ActiveVital: Geometry-Aware Embodied Vital Signs Monitoring for Home Healthcare Robots
For home healthcare robots, this work enables accurate, contact-free vital signs monitoring without requiring constrained robot-human orientations, solving a practical bottleneck in real-world deployment.
ActiveVital addresses the challenge of reliable vital signs monitoring in unconstrained home settings by using a vision-guided sensing framework that actively adjusts robot geometry to maximize radial observability, reducing respiration interval error from 0.87 s to 0.14 s and heart rate error from 13.59 bpm to 2.22 bpm.
Home robots require reliable vital signs monitoring to support long-term companionship and safety in daily environments, yet obtaining respiration and heart rate without physical contact remains challenging in unconstrained home settings. Millimeter-wave (mmWave) radar offers a promising solution due to its phase sensitivity to sub-millimeter motions. However, mmWave measurements are fundamentally constrained by observation geometry, since only the radial component of motion is observable. Consequently, arbitrary robot-human orientations often introduce angular misalignment that destabilizes vital signs estimation. To address this limitation, we reformulate vital signs monitoring from passive signal recovery to active geometric regulation. We propose ActiveVital, a vision-guided sensing framework that treats sensing geometry as an explicit control variable for robots. It localizes the chest anchor via visual keypoints and converts alignment errors into control commands. This steers the robot-mounted radar toward near-normal incidence to the thoracic surface, maximizing radial observability within a perception-action loop. A differential phase enhancement module further stabilizes signal extraction under motion. Experiments show that ActiveVital reduces respiration interval error from 0.87 s to 0.14 s and heart rate error from 13.59 bpm to 2.22 bpm, achieving accuracy comparable to controlled static sensing while remaining robust under unconstrained robot-human configurations.