Quantitative Evaluation of KIRETT Wearable Demonstrator for Rescue Operations

TL;DR

This study evaluates the KIRETT wearable demonstrator in a two-day field test with 14 rescue operators to assess the demand for digitalization and AI-assisted decision support in emergency medicine. Leveraging a local IoT wearable with FPGA-based hardware acceleration and a knowledge-graph–driven decision system, the study combines hands-on testing with qualitative and quantitative assessments. Results reveal a strong need for real-time analysis, hygienic and glove-friendly usability, and hospital-system integration for vitals and treatment reporting, guiding design toward compact, high-ergonomics wearables. The findings underscore the potential of real-time treatment recommendations to improve patient outcomes and inform future evaluations of the KIRETT approach in emergency workflows.

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.

Figures (7)

• Figure 1: Setup of the KDT: A test group of two rescuers (left), will initially be provided an introduction and conduct a pre-interview with regards on personal information and their experience with artificial intelligence. Afterwards they have n-many iterations of various test-cases exploring the KIRETT-demonstrator. Subsequently, a qualitative interview follows, discussion various experiences gained in the testing phase. A Hardware-Table is provided to check and elaborate the KDT-hardware with a questionnaire at the end.
• Figure 2: Setup of the KDT: The dummy with the sofware-based KIRETT-demonstrator. A shows the demonstrator setup and B the active usecase-treatment with rescue operators.
• Figure 3: Hardware related Additional Improvements This figure presents the, in the KDT presented, hardware related questions, which indicate a high important factor in hygience and cleaning of devices. Beside a small formfactor, a easy to clean and desinfect device is much wished from the rescue operators.
• Figure 4: Age distribution of the 14 participants of the KDT Testing.
• Figure 5: Additional features for the KIRETT-demonstrator were identified to add further features to provide a much more domain-specific device.