A Roadmap for Quickstart: Curated resources for Vital Signs & HCI. Maintained by phish-tech.
A curated, research-grade index of millimeter-wave (mmWave) radar sensing for vital signs (respiration/heartbeat), HCI & gesture, and indoor tracking/imaging.
Built for engineers and researchers who want credible papers first, plus datasets, tools, and hardware pointers.
Language: English | 简体中文
- 🔥 Featured / Recommended
- 📚 Academic Paper Index
- 🛠 Open Source Tools
- 💾 Datasets
- 🔌 Hardware
- 🎓 Zero to Hero
- 👥 Community & Contributing
- 🧩 Phish-tech Private Goods (Placed at the End)
If you only bookmark a few things:
- Start from Vital Signs fundamentals: mmWave FMCW phase-based extraction + multi-person separation. - For HCI: Soli (CHI/SIGGRAPH lineage) + IMWUT arm gesture systems.
- For Tracking/Imaging: milliMap (MobiSys) + HuPR (WACV) + IMWUT multi-person tracking.
Broader radar perception lists (non-mmWave-specific but useful for cross-referencing):
awesome-radar-perception(datasets + detection + tracking): https://github.com/ZHOUYI1023/awesome-radar-perception
| ID | Year | Title | Venue | Links |
|---|---|---|---|---|
| VS-01 | 2016 | Monitoring Vital Signs Using Millimeter Wave | ACM MobiHoc | DOI: https://doi.org/10.1145/2942358.2942381 |
| VS-02 | 2017 | Vital Sign and Sleep Monitoring Using Millimeter Wave | ACM (IMWUT/UbiComp lineage) | DOI: https://doi.org/10.1145/3051124 |
| VS-03 | 2019 | Remote Monitoring of Human Vital Signs Using mm-Wave FMCW Radar | IEEE Access | PDF: https://www.weizmann.ac.il/math/yonina/sites/math.yonina/files/Remote_Monitoring_of_Human_Vital_Signs_Using_mm-Wave_FMCW_Radar.pdf |
| VS-04 | 2020 | Remote Monitoring of Human Vital Signs Based on 77-GHz mm-Wave FMCW Radar | Sensors | DOI: https://doi.org/10.3390/s20102999 |
| VS-05 | 2021 | Non-Contact Monitoring of Human Vital Signs Using FMCW Millimeter Wave Radar in the 120 GHz Band | Sensors | DOI: https://doi.org/10.3390/s21082732 |
| VS-06 | 2022 | High-Precision Vital Signs Monitoring Method Using a FMCW Millimeter-Wave Sensor | Sensors | DOI: https://doi.org/10.3390/s22197543 |
| VS-07 | 2022 | Your Breath Doesn't Lie: Multi-user Authentication by Sensing Respiration Using mmWave Radar | IEEE SECON | DOI: https://doi.org/10.1109/SECON55815.2022.9918606 |
| VS-08 | 2023 | Sparsity-Based Multi-Person Non-Contact Vital Signs Monitoring via FMCW Radar | IEEE JBHI | DOI: https://doi.org/10.1109/JBHI.2023.3255740 |
| VS-09 | 2023 | Pi-ViMo: Physiology-inspired Robust Vital Sign Monitoring using mmWave Radars | ACM TIOT | DOI: https://doi.org/10.1145/3589347 |
| VS-10 | 2025 | Event-level Identification of Sleep Apnea using FMCW Radar | Scientific Reports | https://doi.org/10.3390/bioengineering12040399 |
More (Vital Signs):
- Multi-person TI reference (classic industry technical report): https://e2echina.ti.com/cfs-file/__key/communityserver-discussions-components-files/60/Vital-Signs-Monitoring-of-Multiple-People-using-a.pdf
| ID | Year | Title | Venue | Links |
|---|---|---|---|---|
| HCI-01 | 2016 | Soli: Ubiquitous Gesture Sensing with Millimeter Wave Radar | ACM TOG | DOI: https://doi.org/10.1145/2897824.2925953 |
| HCI-02 | 2020 | Real-time Arm Gesture Recognition in Smart Home Scenarios via Millimeter Wave Sensing (mHomeGes) | ACM IMWUT | DOI: https://doi.org/10.1145/3432235 |
