Zachary Spielman

Application of Eye Tracking for Measurement and Evaluation in Human Factors Studies in Control Room Modernization

Abstract An important element of human factors engineering (HFE) pertains to measurement and evaluation (M&E). The role of HFE-M&E should be integrated throughout the entire control room modernization (CRM) process and be used for human–system performance evaluation and diagnostic purposes to resolve potential human engineering deficiencies and other human–machine interface design issues. NUREG-0711 describes how HFE in CRM should employ a hierarchical set of measures, particularly during integrated system validation, including plant performance, personnel task performance, situation awareness, cognitive workload, and anthropometric/physiological factors. Historically, subjective measures have been primarily used since they are easier to collect and do not require specialized equipment. However, there are pitfalls with relying solely on subjective measures in M&E such as negatively impacting reliability, sensitivity, and objectivity. As part of comprehensively capturing a diverse set of measures that strengthen findings and inferences made about the benefits from emerging technologies like advanced displays, this paper discusses the value of using eye tracking as an objective method that can be used in M&E. A brief description of eye tracking technology and relevant eye tracking measures is provided. Additionally, technical considerations and the unique challenges with using eye tracking in full-scale simulations are addressed. Finally, this paper shares preliminary findings regarding the use of a wearable eye tracking system in a full–scale simulator study. These findings should help guide future full–scale simulator studies using eye tracking as a methodology to evaluate human-system performance.

Quantifying the Contribution of Individual Display Features on Fixation Duration to Support Human-System Interface Design in Nuclear Power Plants

The integration of new digital instrumentation and control (I&C) technologies like advanced human-system interfaces in U.S. nuclear power plant main control rooms is important for addressing long-term aging and obsolescence of existing I&C. Nonetheless, attention should be made to ensure these technologies reflect state-of-the-art human factors engineering (HFE) principles. Often, there is conflicting guidance from one guideline to another, requiring the analyst to make a judgment call on addressing these ‘tradeoffs.’ The objective of this research was to inform the analyst of these tradeoffs through an empirical investigation of how certain display features that characterize common HFE guidelines concerning visual clutter and saliency influence information processing in a naturalistic context. By understanding the unique contribution of each display feature using a multilevel model, the HFE analyst should have an understanding of the interrelations of each feature with its impact on cognitive processes. Results and implications are discussed in this paper.

Full Scale Evaluation of How Task-Based Overview Displays Impact Operator Workload and Situation Awareness When in Emergency Procedure Space

Control room modernization is critical to extending the life of the 99 operating commercial nuclear power plants (NPP) within the United States. However, due to the lack of evidence demonstrating the efficiency and effectiveness of recent candidate technologies, current NPP control rooms operate without the benefit of various newer technologies now available. As nuclear power plants begin to extend their licenses to continue operating for another 20 years, there is increased interest in modernizing the control room and supplementing the existing control boards with advanced technologies. As part of a series of studies investigating the benefits of advanced control room technologies, the researchers conducted an experimental study to observe the effect of Task-Based Overview Displays (TODs) on operator workload and situation awareness (SA) while completing typical operating scenarios. Researchers employed the Situation Awareness Rating Technique (SART) and the NASA Task Load Index (TLX) as construct measures.

Machine Learning and Big Data Analytics in Support of Fleet Safety During Severe Weather

The US DoT estimates 22% of the 5.7 million vehicle crashes a year are weather related. At Idaho National Laboratories, home of the DOE’s largest transit, heavy and light vehicle fleet in the nation, weather is a constant challenge for the 4000 employees traveling the 45 to 65 mile stretch of road. Driving conditions can vary immensely; micro-climate conditions at INL site locations highways go unmonitored and causing severe challenges. INL has taken the initiative to review applicable technologies determining that addressing severe weather and road conditions through the application of advanced modeling methods holds promise for enhancing driver safety and dispatch planning. INL engaged IBM Global Business Services Advanced Analytics Center of Competency (CoC) Team for support in this effort. This presentation reviews the benefits expected, data surveyed, and how to use integrated sources and cognitive analytics to improve real-time weather forecasting and INL site fleet and operations planning.