GeoVision-Enabled Digital Twin for Hybrid Autonomous-Teleoperated Medical Responses
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 16 references that are in the map. First posted on arXiv 14 Apr 2026.
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4 of 16 references.
References · 4
- 2019Advances and Open Problems in Federated Learningcited by 3,046
- 2018Federated Optimization in Heterogeneous Networkscited by 2,987
- 2020The Future of Digital Health with Federated Learningcited by 633
- 2025Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecastingcited by 2
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