| HCI-03 | 2020 | MU-ID: Multi-user Identification Through Gaits Using 60 GHz Radios | IEEE INFOCOM | DOI: https://doi.org/10.1109/INFOCOM41043.2020.9155456 |
| HCI-04 | 2020 | Handwriting Tracking using 60 GHz mmWave Radar | IEEE WF-IoT | DOI: https://doi.org/10.1109/WF-IoT48130.2020.9221158 |
| HCI-05 | 2021 | Hand Gesture Recognition Using 802.11ad mmWave Sensor in the Mobile Device | IEEE WCNC Workshops | DOI: https://doi.org/10.1109/WCNCW49093.2021.9419978 |
| HCI-06 | 2021 | mmWrite: Passive Handwriting Tracking Using a Single Millimeter-Wave Radio | IEEE IoT-J | DOI: https://doi.org/10.1109/JIOT.2021.3066507 |
| HCI-07 | 2021 | DI-Gesture: A Fine-grained Dataset and Benchmark for Doppler Imaging-based Gesture Recognition | arXiv | https://arxiv.org/abs/2101.05214 |
| HCI-08 | 2022 | mm4Arm: Leveraging Properties of mmWave Signals for 3D Arm Motion Tracking | ACM POMACS | DOI: https://doi.org/10.1145/3570613 |
| HCI-09 | 2022 | GaitCube: Deep Data Cube Learning for Human Recognition With Millimeter-Wave Radio | IEEE IoT-J | DOI: https://doi.org/10.1109/JIOT.2021.3083934 |
| HCI-10 | 2024 | mmSign: mmWave-based Few-Shot Online Handwritten Signature Verification | ACM TOSN | DOI: https://doi.org/10.1145/3605945 |
| HCI-11 | 2025 | mmPencil: Toward Writing-Style-Independent In-Air Handwriting Recognition via mmWave Radar and Large Vision-Language Model | ACM IMWUT | DOI: https://doi.org/10.1145/3749504 |
| ID | Year | Title | Venue | Links |
|---|---|---|---|---|
| TRK-01 | 2018 | Indoor Localization Using Commercial Off-The-Shelf 60 GHz Access Points | IEEE INFOCOM | DOI: https://doi.org/10.1145/INFOCOM.2018.8486232 |
| TRK-02 | 2019 | RadHAR: Human Activity Recognition from Point Clouds Generated through a Millimeter-wave Radar | ACM mmNets (MobiCom WS) | DOI: https://doi.org/10.1145/3349624.3356768 |
| TRK-03 | 2020 | milliMap: Robust Indoor Mapping with Low-cost mmWave Radar | ACM MobiSys | DOI: https://doi.org/10.1145/3386901.3388945 |
| TRK-04 | 2022 | mTransSee: Enabling Real-time mmWave Sparse Imaging through Non-RF Occluders | ACM IMWUT | DOI: https://doi.org/10.1145/3517231 |
| TRK-05 | 2023 | HuPR: A Benchmark for Human Pose Estimation Using Millimeter Wave Radar | IEEE WACV | PDF: https://openaccess.thecvf.com/content/WACV2023/papers/Lee_HuPR_A_Benchmark_for_Human_Pose_Estimation_Using_Millimeter_Wave_WACV_2023_paper.pdf |
| TRK-06 | 2023 | Environment-aware Multi-person Tracking in Indoor Environments with mmWave Radars | ACM IMWUT | DOI: https://doi.org/10.1145/3610902 |
| TRK-07 | 2023 | MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Wireless Human Sensing | NeurIPS Datasets & Benchmarks / arXiv | Project: https://ntu-aiot-lab.github.io/mm-fi |
| TRK-08 | 2024 | PmTrack: Enabling Personalized mmWave-based Human Tracking in Commodity Smart Home | ACM IMWUT | DOI: https://doi.org/10.1145/3631433 |
| TRK-09 | 2024 | Waffle: Waterproof mmWave-based Sensing Inside Bathrooms with Running Water | ACM IMWUT | DOI: https://doi.org/10.1145/3631458 |
| TRK-10 | 2024 | Fast Human Action Recognition via mmWave Radar Point Clouds | ACM (conference proceedings) | DOI: https://doi.org/10.1145/3627673.3679787 |
| TRK-11 | 2025 | DragonFly: Drone-based 3D Localization of Backscatter Tags Using mmWave Radar | ACM MobiCom | DOI: https://doi.org/10.1145/3680207.3765269 |
- OpenRadar — Open-source radar signal processing modules and examples.
