# RF-Based Human Activity Recognition Using Signal Adapted Convolutional Neural Network

arXiv: https://arxiv.org/abs/2110.14307 · HTML: https://mlsanity.com/paper/2110.14307/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2021.

## Built on by (most cited first, 11 of 11)

- [Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks](https://mlsanity.com/paper/2303.15991/) (2023)
- [FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks](https://mlsanity.com/paper/2311.01483/) (2023)
- [SATSense: Multi-Satellite Collaborative Framework for Spectrum Sensing](https://mlsanity.com/paper/2405.15542/) (2024)
- [Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition](https://mlsanity.com/paper/2410.19766/) (2024)
- [A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observation](https://mlsanity.com/paper/2507.05731/) (2025)
- [OCHID-Fi: Occlusion-Robust Hand Pose Estimation in 3D via RF-Vision](https://mlsanity.com/paper/2308.10146/) (2023)
- [Action Recognition in Real-World Ambient Assisted Living Environment](https://mlsanity.com/paper/2503.23214/) (2025)
- [Intra-DP: A High Performance Collaborative Inference System for Mobile Edge Computing](https://mlsanity.com/paper/2507.05829/) (2025)
- [Tri-Cam: Practical Eye Gaze Tracking via Camera Network](https://mlsanity.com/paper/2409.19554/) (2024)
- [ALERT Open Dataset and Input-Size-Agnostic Vision Transformer for Driver Activity Recognition using IR-UWB](https://mlsanity.com/paper/2512.12206/) (2025)
- [A comparison between ceiling-mounted FMCW, IR-UWB and Wi-Fi radar for in-bedroom human activity monitoring and sleep interruption detection](https://mlsanity.com/paper/2608.20322/) (2026)

## Abstract (arXiv)

Human Activity Recognition (HAR) plays a critical role in a wide range of real-world applications, and it is traditionally achieved via wearable sensing. Recently, to avoid the burden and discomfort caused by wearable devices, device-free approaches exploiting RF signals arise as a promising alternative for HAR. Most of the latest device-free approaches require training a large deep neural network model in either time or frequency domain, entailing extensive storage to contain the model and intensive computations to infer activities. Consequently, even with some major advances on device-free HAR, current device-free approaches are still far from practical in real-world scenarios where the computation and storage resources possessed by, for example, edge devices, are limited. Therefore, we introduce HAR-SAnet which is a novel RF-based HAR framework. It adopts an original signal adapted convolutional neural network architecture: instead of feeding the handcraft features of RF signals into a classifier, HAR-SAnet fuses them adaptively from both time and frequency domains to design an end-to-end neural network model. We apply point-wise grouped convolution and depth-wise separable convolutions to confine the model scale and to speed up the inference execution time. The experiment results show that the recognition accuracy of HAR-SAnet outperforms state-of-the-art algorithms and systems.