Implementing an AI-Driven Gesture Recognition System in MES for Enhanced Efficiency and Human-Centric Operations in Industry 5.0

The scope of this study is the development methodology of a Gesture Recognition System (GRS), making use of Artificial Intelligence (AI), and integrated into the Manufacturing Execution System (MES) provided in i-FAB, a learning factory in Università Carlo Cattaneo - LIUC, aiming at improving time tracking and displacement of unnecessary movement on the shop floor. Concerning the Human-Centricity pillar of the Industry 5.0 paradigm, this approach aims at enhancing well-being through the reduction of repetitive and inefficient tasks hence, making the MES systems more user-centric. The developed GRS can recognise certain hand movements related to various inputs to the MES so that users spend less time using a keyboard and touching devices. This leads to a reduction of time loss due to the time associated with tracking time and activity data which in the end optimizes production. Moreover, the system also lessens ergonomic risks that pertain to the tasks being performed, since unnecessary and repetitive movements of the operators are greatly reduced. The research findings indicate that this considerably improves the pace of completion of time and activity tracking on MES systems in a way that is designed to meet the requirements of Industry 5.0 which is focused on promoting a collaborative, safe and healthy environment.