# SiFall: Practical Online Fall Detection with RF Sensing

arXiv: https://arxiv.org/abs/2301.03773 · HTML: https://mlsanity.com/paper/2301.03773/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2023.

## Built on by (most cited first, 15 of 15)

- [AdaPose: Towards Cross-Site Device-Free Human Pose Estimation with Commodity WiFi](https://mlsanity.com/paper/2309.16964/) (2023)
- [GPIoT: Tailoring Small Language Models for IoT Program Synthesis and Development](https://mlsanity.com/paper/2503.00686/) (2025)
- [FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients](https://mlsanity.com/paper/2502.20639/) (2025)
- [Context-Aware Predictive Coding: A Representation Learning Framework for WiFi Sensing](https://mlsanity.com/paper/2410.01825/) (2024)
- [TransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation](https://mlsanity.com/paper/2411.16020/) (2024)
- [Compressing Deep Reinforcement Learning Networks with a Dynamic Structured Pruning Method for Autonomous Driving](https://mlsanity.com/paper/2402.05146/) (2024)
- [MaskFi: Unsupervised Learning of WiFi and Vision Representations for Multimodal Human Activity Recognition](https://mlsanity.com/paper/2402.19258/) (2024)
- [Self-Supervised Learning for WiFi CSI-Based Human Activity Recognition: A Systematic Study](https://mlsanity.com/paper/2308.02412/) (2023)
- [Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks](https://mlsanity.com/paper/2312.13611/) (2023)
- [Cross-Domain Continual Learning for Edge Intelligence in Wireless ISAC Networks](https://mlsanity.com/paper/2502.12736/) (2025)
- [CIG-MAE: Cross-Modal Information-Guided Masked Autoencoder for Self-Supervised WiFi Sensing](https://mlsanity.com/paper/2512.04723/) (2025)
- [Privacy-Preserving Multi-Stage Fall Detection Framework with Semi-supervised Federated Learning and Robotic Vision Confirmation](https://mlsanity.com/paper/2507.10474/) (2025)
- [mmAnomaly: Leveraging Visual Context for Robust Anomaly Detection in the Non-Visual World with mmWave Radar](https://mlsanity.com/paper/2604.00382/) (2026)
- [NeuroPath: Practically Adopting Motor Imagery Decoding through EEG Signals](https://mlsanity.com/paper/2604.09654/) (2026)
- [TaFall: Balance-Informed Fall Detection via Passive Thermal Sensing](https://mlsanity.com/paper/2604.09693/) (2026)

## Abstract (arXiv)

Falls present a significant global public health challenge, especially in today's aging society, underscoring the importance of developing an effective fall detection system. Non-invasive radio-frequency (RF) based fall detection has garnered substantial attention due to its wide coverage and privacy-preserving nature. Existing RF-based fall detection systems approach falls as an activity classification problem, assuming that human falls introduce reproducible patterns to the RF signals. However, we argue that falls are inherently accidental, making their impact uncontrollable and unforeseeable. We propose a fundamentally different approach to fall detection by shifting the focus from directly identifying hard-to-quantify falls to recognizing normal, repeatable human activities, thus treating falls as abnormal activities outside the normal activity distribution. We introduce a self-supervised incremental learning system incorporating FallNet, a deep neural network that employs unsupervised learning techniques. Our real-time fall detection system prototype leverages WiFi Channel State Information (CSI) sensing data and has been extensively tested with 16 human subjects.