开源毫米波人体感知数据集，支持手势识别与定位等应用
mmHSense: Multi-Modal and Distributed mmWave ISAC Datasets for Human Sensing
- 构建多模态分布式毫米波感知数据集，覆盖多种人体行为
- 验证模型在手势识别任务上表现优异，微调后计算量降低
- 适合雷达信号处理、深度学习及智能传感研究者使用
本文介绍mmHSense，一套开放标注的毫米波数据集，旨在支持集成感知与通信（ISAC）系统中的人体感知研究。数据集可用于手势识别、人员识别、姿态估计和定位等下游任务，也可用于推进毫米波ISAC的信号处理与深度学习算法研究。文章详述了实验测试平台、设置及各数据集的信号特征，并通过具体下游任务验证了数据集的有效性。此外，展示了参数高效微调方法在适应不同任务时的性能，显著降低计算复杂度，同时保持原有任务表现。
原文摘要 · Abstract (English)
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.
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