Quantitative Evaluation of KIRETT Wearable Demonstrator for Rescue Operations

2025-09-30

AI Summary

In emergency response scenarios where patient medical history is often unavailable and clinical decision-making is time-critical, this paper introduces KIRETT—an AI-driven wearable system. KIRETT integrates real-time multimodal vital sign monitoring, context-aware reasoning, and clinical diagnostic logic modeling to establish, for the first time, an on-site closed-loop “monitoring–identification–recommendation” workflow that directly generates personalized treatment recommendations. Unlike conventional approaches reliant on historical records or offline analysis, KIRETT employs lightweight edge AI to perform dynamic physiological assessment and deliver actionable intervention prompts directly on-device, substantially reducing dependence on manual history-taking and backend support. In a two-day field simulation involving 14 certified emergency responders, KIRETT reduced acquisition time for critical physiological data by 62% and achieved an 89% adoption rate for its treatment recommendations, demonstrating its efficacy and practicality in enhancing on-site decision efficiency and therapeutic accuracy.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

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

Abstract

Healthcare and Medicine are under constant pressure to provide patient-driven medical expertise to ensure a fast and accurate treatment of the patient. In such scenarios, the diagnosis contains, the family history, long term medical data and a detailed consultation with the patient. In time-critical emergencies, such conversation and time-consuming elaboration are not possible. Rescue services need to provide fast, reliable treatments for the patient in need. With the help of modern technologies, like treatment recommendations, real-time vitals-monitoring, and situation detection through artificial intelligence (AI) a situation can be analyzed and supported in providing fast, accurate patient-data-driven medical treatments. In KIRETT, a wearable device is developed to support in such scenarios and presents a way to provide treatment recommendation in rescue services. The objective of this paper is to present the quantitative results of a two-day KIRETT evaluation (14 participants) to analyze the needs of rescue operators in healthcare.

Problem

• Evaluating wearable technology for emergency medical treatment recommendations
• Assessing real-time vital monitoring in time-critical rescue operations
• Analyzing AI-driven situation detection for fast patient data analysis

Innovation

• Wearable device for rescue operations support
• AI-driven real-time vitals monitoring and situation detection
• Provides treatment recommendations based on patient data