Contactless Respiratory Monitoring on Heterogeneous Mobile Robots: A Multimodal Edge-Computing Framework
This paper presents a modality-adaptive contactless respiratory rate monitoring framework for heterogeneous mobile robots with onboard edge computing. It combines brightness-adaptive sensor selection across RGB, thermal, NIR, and low-light cameras, keypoint-guided chest ROI extraction, and SQI-based filtering. Experiments on quadruped and wheeled platforms demonstrate generalization without per-platform retuning: RGB covers up to 8m, NIR up to 6m, thermal short-range only, low-light effective in complete darkness up to 8m. The framework supports autonomous triage and victim assessment in hazardous environments.
[2606.17376] Contactless Respiratory Monitoring on Heterogeneous Mobile Robots: A Multimodal Edge-Computing Framework
[Submitted on 16 Jun 2026]
Title:Contactless Respiratory Monitoring on Heterogeneous Mobile Robots: A Multimodal Edge-Computing Framework
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Abstract:Respiratory-rate (RR) monitoring is a critical component of remote triage and victim assessment in emergency response, disaster recovery, and infectious-disease scenarios, where minimizing physical contact can reduce responder risk and improve operational safety. However, field deployment of contactless RR monitoring remains challenging due to variable illumination, posture changes, platform heterogeneity, and the impracticality of wearable sensors in hazardous environments. In this paper, we present a modality-adaptive contactless RR monitoring framework for heterogeneous mobile robots with onboard edge computing. The proposed system combines brightness-adaptive sensor selection across RGB, thermal, near-infrared (NIR), and low-light cameras, keypoint-guided chest ROI extraction for posture-robust monitoring, and a signal-quality-index (SQI)-based filtering mechanism for reliable respiratory estimation. We implement and evaluate the framework on three robotic platforms spanning quadruped and wheeled locomotion and multiple edge-computing architectures. Experiments conducted across diverse lighting conditions, subject poses, and robot-to-subject distances demonstrate that the framework generalizes across platforms without per-platform algorithmic retuning, while revealing modality-specific operational boundaries. RGB provides the broadest coverage up to 8m, NIR remains effective up to 6m, thermal is reliable only at short range, and low-light sensing supports monitoring in complete darkness up to 8m. Overall, the results demonstrate the feasibility of multimodal contactless RR monitoring on mobile robots and support its use as a foundation for autonomous triage and victim assessment in hazardous search-and-rescue settings.
Comments: 8 pages, 6 figures. To appear in Proceedings of the 8th International Workshop on IoT Applications and Industry 5.0 (IoTI5 2026), co-located with IEEE DCOSS-IoT 2026, Reykjavik, Iceland, June 2026
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
ACM classes: C.2.1; I.4.9; J.3
Cite as: arXiv:2606.17376 [cs.RO]
(or arXiv:2606.17376v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.17376
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Milind Ramesh Rampure [view email] [v1] Tue, 16 Jun 2026 00:18:46 UTC (5,972 KB)
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