# Wital: A COTS WiFi Devices Based Vital Signs Monitoring System Using NLOS Sensing Model

arXiv: https://arxiv.org/abs/2305.14490 · HTML: https://mlsanity.com/paper/2305.14490/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2023.

## Built on by (most cited first, 6 of 6)

- [WiOpen: A Robust Wi-Fi-based Open-set Gesture Recognition Framework](https://mlsanity.com/paper/2402.00822/) (2024)
- [Breaking Coordinate Overfitting: Geometry-Aware WiFi Sensing for Cross-Layout 3D Pose Estimation](https://mlsanity.com/paper/2601.12252/) (2026)
- [PulseFi: A Low Cost Robust Machine Learning System for Accurate Cardiopulmonary and Apnea Monitoring Using Channel State Information](https://mlsanity.com/paper/2510.24744/) (2025)
- [Beyond Physical Labels: Redefining Domains for Robust WiFi-based Gesture Recognition](https://mlsanity.com/paper/2601.03825/) (2026)
- [Towards Battery-Free Wireless Sensing via Radio-Frequency Energy Harvesting](https://mlsanity.com/paper/2409.00086/) (2024)
- [MobiVital: Self-supervised Time-series Quality Estimation for Contactless Respiration Monitoring Using UWB Radar](https://mlsanity.com/paper/2503.11064/) (2025)

## Abstract (arXiv)

Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras; however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring system based on low-cost and widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two challenges that need to be overcome. First, the torso deformations caused by breathing/heartbeats are weak. How can such deformations be effectively captured? Second, movements such as turning over affect the accuracy of vital sign monitoring. How can such detrimental effects be avoided? For the former, we propose a non-line-of-sight (NLOS) sensing model for modeling the relationship between the energy ratio of line-of-sight (LOS) to NLOS signals and the vital sign monitoring capability using Ricean K theory and use this model to guide the system construction to better capture the deformations caused by breathing/heartbeats. For the latter, we propose a motion segmentation method based on motion regularity detection that accurately distinguishes respiration from other motions, and we remove periods that include movements such as turning over to eliminate detrimental effects. We have implemented and validated Wital on low-cost COTS devices. The experimental results demonstrate the effectiveness of Wital in monitoring vital signs.