Data-Driven Industrial Human-Machine Interface Temporal Adaptation for Process Optimization

Abstract

The application of Artificial Intelligence (AI) into Industrial Human-Machine Interfaces (HMIs) moved old systems with physical buttons and analogue actuators into adaptive interaction models and context-based self adjusted interfaces. To date, little attention has been paid to industrial Human-Machine Interfaces (HMI) which play a vital role in the communication between operator and complex productive systems. Current industrial HMIs do not take into account operator behaviour, but rather focus on the production process. To enhance User Experience (UX) and improve performance it is necessary to adapt the interface to the needs of the operator. This paper proposes a Machine Learning (ML) based operator interaction Data-Driven methodology to extract a set of interface adaptation rules. The methodology optimizes the interaction by reducing the number of actions and hence the amount of time and possible errors in repetitive monitoring and control tasks. An experiment with real operators was conducted to validate the proposed approach. The system was able to extract their interaction patterns and propose temporal interface adaptations, leading to a personalized, adaptive and more effective interaction.