TY - JOUR A1 - Schmitt, Jan A1 - Engelmann, Bastian A1 - Manghisi, Vito Modesto A1 - Wilhelm, Markus A1 - Uva, Antonello A1 - Fiorentino, Michele T1 - Towards gestured-based technologies for human-centred smart factories T2 - International Journal of Computer Integrated Manufacturing N2 - Despite the increasing degree of automation in industry, manual or semi-automated are commonly and inevitable for complex assembly tasks. The transformation to smart processes in manufacturing leads to a higher deployment of data-driven approaches to support the worker. Upcoming technologies in this context are oftentimes based on the gesture-recognition, − monitoring or – control. This contribution systematically reviews gesture or motion capturing technologies and the utilization of gesture data in the ergonomic assessment, gesture-based robot control strategies as well as the identification of COVID-19 symptoms. Subsequently, two applications are presented in detail. First, a holistic human-centric optimization method for line-balancing using a novel indicator – ErgoTakt – derived by motion capturing. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and the takt-time balancing. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-score and the cycle time of each assembly workstation with respect to the workers’ ability. The second application is gesture-based robot-control. A cloud-based approach utilizing a generally accessible hand-tracking model embedded in a low-code IoT programming environment is shown. KW - gesture-based monitoring KW - gesture-based control KW - manufacturing Y1 - 2023 UR - https://opus4.kobv.de/opus4-fhws/frontdoor/index/index/docId/2231 UR - https://doi.org/10.1080/0951192X.2022.2121424 SN - 1362-3052 VL - 36 IS - 1 SP - 110 EP - 127 ER -