Toward a ML Framework for Multisensory Human Health and Awareness

Abstract:

This paper proposes a conceptual framework for an intelligent soldier monitoring system, integrating multimodal sensor networks with Multimodal Large Language Models (MLLMs) to advance battlefield healthcare and situational awareness. The envisioned architecture combines physiological sensors (e.g., heart rate variability, cortisol levels, core temperature) with environmental sensors (e.g., acoustic, visual, thermal) through an edge-AI processing pipeline. Based on our literature review, we target three key limitations in existing systems: (1) real-time data fusion latency (aiming for <100 ms), (2) predictive health analytics accuracy (aiming for >90% for critical conditions), and (3) adaptive threat response capabilities. Our research suggests that the proposed technologies are at an early conceptual stage, supported by analysis of existing component technologies not yet integrated into a cohesive system. We identify challenges in power efficiency (targeting <50 mW per sensor) and ethical implementation, proposing solutions such as on-device processing and explainable AI. This work establishes a theoretical foundation and a research roadmap for future development of advanced military monitoring systems, balancing performance with operational and ethical considerations..

• Periodical: Applied Mechanics and Materials (Volume 935)
• Pages: 121-129
• DOI: https://doi.org/10.4028/p-FvwP6Q
• Online since: April 2026
• Authors: Francis Anyebe Oteikwu, Osezua Ejodame, Norman Osa-Uwagbue*
• Keywords: AI-Driven Diagnostics, Military Wearables, Multimodal Large Language Models (MLLMs), Multimodal Sensors, Predictive Threat detection. , Soldier Health Monitoring

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