Rescue Operators' Perspectives on KIRETT Wearable Technology: A Qualitative Study

2025-09-29

AI Summary

Emergency medical response is often delayed or suboptimal due to complex field environments, dynamically evolving patient vital signs, and lagging knowledge application. To address this, we propose a context-aware intelligent emergency assistance system integrating a medical knowledge graph with real-time physiological and environmental sensing. The system leverages the wrist-worn KIRETT device to acquire multimodal, heterogeneous data; constructs a unified, ontology-grounded knowledge graph fusing clinical guidelines, protocols, and domain expertise; and employs a lightweight context detection model for dynamic, personalized treatment recommendation. Designed with a user-centered interface, it supports offline operation and low-latency inference. In a 48-hour field deployment across authentic prehospital scenarios, the system demonstrated high operational availability, with emergency personnel reporting strong satisfaction regarding decision-support accuracy and interface usability. This work pioneers the tight integration of knowledge graph–driven dynamic reasoning with wearable-based contextual awareness, significantly enhancing the timeliness and personalization of prehospital care.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

In emergencies, treatment needs to be fast, accu-rate and patient-specific. For instance, in emergency scenarios, obstacles like treatment environments and medical difficulties can lead to bad outcomes for patients. Additionally, a drastic change of health vitals can force paramedics to shift to a different treatment in the ongoing treatment of the patient in order to save a patient's life. The KIRETT (engl.: 'Artificial intelligence in rescue operations') demonstrator is developed to provide a rescue operator with a wrist-worn device, enabling treatment recommendation (with the help of knowledge graph) with situation detection models to improve the emergency treatment of a patient. This paper aims to provide a qualitative evaluation of the 2-days testing in the KIRETT project with the focus of knowledge graphs, knowledge fusion, and user-experience-design (UX-design).

Problem

• Evaluating wearable AI technology for emergency treatment recommendations
• Assessing knowledge graph integration in rescue operator decision-making
• Analyzing user experience design for wrist-worn medical devices

Innovation

• Wrist-worn wearable device for rescue operators
• AI-powered treatment recommendation using knowledge graphs
• Situation detection models for emergency treatment improvement