https://github.com/PreSenseRadar/OpenRadar - ti_mmwave_rospkg — ROS integration for TI mmWave sensors.
https://github.com/robotics-upo/ti_mmwave_rospkg - TI mmWave SDK (official) — Firmware + reference processing chain for IWR/AWR devices.
https://www.ti.com/tool/MMWAVE-SDK - ⭐ mmWave Preprocessing Tool for Heartbeat Estimation https://github.com/phish-tech/mmWave-Heartbeat-Dataset-Preprocessing-Toolbox
- RadHAR (mmWave point-cloud HAR): https://github.com/nesl/RadHAR
- HuPR (mmWave pose benchmark): https://github.com/robert80203/HuPR-A-Benchmark-for-Human-Pose-Estimation-Using-Millimeter-Wave-Radar
- MM-Fi (multi-modal 4D dataset incl. mmWave radar): https://github.com/ybhbingo/MMFi_dataset
- mHomeGes-dataset (mmWave arm gestures in smart homes): https://github.com/GestureMan/mHomeGes-dataset
- Texas Instruments (TI) mmWave (IWR/AWR series) Example: IWR6843 product page: https://www.ti.com/product/IWR6843
- Infineon XENSIV™ 60 GHz radar Example: BGT60TR13C: https://www.infineon.com/part/BGT60TR13C
- Silicon Radar (122 GHz ISM band devices) Example: TRX_120_001 datasheet: https://siliconradar.com/datasheets/Datasheet_TRX_120_001.html
New to mmWave radar? Follow this learning path to go from concept to implementation:
- Theory (The Basics) 📖 Read the classic TI FMCW Radar Basics whitepaper. Understand Range-FFT, Doppler-FFT, and Angle Estimation.
- Hands-on (The Quickstart) 🛠️ Run the mmWave-Heartbeat-Toolbox. It handles the complex data parsing and gives you a working vital signs baseline.
- Deep Dive (The Academic Pillar) 🎓 Read the foundational paper VS-01 (MobiHoc '16). It defined the phase-based sensing pipeline used by most researchers today.
- Expansion (The Community) 🧩 Try replicating examples from OpenRadar to explore detection and tracking.
Contributions are welcome and appreciated.
How to add a paper/tool/dataset
- Keep scope: mmWave radar sensing (vital signs / HCI / tracking & imaging).
- Prefer peer-reviewed venues (ACM/IEEE/Elsevier/Nature family) and stable links (DOI/project page).
- Follow the indexing format: add a new ID and a one-line citation.
Suggested repo files
CONTRIBUTING.md— contribution rules + formattingCODE_OF_CONDUCT.md— community policyCITATION.cff— how to cite this list
The following items are presented by the author of this project.
- ⭐ mmWave Preprocessing Tool for Heartbeat Estimation — https://github.com/phish-tech/mmWave-Heartbeat-Toolbox
A lightweight, pure Python framework for TI mmWave radar data processing. Features EEMD for robust vital sign extraction. 🚀 Recommended for Beginners.