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

2026-06-29

AI Summary

This study addresses the challenge of unstable respiratory and cardiac monitoring using millimeter-wave radar in unconstrained home environments, where performance degrades due to sensitivity to observation geometry. The authors propose a vision-radar cooperative active perception framework that, for the first time, incorporates sensing geometry as a controllable variable in robotic motion planning. By leveraging visual guidance to dynamically adjust the radar’s pose—aligning it nearly perpendicularly to the thoracic surface—the system maximizes observability of radial physiological motion. This approach establishes a closed-loop perception-action pipeline integrating visual keypoint localization, robotic motion control, millimeter-wave signal processing, and differential phase enhancement. Experimental results demonstrate significant improvements: respiratory interval error decreases from 0.87 s to 0.14 s, and heart rate error drops from 13.59 bpm to 2.22 bpm, achieving accuracy in free-living conditions comparable to that in static, controlled settings.

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.

Problem

• vital signs monitoring
• mmWave radar
• observation geometry
• home healthcare robots
• non-contact sensing

Innovation

• geometry-aware sensing
• active perception
• mmWave radar
• vital signs monitoring
• vision-guided control