Forget smartwatches, scientists teach WiFi to monitor heartbeats

Researchers at the University of California, Santa Cruz have developed Pulse-Fi, a novel system that uses ordinary WiFi signals to monitor heart rate with clinical accuracy, eliminating the need for wearables or specialized medical devices. By leveraging inexpensive hardware like ESP32 chips and Raspberry Pi boards, Pulse-Fi applies machine learning algorithms to detect subtle variations in WiFi signals caused by heartbeats, filtering out noise from movement or environmental factors. Tested on 118 participants, the system achieved heart rate measurements with an error margin of just half a beat per minute after five seconds of processing, maintaining accuracy across different postures and activities. Pulse-Fi represents a significant advancement in non-intrusive health monitoring, potentially transforming everyday WiFi routers into health trackers capable of continuous heart rate monitoring. The technology works by analyzing how radio frequency waves are absorbed and scattered by the human body, with machine learning models trained on data collected alongside standard oximeters to recognize heartbeat-induced signal fluctuations. The system also demonstrated reliable performance up to three meters away