Researchers can now monitor your heart rate from 10 feet away, and you don't even need to stand still.
Researchers have proven they can check your heart rate with clinical-level accuracy just by being in the same room with you and your Raspberry Pi. That alone might make it our favorite Raspberry Pi project of the year.
In news straight out of a sci-fi script, a team of researchers from the University of California, Santa Cruz (UCSC), has developed a low-cost, contactless heart rate monitoring system that runs on standard Wi-Fi hardware and a machine learning algorithm. You don’t even need to wear any other wearable device – and it might even be more accurate than your $400 Apple Watch.
A paper on the study, which the researchers have dubbed “Pulse-Fi,” was published in the proceedings of the 2025 IEEE International Conference on Distributed Computing in Smart Systems and news shared on the UCSC website. So, how does it work?
The system operates by analyzing the subtle ways your body impacts Wi-Fi signals. When Wi-Fi waves travel through a room they’re absorbed by objects, including people. A beating heart causes tiny, barely-perceptible changes in that Wi-Fi signal. The Pulse-Fi study used a Raspberry Pi and even cheaper ESP32, which retail for between 5 and $10.
The researchers then made use of an Wi-Fi transmitter and receiver, which runs Pulse-Fi’s signal processing and machine learning algorithm. This algorithm was taught to distinguish even the faintest variations in signal caused by a human heart beat, and filtered out all impacts from the environment around it – even movement.

With the system trained to detect the faint variations coming through your heart and filtering out all other “noise” from the environment, the team then tested Pulse-Fi on 118 participants. The results were remarkable, showing that the system can measure your heart rate within half a beat per minute of error after just five seconds – and it works from up to 10 feet away.
Notably, performance wasn’t affected by the position of the person being measured, it worked whether they were sitting, standing, lying down, or even walking. In total, 17 different body positions were tested. Following the results, the researchers, which included Prof. Katia Obraczka, PhD student Nayan Bhatia, and high school student Pranay Kocheta, created a new dataset to teach their neural network, ensuring the system’s reliability.
The team is already working to expand the technology to detect breathing rate in addition to heart rate, which could be useful for monitoring conditions like sleep apnea.
As Raspberry Pi projects go, this might be the most impressive yet!
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