# CSI-Net: Unified Human Body Characterization and Pose Recognition

arXiv: https://arxiv.org/abs/1810.03064 · HTML: https://mlsanity.com/paper/1810.03064/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [GraSens: A Gabor Residual Anti-aliasing Sensing Framework for Action Recognition using WiFi](https://mlsanity.com/paper/2205.11945/) (2022)
- [A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition](https://mlsanity.com/paper/2205.11962/) (2022)
- [WiFi-based Spatiotemporal Human Action Perception](https://mlsanity.com/paper/2206.09867/) (2022)

## Abstract (arXiv)

We build CSI-Net, a unified Deep Neural Network~(DNN), to learn the representation of WiFi signals. Using CSI-Net, we jointly solved two body characterization problems: biometrics estimation (including body fat, muscle, water, and bone rates) and person recognition. We also demonstrated the application of CSI-Net on two distinctive pose recognition tasks: the hand sign recognition (fine-scaled action of the hand) and falling detection (coarse-scaled motion of the body).