GeoVision-Enabled Digital Twin for Hybrid Autonomous-Teleoperated Medical Responses

Parham Kebria; Soheil Sabri; Laura J Brattain

cs.ROApr 14, 2026

GeoVision-Enabled Digital Twin for Hybrid Autonomous-Teleoperated Medical Responses

Authors: Parham Kebria, Soheil Sabri, Laura J Brattain

Organizations: Electrical and Computer Engineering / North Carolina A&T State University / Greensboro, USA · Urban Digital Twin Lab, School of Modeling Simulation and Training / University of Central Florida / Orlando, USA · Department of Internal Medicine / College of Medicine, University of Central Florida / Orlando, USA

Abstract

Remote medical response systems are increasingly being deployed to support emergency care in disaster-affected and infrastructure-limited environments. Enabled by GeoVision capabilities, this paper presents a Digital Twin architecture for hybrid autonomous-teleoperated medical response systems. The proposed framework integrates perception and adaptive navigation with a Digital Twin, synchronized in real-time, that mirrors system states, environmental dynamics, patient conditions, and mission objectives. Unlike traditional ground control interfaces, the Digital Twin provides remote clinical and operational users with an intuitive, continuously updated virtual representation of the platform and its operational context, enabling enhanced situational awareness and informed decision-making.

Figures & tables

Fig. 1: The proposed GeoVision-enabled digital twin framework provides medical services to people in remote and inaccessible areas. Healthcare providers can communicate and monitor the patient’s vital signs while supervising the entire situation through the digital twin in real-time.

Fig. 2: The proposed GeoVision-enabled Digital Twin framework for medical and emergency response applications. GeoVision-enabled drone fleet traverses disaster zones to collect vital data, using Federated Learning to collaboratively update detection on-device models to preserve privacy and bandwidth in communication-limited environments.

Fig. 3: Mean policy performance with 95% confidence intervals across degradation levels, quantifying both expected outcomes and statistical uncertainty for each core metric.

Fig. 4: Median, P90, and P95 high-severity intervention delays versus degradation, emphasizing tail-risk behavior and worst-case clinical responsiveness by policy.

Fig. 5: Distributional comparison of high-severity intervention delay and operator workload by policy, showing central tendency and spread beyond mean-only summaries.

Fig. 6: Pareto trade-off between intervention delay and failure rate across operating conditions; larger bubbles indicate higher effective success, illustrating multi-objective policy positioning.

Policy	$\bar{T}_{\mathrm{int}}$ (min)	$\rho$	$R_{\mathrm{fail}}$	$\bar{W}$	Mission Time (min)
$\pi_{1}$	182.584	0.0247	0.1810	0.0381	392.199
$\pi_{2}$	50.947	0.2209	0.1224	0.0440	114.439
$\pi_{3}$	29.546	0.1295	0.0664	0.0200	140.881

TABLE I: Policy comparisons across all degradation and patient-load conditions. Lower is better for

\bar{T}_{\mathrm{int}}

R_{\mathrm{fail}}

, and

\bar{W}

; higher is better for

\rho

Digital Twins

Digital twins are virtual models synchronized with real systems to support monitoring, prediction, simulation, and decision-making.

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Decentralized Direct Volume Rendering: A Browser-Native GPU Architecture for MRI Digital Twins in Resource-Constrained Settings

Oserebameh Augustine Beckley

Lagos State University

3D Medical Imaging Digital Twins

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Digital Twin (DT) technology holds immense potential for surgical planning and personalized medicine. However, generating interactive, patient-specific anatomical twins currently relies on computationally heavy Server-Side Rendering (SSR) or expensive local workstations, creating significant barriers to deployment, especially in resource-constrained settings (RCS). This paper presents a decentralized, client-side WebGPU architecture that democratizes access to high-fidelity anatomical Digital Twins. By bypassing standard server-side rendering pipelines, the framework executes deterministic single-pass raymarching and morphological gradient calculations directly on low-cost integrated edge GPUs. Eliminating the network latency inherent to cloud-rendered solutions, the system achieves a Time to First Pixel (TTFP) of under 920.0ms and maintains stable interactivity at >= 82.0 FPS. Continuous Interaction Fidelity is maintained via uniform buffers, enabling zero-latency manipulation of tissue parameters for dynamic clinical decision-making. By proving that complex 3D medical simulations of patient-specific MRI scan can be executed natively in the browser without deep learning or external computational dependencies, this architecture provides a scalable, affordable foundation for the widespread clinical adoption of healthcare Digital Twins.

Abstract

Digital Twins

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