Guest Editor: JIN Biao

This dataset is a millimeter wave radar three-dimensional point cloud dataset for human movements (referred to as mmWave-3DPCHM-1.0), which includes point cloud data collected and processed by two types of millimeter wave radar sensors. The two data acquisition devices are TI's IWR1443-ISK and Vayyar's vBlu RF imaging module. This dataset includes raw point cloud data from millimeter wave radar and preprocessed point cloud data, as well as point cloud processing codes, images and videos of various predetermined actions. This dataset is collected in a conference room. The data collection process involves the use of computers, brackets, and millimeter wave radars. The radar is installed on the wall at a height of 1.75 meters above the ground. Volunteers complete the prescribed actions within a rectangular area, which is 1.2 meters horizontally from the radar and has a length and width of 2 meters. This dataset selected 7 volunteers and collected 12 types of human movements, including 3 static movements (falling, sitting, and standing) and 9 dynamic movements (punching, jumping, left waving, left leaning forward, opening arms, right waving, right leaning forward, squatting, and walking). Collect 3 sets of data for each action, with each set collected for 3 minutes. During the collection process, volunteers continuously completed the prescribed actions. The data is saved as an Excel file, with the specific format shown in Figure 4. For the convenience of researchers, we preprocessed the data of TI millimeter wave radar through multi frame fusion clustering, and the processed code and results are all in the corresponding folder.   

 For details of dataset, please refer tommWave-3DPCHM-1.0：A 3D Point Cloud Dataset for Human Actions Using Millimeter-Wave Radar_User Guide.pdf

The current data reference format is as follows:

[1] JIN Biao, SUN Kangsheng, WU Hao, et al. 3D point cloud from millimeter-wave radar for human action recognition: dataset and method[J]. Journal of Radars, 2025, 14(1): 73–89. doi:  10.12000/JR24195

Expected Release date: January 14, 2025