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arXiv · 2606.30275

ActiveVital: Geometry-Aware Embodied Vital Signs Monitoring for Home Healthcare Robots

Yuxuan Hu·Shihao Li·Yang Xiao·Gen Li·Feng Xu·Jianfei Yang
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Abstract

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.

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Yuxuan Hu, Shihao Li, Yang Xiao, Gen Li, Feng Xu, Jianfei Yang. 2026-06-29. ActiveVital: Geometry-Aware Embodied Vital Signs Monitoring for Home Healthcare Robots. https://arxiv.org/abs/2606.30275

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