google·Jun 4, 2026·3 min readCurated summary

Towards passive heart health monitoring via smartphone camera

PHRM is a Google Research system that passively estimates heart rate and daily resting heart rate using short facial videos captured by a smartphone’s front-facing camera after face unlock. It applies remote photoplethysmography and efficient deep learning, achieving wearable-comparable accuracy in laboratory and real-world settings. A central contribution is its deliberate validation across skin tones, including substantial representation of participants with dark skin. ## Passive Heart Monitoring with a Smartphone - PHRM records approximately eight seconds of facial video during everyday phone use. - It estimates heart rate from changes in how light reflects from the skin as blood pulses through facial tissue. - The system uses: - Temporal shift convolutional neural networks for efficient on-device prediction. - Confidence scores to filter unreliable measurements. - Kalman filtering and daily aggregation to estimate resting heart rate. - Unlike earlier smartphone approaches requiring a finger over the camera, PHRM works passively in the background. ## Designing for Skin-Tone Inclusivity - Existing remote PPG research often relied on small, controlled studies and underrepresented people with dark skin. - Google trained PHRM on more than 350,000 video clips from nearly 700 participants across laboratory and real-world conditions. - Dataset targets based on the Monk Skin Tone scale ensured: - At least 25% representation of light and medium skin tones. - At least 33% representation of dark skin tones. - The researchers required heart-rate error differences between skin-tone groups to remain below five percentage points. ## Laboratory Validation - The model was trained using synchronized facial video and ECG recordings from 365 diverse participants. - On a separate 104-person test set, PHRM achieved mean absolute percentage error below 10% for every skin-tone group after confidence filtering. - It outperformed 15 leading published remote-PPG models and was the only evaluated model to meet the under-10% target across all groups. ## Real-World Smartphone Study - In an eight-day free-living study, 231 participants used their own phones normally while wearing an ECG chest strap and Fitbit Charge 6. - The research app captured an average of 231 facial clips per participant per day after face unlock. - Participants reviewed and explicitly authorized uploads each day; recordings were sent to secure, encrypted servers. - On a held-out group of 101 participants: - Overall MAPE was 6.09% after confidence gating. - MAPE was 5.04% for light skin, 5.12% for medium skin, and 7.84% for dark skin. - The average underestimation was only 0.64 beats per minute. - The model again outperformed the 15 comparison systems. ## Resting Heart Rate and Research Access - By combining reliable heart-rate estimates throughout the day, PHRM produced daily resting-heart-rate estimates with mean absolute error below five beats per minute compared with a wearable tracker. - Google released the largest and most diverse publicly available smartphone-video dataset for remote PPG research, along with a pre-trained “PHRM-mini” model. - Access is available to qualified researchers. PHRM demonstrates that smartphones could broaden access to passive cardiovascular monitoring without requiring dedicated wearable hardware. Before clinical use, the system would still require continued validation, careful privacy protections, and assessment across additional devices and populations.

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PhotoplethysmographyDeep LearningComputer VisionConvolutional Neural Networks+3

google·Mar 19, 2025Original article

Loss of Pulse Detection on the Google Pixel Watch 3 (opens in new tab)

Google Research has developed a "Loss of Pulse Detection" feature for the Pixel Watch 3 to address the high mortality rates associated with unwitnessed out-of-hospital cardiac arrests (OHCA). By utilizing a multimodal algorithm that combines photoplethysmography (PPG) and accelerometer data, the device can automatically identify the transition to a pulseless state and contact emergency services. This innovation aims to transform unwitnessed medical emergencies into functionally witnessed ones, potentially increasing survival rates by ensuring timely intervention. ### The Impact of Witness Status on Survival * Unwitnessed cardiac arrests currently face a major public health challenge, with survival rates as low as 4% compared to 20% for witnessed events. * The "Chain of Survival" traditionally relies on human bystanders to activate emergency responses, leaving those alone at a significant disadvantage. * Every minute without resuscitation decreases the chance of survival by 7–10%, making rapid detection the most critical factor in prognosis. * Converting an unwitnessed event into a "functionally witnessed" one via a wearable device could equate to a number needed to treat (NNT) of only six people to save one life. ### Multimodal Detection and the Three-Gate Process * The system uses PPG sensors to measure blood pulsatility by detecting photons backscattered by tissue at green and infrared wavelengths. * To prevent false positives and errant emergency calls, the algorithm must pass three sequential "gates" before making a classification. * **Gate 1:** Detects a sudden, significant drop in the alternating current (AC) component of the green PPG signal, which suggests a transition from a pulsatile to a pulseless state, paired with physical stillness. * **Gate 2:** Employs a machine learning algorithm trained on diverse user data to quantify the probability of a true pulseless transition. * **Gate 3:** Conducts additional sensor checks using various LED and photodiode geometries, wavelengths, and gain settings to confirm the absence of even a weak pulse. ### On-Device Processing and User Verification * All data processing occurs entirely on the watch to maintain user privacy, consistent with Google’s established health data policies. * If the algorithm detects a loss of pulse, it initiates two check-in prompts involving haptic, visual, and audio notifications to assess user responsiveness. * The process can be de-escalated immediately if the user moves their arm purposefully, ensuring that emergency services are only contacted during true incapacitation. * When a user remains unresponsive, the watch automatically contacts emergency services to provide the individual's current location and medical situation. By providing a passive, opportunistic monitoring system on a mass-market wearable, this technology offers a critical safety net for individuals at risk of unwitnessed cardiac events. For the broader population, the Pixel Watch 3 serves as a life-saving tool that bridges the gap between a sudden medical emergency and the arrival of professional responders.

PhotoplethysmographyAIMachine LearningWearable Technology+5