The widespread use of Wi-Fi infrastructure in indoor environments allows for non-intrusive health monitoring. While traditional vital sign monitoring relies on specialized equipment that can be expensive, uncomfortable, or not viable for long-term use, recent advances in wireless sensing allow for continuous, contactless monitoring using commodity devices. Our work looks at how Channel State Information (CSI) from low-cost, single antenna Wi-Fi devices can be used for accurate heart rate monitoring using compact deep learning.
Our experimental validation is done using two datasets, including the most CSI-based heart rate dataset with 118 participants in 17 positions, which shows that PulseFi achieves state-of-the-art accuracy (Mean absolute error 0.08 BPM) compared to existing methods, while maintaining nearly perfect consistency across varying distances between transmitter/receiver, and positions. By using single antenna amplitude information alone, PulseFi significantly reduces the hardware requirements and computational complexity, with the models being able to run locally real-time on the ESP32 chips (<600KB of RAM).
