which is the main objective of this paper.