WiFi Signals Can Measure Heart Rate—No Wearables Needed Date: September 2, 2025 Author: Emily Cerf Source: University of California, Santa Cruz (UCSC) News --- Key Takeaways Pulse-Fi System: Uses ultra low-cost WiFi devices to achieve clinical-level heart rate monitoring accuracy, suitable for low-resource settings. Versatile Functionality: Works accurately regardless of user position (sitting, standing, lying down, walking) or equipment placement. Distance: Effective from up to 10 feet (3 meters) away; longer distances show promising results. --- Overview UC Santa Cruz engineers have developed Pulse-Fi, a novel technology leveraging standard WiFi signals to accurately monitor heart rate without the need for wearables. This system uses low-cost WiFi devices combined with advanced machine learning to detect subtle signal variations caused by a person's heartbeat. Importance of Heart Rate Monitoring Heart rate provides insights into physical activity, stress, anxiety, hydration, and overall health. Traditionally, heart rate monitoring requires wearable devices or hospital machinery, which can be intrusive or expensive. Research and Publication The Pulse-Fi technology has been demonstrated through a proof-of-concept study published at the 2025 IEEE International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT). --- How Pulse-Fi Works: Measuring with WiFi Setup: Uses a WiFi transmitter and receiver, often devices costing between $5 to $30 (ESP32 and Raspberry Pi chips). Signal Processing: Radio frequency waves are sensitive to objects and human bodies; subtle changes caused by heartbeats are isolated using filtering and machine learning. Machine Learning Model: Trained to distinguish heartbeat signals from environmental noise or other movement. Data Collection: Researchers created their own dataset by pairing WiFi signal data with standard oximeter readings from 118 participants. Experimental Highlights Accuracy rivaling clinical devices after just 5 seconds of data collection. Tested 17 body positions per participant with consistent performance. System performance unaffected by the physical distance between sensor and person within the tested range. ESP32 chips used for primary tests; Raspberry Pi-based experiments showed even better results. --- Creating the Dataset Due to lack of existing datasets for ESP32 WiFi-based heart rate detection, researchers collected new data in UCSC’s Science and Engineering library: They used a standard oximeter to record "ground truth" heart rate. Combined oximeter data with WiFi signal patterns to train a neural network to identify heartbeat-related signal changes. Also utilized an extensive dataset from a Brazilian research team using Raspberry Pi devices for validation. --- Beyond Heart Rate The team is extending Pulse-Fi to detect breathing rate, which can help diagnose conditions such as sleep apnea. Early, unpublished results demonstrate promising accuracy in apnea detection. --- Contact and Further Information For commercial inquiries, contact: Marc Oettinger Assistant Director of Innovation Transfer marc.oettinger@ucsc.edu The full paper can be accessed here: Pulse-Fi Research Paper (PDF) --- Related Topics Health Technology --- Image Captions Main image: Ph.D. student Nayan Bhatia demonstrates Pulse-Fi technology with a WiFi device and a laptop displaying heart rate data. Lab photo: UCSC Professor Katia Obraczka and Ph.D. student Nayan Bhatia in the research lab. Research hardware: Low-cost ESP32 WiFi chip used in experiments. Research intern: Pranay