# Unsupervised Learning for Human Sensing Using Radio Signals

arXiv: https://arxiv.org/abs/2207.02370 · HTML: https://mlsanity.com/paper/2207.02370/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 49 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Learning Representations by Maximizing Mutual Information Across Views](https://mlsanity.com/paper/1906.00910/) (2019)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [Unsupervised Embedding Learning via Invariant and Spreading Instance Feature](https://mlsanity.com/paper/1904.03436/) (2019)
- [Local Aggregation for Unsupervised Learning of Visual Embeddings](https://mlsanity.com/paper/1903.12355/) (2019)
- [Variational Autoencoder for Deep Learning of Images, Labels and Captions](https://mlsanity.com/paper/1609.08976/) (2016)
- [Memory-augmented Dense Predictive Coding for Video Representation Learning](https://mlsanity.com/paper/2008.01065/) (2020)
- [Person-in-WiFi: Fine-grained Person Perception using WiFi](https://mlsanity.com/paper/1904.00276/) (2019)
- [Making the Invisible Visible: Action Recognition Through Walls and Occlusions](https://mlsanity.com/paper/1909.09300/) (2019)
- [Learning Longterm Representations for Person Re-Identification Using Radio Signals](https://mlsanity.com/paper/2004.01091/) (2020)
- [In-Home Daily-Life Captioning Using Radio Signals](https://mlsanity.com/paper/2008.10966/) (2020)
- [Position Tracking for Virtual Reality Using Commodity WiFi](https://mlsanity.com/paper/1703.03468/) (2017)

## Built on by (most cited first, 14 of 14)

- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [Bootstrapping Autonomous Driving Radars with Self-Supervised Learning](https://mlsanity.com/paper/2312.04519/) (2023)
- [ByteNet: Rethinking Multimedia File Fragment Classification through Visual Perspectives](https://mlsanity.com/paper/2410.20855/) (2024)
- [hear-your-action: human action recognition by ultrasound active sensing](https://mlsanity.com/paper/2309.08087/) (2023)
- [Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications](https://mlsanity.com/paper/2504.16972/) (2025)
- [Deakin RF-Sensing: Experiments on Correlated Knowledge Distillation for Monitoring Human Postures with Radios](https://mlsanity.com/paper/2305.14829/) (2023)
- [Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction](https://mlsanity.com/paper/2306.14233/) (2023)
- [Divide and Fuse: Body Part Mesh Recovery from Partially Visible Human Images](https://mlsanity.com/paper/2407.09694/) (2024)
- [ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning](https://mlsanity.com/paper/2210.05513/) (2022)
- [Contactless Oxygen Monitoring with Gated Transformer](https://mlsanity.com/paper/2212.03357/) (2022)
- [BGM2Pose: Active 3D Human Pose Estimation with Non-Stationary Sounds](https://mlsanity.com/paper/2503.00389/) (2025)
- [Lessons from Deploying Learning-based CSI Localization on a Large-Scale ISAC Platform](https://mlsanity.com/paper/2504.17173/) (2025)
- [ARC-Fi: Exploiting Antenna Spatial Diversity for Label-Efficient Domain Generalization in Wi-Fi Sensing](https://mlsanity.com/paper/2310.06328/) (2023)
- [LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Models](https://mlsanity.com/paper/2604.17770/) (2026)

## Abstract (arXiv)

There is a growing literature demonstrating the feasibility of using Radio Frequency (RF) signals to enable key computer vision tasks in the presence of occlusions and poor lighting. It leverages that RF signals traverse walls and occlusions to deliver through-wall pose estimation, action recognition, scene captioning, and human re-identification. However, unlike RGB datasets which can be labeled by human workers, labeling RF signals is a daunting task because such signals are not human interpretable. Yet, it is fairly easy to collect unlabelled RF signals. It would be highly beneficial to use such unlabeled RF data to learn useful representations in an unsupervised manner. Thus, in this paper, we explore the feasibility of adapting RGB-based unsupervised representation learning to RF signals. We show that while contrastive learning has emerged as the main technique for unsupervised representation learning from images and videos, such methods produce poor performance when applied to sensing humans using RF signals. In contrast, predictive unsupervised learning methods learn high-quality representations that can be used for multiple downstream RF-based sensing tasks. Our empirical results show that this approach outperforms state-of-the-art RF-based human sensing on various tasks, opening the possibility of unsupervised representation learning from this novel modality.