Multi-sensor fusion for AI-driven behavior planning in medical applications
IntroductionMulti-sensor fusion has emerged as a transformative approach in AI-driven behavior planning for medical applications, significantly enhancing perception, decision-making, and adaptability in complex and dynamic environments. Traditional fusion methods primarily rely on deterministic tech...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2025.1588715/full |
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| Summary: | IntroductionMulti-sensor fusion has emerged as a transformative approach in AI-driven behavior planning for medical applications, significantly enhancing perception, decision-making, and adaptability in complex and dynamic environments. Traditional fusion methods primarily rely on deterministic techniques such as Kalman Filters or rule-based decision models. While effective in structured settings, these methods often struggle to maintain robustness under sensor degradation, occlusions, and environmental uncertainties. Such limitations pose critical challenges for real-time decision-making in medical applications, where precision, reliability, and adaptability are paramount.MethodsTo address these challenges, we propose an Adaptive Probabilistic Fusion Network (APFN), a novel framework that dynamically integrates multi-modal sensor data based on estimated sensor reliability and contextual dependencies. Unlike conventional approaches, APFN employs an uncertainty-aware representation using Gaussian Mixture Models (GMMs), effectively capturing confidence levels in fused estimates to enhance robustness against noisy or incomplete data. We incorporate an attention-driven deep fusion mechanism to extract high-level spatial-temporal dependencies, improving interpretability and adaptability. By dynamically weighing sensor inputs and optimizing feature selection, APFN ensures superior decision-making under varying medical conditions.ResultsWe rigorously evaluate our approach on multiple large-scale medical datasets, comprising over one million trajectory samples across four public benchmarks. Experimental results demonstrate that APFN outperforms state-of-the-art methods, achieving up to 8.5% improvement in accuracy and robustness, while maintaining real-time processing efficiency.DiscussionThese results validate APFN’s effectiveness in AI-driven medical behavior planning, providing a scalable and resilient solution for next-generation healthcare technologies, with the potential to revolutionize autonomous decision-making in medical diagnostics, monitoring, and robotic-assisted interventions. |
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| ISSN: | 2296-424X |