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A dataset of body tracking information is presented. The dataset consists of 315 captured walking sequences. Each sequence is simultaneously captured by two Azure Kinect devices. The two captures are interleaved to effectively double the frame rate. Fifteen participants partook in this experiment. Each experiment consists of seven walking actions, and having three predefined trajectories per experiment. That results in 21 sequences per participant. The data were collected using the Azure Kinect Sensor SDK. They were later processed using the official tools and libraries provided by Microsoft. For each sequence and trajectory, the positions and orientations of thirty-two tracked joints were obtained and saved. The dataset is structured as follows. The experiments from each subject are saved in a single directory. Each directory contains multiple JSON files of timestamped body tracking information to enable the fusion of the two device streams. A calibration file is also provided, enabling the mapping of the coordinates between the two Azure Kinect devices capturing the data (mapping the coordinates of the device known as the Subordinate device to the Master device coordinate system). This data can be used to train neural networks for human motion prediction tasks or test pre-existing algorithms on Azure Kinect data. This dataset could also aid in gait recognition and analysis, as well as in performing action recognition and other surveillance activities.<br> <br> <strong>Journal Publication Citation:</strong><br> Charli Posner, Adrián Sánchez-Mompó, Ioannis Mavromatis, Mustafa Al-Ani,<br> A dataset of human body tracking of walking actions captured using two Azure Kinect sensors,<br> Data in Brief,<br> Volume 49,<br> 2023,<br> 109334,<br> ISSN 2352-3409,<br> https://doi.org/10.1016/j.dib.2023.109334.<br> (https://www.sciencedirect.com/science/article/pii/S2352340923004523)