TY - GEN
T1 - Smart_Safe
T2 - 22nd IEEE International Conference on Mobile Ad-Hoc and Smart Systems, MASS 2025
AU - Stasa, Pavel
AU - Benes, Filip
AU - Svub, Jiri
AU - Holusa, Veroslav
AU - Obrusnikova, Miroslava
AU - Dulovec, Jan
AU - Hollesch, Lukas
AU - Unucka, Jakub
AU - Rhee, Jongtae
AU - Jung, Jin Woo
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This poster presents the Smart_Safe system, a modular platform for real-time safety management in indoor industrial environments. The system integrates wearable sensors, Auto-ID technologies (such as RFID and Bluetooth), and AI-based analytics to detect, evaluate, and prevent occupational safety risks. Its core functionality includes real-time tracking of workers, detection of critical events (such as falls or zone violations), and prevention of collisions between people and mobile robots or forklifts. The system is designed to be scalable, interoperable with existing infrastructure, and privacy-respecting through the use of anonymized tracking and local processing. Integration with edge computing and digital twins enables context-aware decision-making and dynamic response to incidents. Smart_Safe supports applications in warehouses, smart factories, and production halls with a focus on high-risk or high-traffic areas. Initial testing demonstrates the feasibility of using hybrid sensor networks and lightweight AI models to ensure workplace safety and optimize movement flows. The poster also outlines the international collaboration between Czech and Korean partners, highlighting the hardware-software co-design process and the future roadmap for deployment.
AB - This poster presents the Smart_Safe system, a modular platform for real-time safety management in indoor industrial environments. The system integrates wearable sensors, Auto-ID technologies (such as RFID and Bluetooth), and AI-based analytics to detect, evaluate, and prevent occupational safety risks. Its core functionality includes real-time tracking of workers, detection of critical events (such as falls or zone violations), and prevention of collisions between people and mobile robots or forklifts. The system is designed to be scalable, interoperable with existing infrastructure, and privacy-respecting through the use of anonymized tracking and local processing. Integration with edge computing and digital twins enables context-aware decision-making and dynamic response to incidents. Smart_Safe supports applications in warehouses, smart factories, and production halls with a focus on high-risk or high-traffic areas. Initial testing demonstrates the feasibility of using hybrid sensor networks and lightweight AI models to ensure workplace safety and optimize movement flows. The poster also outlines the international collaboration between Czech and Korean partners, highlighting the hardware-software co-design process and the future roadmap for deployment.
KW - Auto-ID technologies
KW - Collision prevention
KW - Real-time risk detection
KW - Smart safety systems
KW - Wearable sensors
UR - https://www.scopus.com/pages/publications/105026346249
U2 - 10.1109/MASS66014.2025.00081
DO - 10.1109/MASS66014.2025.00081
M3 - Conference contribution
AN - SCOPUS:105026346249
T3 - Proceedings - 2025 IEEE 22nd International Conference on Mobile Ad-Hoc and Smart Systems, MASS 2025
SP - 510
EP - 511
BT - Proceedings - 2025 IEEE 22nd International Conference on Mobile Ad-Hoc and Smart Systems, MASS 2025
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 October 2025 through 8 October 2025
ER -