mmWaveXR: Multi-Modal and Distributed mmWave ISAC Datasets for Human Sensing

This work introduce a comprehensive collection of six labeled datasets for Integrated Sensing and Communication (ISAC) research. The datasets are designed to advance human sensing applications such as gesture recognition, pose estimation, person identification, and localization. They feature diverse experimental setups, including bi-static, multi-static, and distributed configurations, and capture data using both commercial off-the-shelf (COTS) Wi-Fi hardware (IEEE 802.11ad\/ay) and custom 5G mmWave OFDM systems.Each dataset provides access to a variety of signal features\u2014channel state information (CSI), beam SNR, and power per beam pair (PPBP)\u2014as well as auxiliary modalities like sub-6 GHz CSI and synchronized vision-based pose data. Data collection campaigns were conducted across multiple environments and countries, ensuring rich diversity in users, gestures, and environmental conditions.The datasets addresses the lack of open, large-scale mmWave ISAC datasets and supports research in deep learning, multi-modal sensor fusion, and cross-domain generalization.The six datasets are names as follows:mmWGesture, mmWPose, 5GmmGesture, DISAC-mmVRPose, mmW-GaitID, mmW-Loc