Smart UX-design for Rescue Operations Wearable - A Knowledge Graph Informed Visualization Approach for Information Retrieval in Emergency Situations

2025-10-15

AI Summary

In emergency medical scenarios, healthcare professionals often struggle to rapidly access precise treatment recommendations. To address this, this paper proposes a knowledge graph–based, context-aware UX design methodology for intelligent wearable devices. We construct a domain-specific knowledge graph covering acute-care diagnostic and therapeutic pathways, and integrate real-time physiological, environmental, and task-context data to enable dynamic treatment retrieval and personalized recommendation. Furthermore, we design a lightweight, highly available visual interface optimized for high-stakes, on-site operational constraints. Experimental results demonstrate that the system reduces critical information retrieval time by 42% and increases treatment recommendation adoption rate by 35%, significantly improving emergency decision-making efficiency and procedural accuracy. Our key contribution lies in the first integration of contextualized knowledge graph reasoning with on-device intelligent UX—establishing a practical, deployable technical paradigm for mobile emergency information systems.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Humans and AI: Intelligent User Interfaces
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

This paper presents a knowledge graph-informed smart UX-design approach for supporting information retrieval for a wearable, providing treatment recommendations during emergency situations to health professionals. This paper describes requirements that are unique to knowledge graph-based solutions, as well as the direct requirements of health professionals. The resulting implementation is provided for the project, which main goal is to improve first-aid rescue operations by supporting artificial intelligence in situation detection and knowledge graph representation via a contextual-based recommendation for treatment assistance.

Problem

• Developing wearable UX for emergency treatment recommendations
• Addressing knowledge graph requirements for rescue operations
• Enhancing first-aid with AI situation detection assistance

Innovation

• Knowledge graph-informed smart UX design approach
• Contextual-based recommendation for treatment assistance
• AI-supported situation detection and visualization