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.
Abstract (translated)
本文介绍了mmHSense，这是一个包含开放标记的毫米波数据集的集合，旨在支持集成传感和通信（ISAC）系统中的人体感应研究。这些数据集可用于探索毫米波ISAC在各种终端应用中的用途，例如手势识别、人员识别、姿态估计和定位。此外，还可以利用这些数据集来开发和推进毫米波ISAC的信号处理和深度学习研究。本文描述了每个数据集的测试平台、实验设置和信号特征。此外，通过在一个特定的下游任务上进行验证，展示了数据集的应用价值。另外，我们还演示了如何使用参数高效微调方法将ISAC模型适应不同的任务，在减少计算复杂度的同时保持在先前任务上的性能水平。
URL
https://arxiv.org/abs/2509.21396
