Multi-Sensor Fusion for AI-Driven Behavior Planning in Medical Applications

Mianhua Wu^1*Yuanyuan Qin¹jianming Chang²Li Li¹

• ¹Nanjing University of Chinese Medicine, Nanjing, China
• ²Southeast University, Dhaka, Dhaka, Bangladesh

Multi-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 realtime decision-making in medical applications, where precision, reliability, and adaptability are paramount. To 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 highlevel 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. We 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. These 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.1 Sample et al.