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Blockchain‐Enabled AI for Smart Head Counting and Emergency Mustering System.

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Title: Blockchain‐Enabled AI for Smart Head Counting and Emergency Mustering System.
Authors: Saad, Muhammad1 (AUTHOR), Ahmad, Maaz Bin1 (AUTHOR), Asif, Muhammad2 (AUTHOR), Ali, Syed Mubashir3 (AUTHOR), Hamdi, Monia4 (AUTHOR), Abdin, Asif Ali Zain Ul1 (AUTHOR), Sarwar, Nadeem3 (AUTHOR) nsarwar.bulc@bahria.edu.pk, Galli, Antonio (AUTHOR) antonio.galli@unina.it
Source: IET Software (Wiley-Blackwell). 6/30/2026, Vol. 2026, p1-22. 22p.
Subjects: Human facial recognition software, Blockchains, Industrial safety, Artificial intelligence, Real-time computing, Building evacuation, Quantitative research, Internet of things
Abstract: This research provides a comprehensive framework for the digitalization of head counting and the emergency mustering system. An end‐to‐end framework and solution is proposed for the developing countries by prioritizing health and safety, particularly in the industrial sector, where a large number of employees face significant risks during chaotic situations. It addresses the limitations of manual and traditional head counting during emergencies by providing real‐time analytics through facial recognition, enabling the identification of accounted‐for and missing personnel. The work integrates blockchain as compared to traditional methods along with the Internet of Things (IoT), Messaging Queue Telemetry Transport (MQTT), and wireless technologies to create an end‐to‐end framework for automating the headcount process. The study emphasizes using blockchain and IoT‐enabled terminals, facilitating seamless data transmission to a blockchain‐enabled network. The cross‐platform software application is also developed to represent the data in the form of a dashboard on the web and mobile. The pilot project was implemented in an industrial environment to collect experimental logs and validate the robustness of this highly integrated system. The proposed system achieved 99.8% accuracy with a precision of 99.6%, a recall of 99.7%, and an F1‐score of 99.65%, reducing errors from 8% to 0.2% and reducing the emergency response time by 35% as compared to traditional roll‐call methods. Future research may focus on scaling the system using artificial intelligence (AI) cameras and 5G technology to enhance mobility and headcount accuracy. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:This research provides a comprehensive framework for the digitalization of head counting and the emergency mustering system. An end‐to‐end framework and solution is proposed for the developing countries by prioritizing health and safety, particularly in the industrial sector, where a large number of employees face significant risks during chaotic situations. It addresses the limitations of manual and traditional head counting during emergencies by providing real‐time analytics through facial recognition, enabling the identification of accounted‐for and missing personnel. The work integrates blockchain as compared to traditional methods along with the Internet of Things (IoT), Messaging Queue Telemetry Transport (MQTT), and wireless technologies to create an end‐to‐end framework for automating the headcount process. The study emphasizes using blockchain and IoT‐enabled terminals, facilitating seamless data transmission to a blockchain‐enabled network. The cross‐platform software application is also developed to represent the data in the form of a dashboard on the web and mobile. The pilot project was implemented in an industrial environment to collect experimental logs and validate the robustness of this highly integrated system. The proposed system achieved 99.8% accuracy with a precision of 99.6%, a recall of 99.7%, and an F1‐score of 99.65%, reducing errors from 8% to 0.2% and reducing the emergency response time by 35% as compared to traditional roll‐call methods. Future research may focus on scaling the system using artificial intelligence (AI) cameras and 5G technology to enhance mobility and headcount accuracy. [ABSTRACT FROM AUTHOR]
ISSN:17518806
DOI:10.1049/sfw2/3472254