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

Abstract--This article presents mmHSense, a set of open labeled mmWave datasets to support human sensing research within Integrated Sensing and Communication (ISAC) systems. The datasets can be used to explore mmWave ISAC for various end applications such as gesture recognition, person identification, pose estimation, and localization. Moreover, the datasets can be used to develop and advance signal processing and deep learning research on mmWave ISAC. This article describes the testbed, experimental settings, and signal features for each dataset. Furthermore, the utility of the datasets is demonstrated through validation on a specific downstream task. In addition, we demonstrate the use of parameter-efficient fine-tuning to adapt ISAC models to different tasks, significantly reducing computational complexity while maintaining performance on prior tasks. Integrated Sensing and Communication (ISAC) [1] enables communication networks to double as intelligent sensing systems, enabling advance human sensing applications. For instance, ISAC can enable Wi-Fi routers to recognize human gestures in smart-home applications [2].