google Measuring heart rate with consumer ultra-wideband radar (opens in new tab)
Google Research has demonstrated that ultra-wideband (UWB) radar technology, which is already integrated into many modern smartphones for tasks like precise location and vehicle unlocking, can be repurposed for contactless heart rate monitoring. By employing a transfer learning approach, researchers successfully applied models trained on large datasets from Frequency Modulated Continuous Wave (FMCW) radar to the newer UWB systems. This development suggests that everyday consumer electronics could soon provide accurate vital sign measurements without the need for additional specialized sensors or physical contact. ## Leveraging Existing Consumer Hardware While Google previously used Soli radar (FMCW) for sleep sensing in the Nest Hub, UWB technology represents a more widely available hardware platform in the mobile market. * UWB is currently used primarily for non-radar applications like digital car keys and item tracking (e.g., Apple AirTags). * The technology is increasingly standard in high-end mobile phones, providing a ready-made infrastructure for health sensing. * Utilizing existing UWB chips eliminates the need for manufacturers to add dedicated medical sensors to devices. ## Overcoming Signal Interference in Vital Sensing The primary challenge in radar-based heart rate monitoring is that the micro-movements of the chest wall caused by a heartbeat are significantly smaller than movements caused by breathing or general body shifts. * The system utilizes three-dimensional spatial resolution to create a "measurement zone" focused specifically on the user's torso. * High temporal resolution, sampling at speeds up to 200Hz, allows the radar to capture the rapid, subtle pulses of a heartbeat. * By isolating reflections from the chest area, the radar can ignore stationary background objects and external movements that would otherwise corrupt the data. ## Cross-Radar Transfer Learning Because the researchers possessed extensive datasets for FMCW radar but very limited data for UWB, they developed a method to transfer learned features between different radar types despite their different physical principles. * FMCW radar transmits continuous sinusoidal waves, whereas UWB radar transmits extremely short pulses (picoseconds to nanoseconds). * The study used a large 980-hour FMCW dataset to "teach" the model the characteristics of human vitals. * This pre-trained knowledge was then applied to a smaller 37.3-hour UWB dataset, proving that heart rate features are consistent enough across hardware types for effective transfer learning. ## A Novel Spatio-Temporal Deep Learning Model The researchers designed a custom neural network architecture to process the complex multidimensional data generated by radar sensors. * The framework uses a 2D ResNet to analyze the input data across two axes: time and spatial measurements. * Following the initial analysis, the model uses average pooling to collapse the spatial dimension, focusing purely on the temporal signal. * A 1D ResNet then identifies long-range periodic patterns to estimate the heart rate. * The model achieved a mean absolute error (MAE) of 0.85 beats per minute (bpm), which is a 50% reduction in error compared to previous state-of-the-art methods. This research indicates that high-precision health monitoring can be integrated into the mobile devices users already carry. By transforming smartphones into passive health sensors, UWB technology could allow for continuous heart rate tracking during routine activities, such as sitting at a desk or holding a phone in one's lap.