# SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing

arXiv: https://arxiv.org/abs/2207.07859 · HTML: https://mlsanity.com/paper/2207.07859/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 129 references that are in the map.
- First posted on arXiv 16 Jul 2022.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [Comparative Study of CNN and RNN for Natural Language Processing](https://mlsanity.com/paper/1702.01923/) (2017)
- [Person-in-WiFi: Fine-grained Person Perception using WiFi](https://mlsanity.com/paper/1904.00276/) (2019)
- [EfficientFi: Towards Large-Scale Lightweight WiFi Sensing via CSI Compression](https://mlsanity.com/paper/2204.04138/) (2022)
- [Adversarial Attack on Skeleton-based Human Action Recognition](https://mlsanity.com/paper/1909.06500/) (2019)
- [Kervolutional Neural Networks](https://mlsanity.com/paper/1904.03955/) (2019)
- [Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network](https://mlsanity.com/paper/2107.04941/) (2021)
- [AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning](https://mlsanity.com/paper/2205.01629/) (2022)
- [FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas](https://mlsanity.com/paper/1907.03994/) (2019)
- [Wi2Vi: Generating Video Frames from WiFi CSI Samples](https://mlsanity.com/paper/2001.05842/) (2019)
- [SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition](https://mlsanity.com/paper/2204.01560/) (2022)
- [IEEE 802.11bf: Toward Ubiquitous Wi-Fi Sensing](https://mlsanity.com/paper/2103.14918/) (2021)
- [Towards Stable and Comprehensive Domain Alignment: Max-Margin Domain-Adversarial Training](https://mlsanity.com/paper/2003.13249/) (2020)

## Built on by (most cited first, 30 of 34)

- [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)
- [AdaPose: Towards Cross-Site Device-Free Human Pose Estimation with Commodity WiFi](https://mlsanity.com/paper/2309.16964/) (2023)
- [FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things](https://mlsanity.com/paper/2310.00109/) (2023)
- [Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment](https://mlsanity.com/paper/2407.17777/) (2024)
- [RSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing](https://mlsanity.com/paper/2402.04888/) (2024)
- [Leveraging Foundation Models for Zero-Shot IoT Sensing](https://mlsanity.com/paper/2407.19893/) (2024)
- [Context-Aware Predictive Coding: A Representation Learning Framework for WiFi Sensing](https://mlsanity.com/paper/2410.01825/) (2024)
- [Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data](https://mlsanity.com/paper/2506.23174/) (2025)
- [MaskFi: Unsupervised Learning of WiFi and Vision Representations for Multimodal Human Activity Recognition](https://mlsanity.com/paper/2402.19258/) (2024)
- [CSI-JEPA: Towards Foundation Representations for Ubiquitous Sensing with Minimal Supervision](https://mlsanity.com/paper/2605.14171/) (2026)
- [Towards SISO Bistatic Sensing for ISAC](https://mlsanity.com/paper/2508.12614/) (2025)
- [CurvFed: Curvature-Aligned Federated Learning for Fairness without Demographics](https://mlsanity.com/paper/2404.19725/) (2024)
- [HoloLLM: Multisensory Foundation Model for Language-Grounded Human Sensing and Reasoning](https://mlsanity.com/paper/2505.17645/) (2025)
- [CIG-MAE: Cross-Modal Information-Guided Masked Autoencoder for Self-Supervised WiFi Sensing](https://mlsanity.com/paper/2512.04723/) (2025)
- [Spectrum Shortage for Radio Sensing? Leveraging Ambient 5G Signals for Human Activity Detection](https://mlsanity.com/paper/2603.03579/) (2026)
- [Evaluating BiLSTM and CNN+GRU Approaches for Human Activity Recognition Using WiFi CSI Data](https://mlsanity.com/paper/2506.11165/) (2025)
- [WhoFi: Deep Person Re-Identification via Wi-Fi Channel Signal Encoding](https://mlsanity.com/paper/2507.12869/) (2025)
- [Generalizing WiFi Gesture Recognition via Large-Model-Aware Semantic Distillation and Alignment](https://mlsanity.com/paper/2510.13390/) (2025)
- [maxVSTAR: Maximally Adaptive Vision-Guided CSI Sensing with Closed-Loop Edge Model Adaptation for Robust Human Activity Recognition](https://mlsanity.com/paper/2510.26146/) (2025)
- [Scale What Counts, Mask What Matters: Evaluating Foundation Models for Zero-Shot Cross-Domain Wi-Fi Sensing](https://mlsanity.com/paper/2511.18792/) (2025)
- [Towards Trustworthy Wi-Fi CSI-based Sensing: Systematic Evaluation of Adversarial Robustness](https://mlsanity.com/paper/2511.20456/) (2025)
- [FuseFi: Combining Irregularly Sampled CSI from Diverse Communication Packets and Frequency Bands for Wi-Fi Sensing](https://mlsanity.com/paper/2512.22143/) (2025)
- [SkeFi: Cross-Modal Knowledge Transfer for Wireless Skeleton-Based Action Recognition](https://mlsanity.com/paper/2601.12432/) (2026)
- [Human Presence Detection via Wi-Fi Range-Filtered Doppler Spectrum on Commodity Laptops](https://mlsanity.com/paper/2603.10845/) (2026)
- [ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization](https://mlsanity.com/paper/2605.01866/) (2026)
- [Practical Wi-Fi-based Motion Recognition Under Variable Traffic Patterns](https://mlsanity.com/paper/2605.08308/) (2026)
- [Physics-Guided Attention in a Lightweight TCN for Efficient WiFi CSI-Based Human Activity Recognition](https://mlsanity.com/paper/2606.01834/) (2026)
- [RePos: Relative-to-Absolute Pose Factorization for Cross-Environment WiFi-Based 3D Human Pose Estimation](https://mlsanity.com/paper/2607.02986/) (2026)

## Abstract (arXiv)

WiFi sensing has been evolving rapidly in recent years. Empowered by propagation models and deep learning methods, many challenging applications are realized such as WiFi-based human activity recognition and gesture recognition. However, in contrast to deep learning for visual recognition and natural language processing, no sufficiently comprehensive public benchmark exists. In this paper, we review the recent progress on deep learning enabled WiFi sensing, and then propose a benchmark, SenseFi, to study the effectiveness of various deep learning models for WiFi sensing. These advanced models are compared in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, feature transferability, and adaptability of unsupervised learning. It is also regarded as a tutorial for deep learning based WiFi sensing, starting from CSI hardware platform to sensing algorithms. The extensive experiments provide us with experiences in deep model design, learning strategy skills and training techniques for real-world applications. To the best of our knowledge, this is the first benchmark with an open-source library for deep learning in WiFi sensing research. The benchmark codes are available at https://github.com/xyanchen/WiFi-CSI-Sensing-Benchmark.