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AISAFETY: An AI-based smart system for enhancing operator safety in production processes.

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Title: AISAFETY: An AI-based smart system for enhancing operator safety in production processes.
Authors: Paco, Francesco Di1 (AUTHOR) francesco.dipaco@phd.unipi.it, Burattini, Luca2 (AUTHOR) luca.burattini@dottorandi.unipg.it, Gabbrielli, Roberto1 (AUTHOR) roberto.gabbrielli@unipi.it, Landi, Luca2 (AUTHOR) luca.landi@unipg.it, Marcelloni, Francesco3 (AUTHOR) francesco.marcelloni@unipi.it, Marrazzini, Leonardo1 (AUTHOR) leonardo.marrazzini@unipi.it, Palumbo, Marco3 (AUTHOR) marco.palumbo@unipi.it, Pirozzi, Marco4 (AUTHOR) m.pirozzi@inail.it
Source: Safety Science. Jul2026, Vol. 199, pN.PAG-N.PAG. 1p.
Subjects: Radio frequency identification systems, Computer vision, Industrial processing equipment, Industrial safety, Human-machine systems, Expert systems, System safety, Industry 4.0
Abstract: • An AI-based supervision system aligned with the EU Machinery Regulation is proposed. • RFID, Computer Vision, and Rule-based AI are integrated for industrial safety. • Detection of unsafe human–machine interactions is enhanced through layered sensing. • A prototype is implemented on a CNC lathe and collaborative robot testbed. • Adaptive and human-centred safety management in Industry 4.0 is supported. The forthcoming EU Machinery Regulation represents a significant evolution in industrial safety by enabling the use of Artificial Intelligence (AI) and autonomous technologies for safety-related functions in collaborative human–machine environments. However, these environments introduce new safety challenges related to human behaviour, improper machine use, and potential manumission of traditional safety measures. The aim of this study is to propose an integrated AI-based supervision system, named AISAFETY, designed to enhance operator safety in production processes through continuous monitoring and intelligent decision support. The proposed system is based on a layered architecture that integrates Radio-Frequency Identification for real-time operator localization, Computer Vision for visual monitoring, and a machine control layer governed by a rule-based expert system for managing safety–critical scenarios. The combination of heterogeneous sensing technologies provides redundancy and robustness in detecting hazardous conditions, such as unauthorized operator presence, tampering with safety guards, and improper collaborative robot operation. To support future validation, a prototype system has been implemented on an experimental setup consisting of a multimodal CNC lathe and a collaborative robot. A set of representative use cases has been defined to model potential hazardous situations and to guide the planned experimental evaluation of the system's safety supervision capabilities. The proposed approach highlights the potential of AI- and IoT-based supervision systems to support the implementation of upcoming regulatory frameworks and to advance human-centred safety management in collaborative industrial environments. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:• An AI-based supervision system aligned with the EU Machinery Regulation is proposed. • RFID, Computer Vision, and Rule-based AI are integrated for industrial safety. • Detection of unsafe human–machine interactions is enhanced through layered sensing. • A prototype is implemented on a CNC lathe and collaborative robot testbed. • Adaptive and human-centred safety management in Industry 4.0 is supported. The forthcoming EU Machinery Regulation represents a significant evolution in industrial safety by enabling the use of Artificial Intelligence (AI) and autonomous technologies for safety-related functions in collaborative human–machine environments. However, these environments introduce new safety challenges related to human behaviour, improper machine use, and potential manumission of traditional safety measures. The aim of this study is to propose an integrated AI-based supervision system, named AISAFETY, designed to enhance operator safety in production processes through continuous monitoring and intelligent decision support. The proposed system is based on a layered architecture that integrates Radio-Frequency Identification for real-time operator localization, Computer Vision for visual monitoring, and a machine control layer governed by a rule-based expert system for managing safety–critical scenarios. The combination of heterogeneous sensing technologies provides redundancy and robustness in detecting hazardous conditions, such as unauthorized operator presence, tampering with safety guards, and improper collaborative robot operation. To support future validation, a prototype system has been implemented on an experimental setup consisting of a multimodal CNC lathe and a collaborative robot. A set of representative use cases has been defined to model potential hazardous situations and to guide the planned experimental evaluation of the system's safety supervision capabilities. The proposed approach highlights the potential of AI- and IoT-based supervision systems to support the implementation of upcoming regulatory frameworks and to advance human-centred safety management in collaborative industrial environments. [ABSTRACT FROM AUTHOR]
ISSN:09257535
DOI:10.1016/j.ssci.2026.107201