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

Abstract--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-T erm 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. Non-intrusive monitoring of vital signs (such as heart, breathing rate, and sleep apnea) has become increasingly important, particularly for home care, elderly care, and managing chronic conditions. As the global population ages and chronic disease rates increase, there is a growing need for continuous and accurate vital sign monitoring systems that can be easily deployed across the healthcare continuum, including hospitals, long-term care and home care settings [1]. Breathing and heart rate provides critical information about an individual's respiratory and cardiovascular health. Furthermore, detection of apnea, characterized by temporary pauses in breathing (typically lasting 10 seconds or longer) [2], is critical as conditions like sleep apnea affect millions worldwide and can lead to serious health complications if undiagnosed [3]. Thus, non invasive monitoring of these cardiopulmonary variables is necessary. Traditional approaches for vital sign monitoring have relied heavily on contact-based sensors such as pulse oximeters, heart rate belts, chest straps, or highly specialized medical equipment, such as polysomnography (PSG) or electrocardiogram (ECG) devices.