Quantitative Evaluation of KIRETT Wearable Demonstrator for Rescue Operations
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909816558977024 |
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| author | Nadeem, Mubaris Zenkert, Johannes Bender, Lisa Weber, Christian Fathi, Madjid |
| author_facet | Nadeem, Mubaris Zenkert, Johannes Bender, Lisa Weber, Christian Fathi, Madjid |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25928 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantitative Evaluation of KIRETT Wearable Demonstrator for Rescue Operations Nadeem, Mubaris Zenkert, Johannes Bender, Lisa Weber, Christian Fathi, Madjid Artificial Intelligence 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. |
| title | Quantitative Evaluation of KIRETT Wearable Demonstrator for Rescue Operations |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.25928 |