Smart Firefighting: A Deep Learning Approach to Tracking Firefighter Movements

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10259280" target="_blank" >RIV/61989100:27240/25:10259280 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/25:10259280

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11268769" target="_blank" >https://ieeexplore.ieee.org/document/11268769</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICUMT67815.2025.11268769" target="_blank" >10.1109/ICUMT67815.2025.11268769</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Smart Firefighting: A Deep Learning Approach to Tracking Firefighter Movements

Popis výsledku v původním jazyce

In recent years, there has been increased interest in using advanced technologies such as artificial intelligence, particularly in public safety and rescue operations. This paper focuses on an innovative approach to monitoring and analysing the movement of firefighters during rescue operations using artificial intelligence. In our research, we implemented a system that uses data obtained from sensors placed on the protective suits of firefighters. This data is analysed using deep-learning neural networks after advanced data preprocessing. The goal is to provide a more accurate real-time interpretation of firefighter movement, improving rescue teams’ coordination and increasing firefighters’ safety in their work. This paper presents the results of initial experiments that demonstrate the effectiveness of the proposed system in different rescue operation scenarios. At the end of the paper, we also discuss possible challenges and directions for further research in this area. Our work represents an important step towards integrating artificial intelligence into critical public safety operations. It offers new opportunities for improving rescue operations and protecting lives.

Název v anglickém jazyce

Smart Firefighting: A Deep Learning Approach to Tracking Firefighter Movements

Popis výsledku anglicky

In recent years, there has been increased interest in using advanced technologies such as artificial intelligence, particularly in public safety and rescue operations. This paper focuses on an innovative approach to monitoring and analysing the movement of firefighters during rescue operations using artificial intelligence. In our research, we implemented a system that uses data obtained from sensors placed on the protective suits of firefighters. This data is analysed using deep-learning neural networks after advanced data preprocessing. The goal is to provide a more accurate real-time interpretation of firefighter movement, improving rescue teams’ coordination and increasing firefighters’ safety in their work. This paper presents the results of initial experiments that demonstrate the effectiveness of the proposed system in different rescue operation scenarios. At the end of the paper, we also discuss possible challenges and directions for further research in this area. Our work represents an important step towards integrating artificial intelligence into critical public safety operations. It offers new opportunities for improving rescue operations and protecting lives.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/VJ02010037" target="_blank" >VJ02010037: Monitorování polohy příslušníků složek IZS i během zásahu v rozsáhlých budovách s využitím prvků umělé inteligence</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

International Congress on Ultra Modern Telecommunications and Control Systems and Workshops 2025

ISBN

979-8-3315-7676-9

ISSN

2157-0221

e-ISSN

2157-023X

Počet stran výsledku

8

Strana od-do

"neuvedeno"

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Florencie

Datum konání akce

3. 11. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001669273500041