# Wi-Motion: A Robust Human Activity Recognition Using WiFi Signals

arXiv: https://arxiv.org/abs/1810.11705 · HTML: https://mlsanity.com/paper/1810.11705/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2018.

## Built on by (most cited first, 5 of 5)

- [Human Silhouette and Skeleton Video Synthesis through Wi-Fi signals](https://mlsanity.com/paper/2203.05864/) (2022)
- [MaskFi: Unsupervised Learning of WiFi and Vision Representations for Multimodal Human Activity Recognition](https://mlsanity.com/paper/2402.19258/) (2024)
- [MAC: ModAlity Calibration for Object Detection](https://mlsanity.com/paper/2310.09461/) (2023)
- [MISO: Monitoring Inactivity of Single Older Adults at Home using RGB-D Technology](https://mlsanity.com/paper/2311.02249/) (2023)
- [WiFi-TCN: Temporal Convolution for Human Interaction Recognition based on WiFi signal](https://mlsanity.com/paper/2305.18211/) (2023)

## Abstract (arXiv)

Recent research has shown that human motions and positions can be recognized through WiFi signals. The key intuition is that different motions and positions introduce different multi-path distortions in WiFi signals and generate different patterns in the time-series of channel state information (CSI). In this paper, we propose Wi-Motion, a WiFi-based human activities recognition system. Unlike existing systems, Wi-Motion adopts the amplitude and phase information extracted from the CSI sequence to construct the classifiers respectively, and combines the results using a combination strategy based on posterior probability. As the simulation results shows, Wi-Motion can recognize six human activities with the mean accuracy of 98:4%.