Empowering Manual Assembly: Dialog System for Enhanced Customization and Efficiency of Cognitive Assistance Systems

Cognitive assistance systems (CAS) used in manual assembly aim to improve worker’s productivity and accuracy by providing guidance and process control. Various CAS in manual assembly can be used simultaneously through integration into the existing IT-infrastructure. The integration of CAS is often enabled by custom software solutions that enable seamless synchronization between CAS. To increase productivity and accuracy, continuous reconfiguration of CAS is required to align it with specific tasks and individual preferences. This can be achieved by directly obtaining workers’ opinions and thus allowing workers to decide what CAS are necessary to optimize individual performance and efficiency. This paper presents a dialog system based on Natural Language Processing (NLP) that leverages voice and text inputs to improve the reconfiguration of CAS.