# Deep Learning for Radio-based Human Sensing: Recent Advances and Future Directions

arXiv: https://arxiv.org/abs/2010.12717 · HTML: https://mlsanity.com/paper/2010.12717/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 12 of 12)

- [MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing](https://mlsanity.com/paper/2305.10345/) (2023)
- [WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing](https://mlsanity.com/paper/2402.09430/) (2024)
- [X-Fi: A Modality-Invariant Foundation Model for Multimodal Human Sensing](https://mlsanity.com/paper/2410.10167/) (2024)
- [Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations](https://mlsanity.com/paper/2302.00992/) (2023)
- [radarODE: An ODE-Embedded Deep Learning Model for Contactless ECG Reconstruction from Millimeter-Wave Radar](https://mlsanity.com/paper/2408.01672/) (2024)
- [Self-Supervised WiFi-Based Activity Recognition](https://mlsanity.com/paper/2104.09072/) (2021)
- [KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment](https://mlsanity.com/paper/2412.04783/) (2024)
- [Vision Transformer with Convolutional Encoder-Decoder for Hand Gesture Recognition using 24 GHz Doppler Radar](https://mlsanity.com/paper/2209.05032/) (2022)
- [NeuralPrefix: A Zero-shot Sensory Data Imputation Plugin](https://mlsanity.com/paper/2502.05883/) (2025)
- [Towards Trustworthy Wi-Fi CSI-based Sensing: Systematic Evaluation of Adversarial Robustness](https://mlsanity.com/paper/2511.20456/) (2025)
- [SkeFi: Cross-Modal Knowledge Transfer for Wireless Skeleton-Based Action Recognition](https://mlsanity.com/paper/2601.12432/) (2026)
- [Bridging the Data Gap: Digital Twin as a New Paradigm for AI-based Radio Sensing](https://mlsanity.com/paper/2609.26214/) (2026)

## Abstract (arXiv)

While decade-long research has clearly demonstrated the vast potential of radio frequency (RF) for many human sensing tasks, scaling this technology to large scenarios remained problematic with conventional approaches. Recently, researchers have successfully applied deep learning to take radio-based sensing to a new level. Many different types of deep learning models have been proposed to achieve high sensing accuracy over a large population and activity set, as well as in unseen environments. Deep learning has also enabled detection of novel human sensing phenomena that were previously not possible. In this survey, we provide a comprehensive review and taxonomy of recent research efforts on deep learning based RF sensing. We also identify and compare several publicly released labeled RF sensing datasets that can facilitate such deep learning research. Finally, we summarize the lessons learned and discuss the current limitations and future directions of deep learning based RF sensing.