# PulseFi: A Low Cost Robust Machine Learning System for Accurate Cardiopulmonary and Apnea Monitoring Using Channel State Information

arXiv: https://arxiv.org/abs/2510.24744 · HTML: https://mlsanity.com/paper/2510.24744/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 56 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DistancePPG: Robust non-contact vital signs monitoring using a camera](https://mlsanity.com/paper/1502.08040/) (2015)
- [Dual Refinement Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/2012.01733/) (2020)
- [Wital: A COTS WiFi Devices Based Vital Signs Monitoring System Using NLOS Sensing Model](https://mlsanity.com/paper/2305.14490/) (2023)
- [Wi-Fi Sensing: Applications and Challenges](https://mlsanity.com/paper/1901.00715/) (2019)
- [Human Biometric Signals Monitoring based on WiFi Channel State Information using Deep Learning](https://mlsanity.com/paper/2203.03980/) (2022)
- [ComplexBeat: Breathing Rate Estimation from Complex CSI](https://mlsanity.com/paper/2502.12657/) (2025)

## Built on by (most cited first, 1 of 1)

- [WiFlow: Estimating Optical Flow using WiFi Channel State Information](https://mlsanity.com/paper/2609.02452/) (2026)

## Abstract (arXiv)

Non-intrusive monitoring of vital signs has become increasingly important in a variety of healthcare settings. In this paper, we present PulseFi, a novel low-cost non-intrusive system that uses Wi-Fi sensing and artificial intelligence to accurately and continuously monitor heart rate and breathing rate, as well as detect apnea events. PulseFi operates using low-cost commodity devices, making it more accessible and cost-effective. It uses a signal processing pipeline to process Wi-Fi telemetry data, specifically Channel State Information (CSI), that is fed into a custom low-compute Long Short-Term Memory (LSTM) neural network model. We evaluate PulseFi using two datasets: one that we collected locally using ESP32 devices and another that contains recordings of 118 participants collected using the Raspberry Pi 4B, making the latter the most comprehensive data set of its kind. Our results show that PulseFi can effectively estimate heart rate and breathing rate in a seemless non-intrusive way with comparable or better accuracy than multiple antenna systems that can be expensive and less accessible.