Smart retrofitting for human factors: a face recognition-based system proposal

Abstract

Industry nowadays must deal with the so called “fourth industrial revolution”, i.e. Industry 4.0. This revolution is based on the introduction of new paradigms in the manufacturing industry such as flexibility, efficiency, safety, digitization, big data analysis and interconnection. However, human factors’ integration is usually not considered, although included as one of the paradigms. Some of these human factors’ most overlooked aspects are the customization of the worker’s user experience and on-board safety. Moreover, the issue of integrating state of the art technologies on legacy machines is also of utmost importance, as it can make a considerable difference on the economic and environmental aspects of their management, by extending the machine’s life cycle. In response to this issue, the Retrofitting paradigm, the addition of new technologies to legacy machines, has been considered. In this paper we propose a novel modular system architecture for secure authentication and worker’s log-in/log-out traceability based on face recognition and on state-of-the-art Deep Learning and Computer Vision techniques, as Convolutional Neural Networks. Starting from the proposed architecture, we developed and tested a device designed to retrofit legacy machines with such capabilities, keeping particular attention to the interface usability in the design phase, little considered in retrofitting applications along with other Human Factors, despite being one of the pillars of Industry 4.0. This research work’s results showed a dramatic improvement regarding machines on-board access safety.

Smart Retrofit: An Innovative and Sustainable Solution

“…[10] Implementation of internet connections for remote access and connection to the cloud system Integration of hardware devices for connectivity [49] Augmented Development of an augmented reality module Integration of connected glasses for augmented reality [43] Control and monitoring program Integration of Raspberry Pi [44] Remote control system with OPC UA server Sensor and Raspberry Pi [4] Human-machine interface, numerical control kernel software, PLC software Integration of machine control unit [52] Mixed-reality software Mixed-reality visor [9] Convolutional network based on Visual Geometry Group Integration of Raspberry Pi [16] Deep learning for face detection [38] Data monitoring software Integration of smart sensors [13] Digital Twin, machine learning algorithms and user interface development Smart sensor and tablet integration Of course, to develop the software side, it is necessary to obtain information from the plants, machinery and systems. Thus, it is still a requirement to retrofit at the hardware level.…”

“…This makes it possible to visualise part defects, stop production and report anomalies to the operator, who can take corrective action based on the information reported on the user dashboard. An article by Generosi et al [16] also discusses a retrofit action for a worker identification system to prevent workplace accidents and improve worker conditions. The hardware retrofit uses Raspberry Pi 4, a touchscreen display, an embedded webcam and LEDs.…”

“…In addition, smart retrofitting action enables benefits related to sustainability. The sustainability brought by retrofit actions can be defined in terms of environmental sustainability related to the reuse of machinery, resource optimisation and reduction in waste [13]; economic sustainability in terms of the decrease in costs, waste and time [14] and increase in productivity [11,12]; and social sustainability, ensuring greater safety and integration of the operator in the workplace [15,16], helping him make decisions and value his work [17].…”

Facial Recognition Technology-Based Attendance Management System Application in Smart Classroom

“…This paper utilizes an attendance recording system based on facial recognition. Face recognition is the ability of a computer to identify a person using AI [4]. Due to the widespread availability of cameras, facial recognition can now be performed at the lowest possible cost.…”