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

arXiv: https://arxiv.org/abs/2509.21396 · HTML: https://mlsanity.com/paper/2509.21396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 24 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video](https://mlsanity.com/paper/2203.00859/) (2022)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing](https://mlsanity.com/paper/2305.10345/) (2023)
- [Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends and Challenges](https://mlsanity.com/paper/2104.11457/) (2021)
- [3D Human Pose Estimation in Multi-View Operating Room Videos Using Differentiable Camera Projections](https://mlsanity.com/paper/2210.11826/) (2022)
- [Integrated Sensing and Communication with mmWave Massive MIMO: A Compressed Sampling Perspective](https://mlsanity.com/paper/2201.05766/) (2022)
- [DISC: a Dataset for Integrated Sensing and Communication in mmWave Systems](https://mlsanity.com/paper/2306.09469/) (2023)
- [Fine Tuning without Catastrophic Forgetting via Selective Low Rank Adaptation](https://mlsanity.com/paper/2501.15377/) (2025)
- [Practical Issues and Challenges in CSI-based Integrated Sensing and Communication](https://mlsanity.com/paper/2204.03535/) (2022)
- [Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned](https://mlsanity.com/paper/2306.17062/) (2023)
- [JUMP: Joint communication and sensing with Unsynchronized transceivers Made Practical](https://mlsanity.com/paper/2304.07766/) (2023)

## Abstract (arXiv)

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.