Smart UX-design for Rescue Operations Wearable - A Knowledge Graph Informed Visualization Approach for Information Retrieval in Emergency Situations
Mubaris Nadeem, Johannes Zenkert, Christian Weber, Madjid Fathi, Muhammad Hamza
TL;DR
The paper addresses the challenge of rapid, accurate information retrieval for emergency health professionals using a wearable device. It presents a knowledge graph–informed smart UX that leverages a Neo4j knowledge graph, AI-driven situation detection, and memory-mapped inter-processor communication to deliver contextual treatment recommendations via a graph-based interface. Methodological contributions include a dual-processor architecture (A53,R5) with dynamic text-to-image rendering to cope with LCD constraints, and a convention-ID encoding scheme for compact situation representation. The work demonstrates a concrete integration of KG-driven inference with emergency UX design, aiming to improve response times and decision quality in real-world rescue operations, with planning for simulated trials and audio navigation. The practical impact lies in enabling health professionals to access structured treatment paths and patient data at the point of care under stress and field constraints.
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.