Smart factories are fostered by integrating intelligent systems and ICT technologies. The role they play is crucial in the spread of Industry 4.0 and the economic growth of developed countries. Smart factories can be empowered by using several sensors aimed at making them more and more 'smart.' Unfortunately, work accidents are still very common resulting in human losses and permanent injuries. This makes it urgent and key to implementing security and safety measures also in the context of a smart factory. In this article, a novel framework for supporting smart devices in a smart factory, using multiple sensors to monitor different biometric features, both physical and behavioral is proposed. Thanks to the fusion of several biometric traits with the support of machine learning technologies working together with different kinds of sensors, it is possible to guarantee three fundamental aspects within the interaction between an operator of a smart device and the device itself: continuous authentication (i.e., continuous face recognition), drowsiness detection, and liveness detection. With the application of the proposed framework, it is possible to significantly improve the safety of operators avoiding fatal accidents for them. Experiments made using COTS-hardware showed that the authors' idea is easy to implement in a large-scale smart factory and further improves the spread of Industry 4.0.
On the Impact of Multimodal and Multisensor Biometrics in Smart Factories
Abate A. F.;Cimmino L.;
2022-01-01
Abstract
Smart factories are fostered by integrating intelligent systems and ICT technologies. The role they play is crucial in the spread of Industry 4.0 and the economic growth of developed countries. Smart factories can be empowered by using several sensors aimed at making them more and more 'smart.' Unfortunately, work accidents are still very common resulting in human losses and permanent injuries. This makes it urgent and key to implementing security and safety measures also in the context of a smart factory. In this article, a novel framework for supporting smart devices in a smart factory, using multiple sensors to monitor different biometric features, both physical and behavioral is proposed. Thanks to the fusion of several biometric traits with the support of machine learning technologies working together with different kinds of sensors, it is possible to guarantee three fundamental aspects within the interaction between an operator of a smart device and the device itself: continuous authentication (i.e., continuous face recognition), drowsiness detection, and liveness detection. With the application of the proposed framework, it is possible to significantly improve the safety of operators avoiding fatal accidents for them. Experiments made using COTS-hardware showed that the authors' idea is easy to implement in a large-scale smart factory and further improves the spread of Industry 4.0.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
