From Contactless Perception to Early Warning: AI-Driven Risk Assessment on Mobile Robots

Mobile robots operating in clinical and assistive environments require the ability to monitor human physiological states without direct contact. While remote photoplethysmography (rPPG) enables the estimation of vital signs from video, existing approaches focus on signal extraction and rarely address downstream clinical decision-making. In this work, we present an AI-driven multimodal pipeline that bridges contactless physiological perception and early warning risk assessment on a mobile robotic platform. The system is deployed on a TIAGo Pro robot equipped with an Intel RealSense D435 sensor, enabling synchronized RGB, near-infrared, and depth acquisition. A multimodal pipeline combines depth-stabilised region-of-interest selection, adaptive RGB–NIR fusion for cardiac estimation, and a depth-based respiratory channel, aiming to provide robust estimates of heart rate, respiratory rate, and oxygen saturation. Building on these measurements, we introduce an interpretable early warning risk assessment module inspired by clinical scoring systems, designed to operate under uncertain sensing conditions. The system is designed as a ROS~2 perception node and will be evaluated through a staged validation plan. This work advances risk-aware embodied perception for contactless monitoring, enabling mobile robots to support continuous and non-invasive patient assessment in real-world scenarios.