Enhancing Human Machine Interface design using cognitive metrics of process operators

In a typical process industry, Human Machine Interfaces (HMIs) are essential for all aspects of communication and interaction between operators and processes, vital for process safety, quality, and efficiency. Good interface design enables operators to accomplish their duties efficiently and effectively with minimal errors. Consequently, it is crucial to design HMIs in a way that facilitates collaboration between automation and operators. Current HMI design practices have a lot of subjectivity and often ignore operator's cognitive behavior. In this work, we propose a quantitative approach that uses cognitive metrics – association and salience metrics – obtained using eye-tracking to improve HMI design for process operators. The association metric helps in identifying information sources which are often used together while salience metric informs about information sources which are extensively used from the HMI. Based on insights from these cognitive metrics, we designed a new HMI. To evaluate the relative efficacy of each HMI, human subject studies were conducted. The results indicate that the new HMI developed with the use of cognitive metrics improved the operators' efficiency. Thus, the proposed approach can help enhance usability of industrial HMIs.