[en] This paper introduces a novel augmentation to the current heuristic usability evaluation methodology. The SPAR-H human reliability analysis method was developed for categorizing human performance in nuclear power plants. Despite the specialized use of SPAR-H for safety critical scenarios, the method also holds promise for use in commercial off-the-shelf software usability evaluations. The SPAR-H method shares task analysis underpinnings with human-computer interaction, and it can be easily adapted to incorporate usability heuristics as performance shaping factors. By assigning probabilistic modifiers to heuristics, it is possible to arrive at the usability error probability (UEP). This UEP is not a literal probability of error but nonetheless provides a quantitative basis to heuristic evaluation. When combined with a consequence matrix for usability errors, this method affords ready prioritization of usability issues

[en] Preparedness for chemical, biological, and radiological/nuclear incidents at nuclear power plants (NPPs) includes the deployment of well trained emergency response teams. While teams are expected to do well, data from other domains suggests that the timeliness and accuracy associated with incident response can be improved through collaborative human-robotic interaction. Many incident response scenarios call for multiple, complex procedure-based activities performed by personnel wearing cumbersome personal protective equipment (PPE) and operating under high levels of stress and workload. While robotic assistance is postulated to reduce workload and exposure, limitations associated with communications and the robot's ability to act independently have served to limit reliability and reduce our potential to exploit human -robotic interaction and efficacy of response. Recent work at the Idaho National Laboratory INL on expanding robot capability has the potential to improve human-system response during disaster management and recovery. Specifically, increasing the range of higher level robot behaviors such as autonomous navigation and mapping, evolving new abstractions for sensor and control data, and developing metaphors for operator control have the potential to improve state-of-the-art in incident response. This paper discusses these issues and reports on experiments underway intelligence residing on the robot to enhance emergency response

[en] This paper describes a cognitively based human reliability analysis (HRA) quantification technique for estimating the human error probabilities (HEPs) associated with operator and crew actions at nuclear power plants. The method described here, Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method, was developed to aid in characterizing and quantifying human performance at nuclear power plants. The intent was to develop a defensible method that would consider all factors that may influence performance. In the SPAR-H approach, calculation of HEP rates is especially straightforward, starting with pre-defined nominal error rates for cognitive vs. action-oriented tasks, and incorporating performance shaping factor multipliers upon those nominal error rates

[en] An ongoing issue within human-computer interaction (HCI) is the need for simplified or ''discount'' methods. The current economic slowdown has necessitated innovative methods that are results driven and cost effective. The myriad methods of design and usability are currently being cost-justified, and new techniques are actively being explored that meet current budgets and needs. Recent efforts in human reliability analysis (HRA) are highlighted by the ten-year development of the Standardized Plant Analysis Risk HRA (SPAR-H) method. The SPAR-H method has been used primarily for determining human centered risk at nuclear power plants. The SPAR-H method, however, shares task analysis underpinnings with HCI. Despite this methodological overlap, there is currently no HRA approach deployed in heuristic usability evaluation. This paper presents an extension of the existing SPAR-H method to be used as part of heuristic usability evaluation in HCI

[en] This paper reports on efforts being sponsored by the U.S. NRC and performed by INEEL to develop a technical basis and perform work to extract information from sources for use in HRA. The objectives of this work are to: (1) develop a method for conducting risk-informed event analysis of human performance information that stems from operating experience at nuclear power plants and for compiling and documenting the results in a structured manner; (2) provide information from these analyses for use in risk-informed and performance-based regulatory activities; (3) create methods for information extraction and a repository for this information that, likewise, support HRA methods and their applications

[en] This paper summarizes an emerging project regarding the utilization of high-fidelity MIDAS simulations for visualizing and modeling control room crew performance at nuclear power plants. The key envisioned uses for MIDAS-based control room simulations are: (1) the estimation of human error associated with advanced control room equipment and configurations, (2) the investigative determination of contributory cognitive factors for risk significant scenarios involving control room operating crews, and (3) the certification of reduced staffing levels in advanced control rooms. It is proposed that MIDAS serves as a key component for the effective modeling of cognition, elements of situation awareness, and risk associated with human performance in next generation control rooms

[en] 3D manikins are often used in visualizations to model human activity in complex settings. Manikins assist in developing understanding of human actions, movements and routines in a variety of different environments representing new conceptual designs. One such environment is a nuclear power plant control room, here they have the potential to be used to simulate more precise ergonomic assessments of human work stations. Next generation control rooms will pose numerous challenges for system designers. The manikin modeling approach by itself, however, may be insufficient for dealing with the desired technical advancements and challenges of next generation automated systems. Uncertainty regarding effective staffing levels; and the potential for negative human performance consequences in the presence of advanced automated systems (e.g., reduced vigilance, poor situation awareness, mistrust or blind faith in automation, higher information load and increased complexity) call for further research. Baseline assessment of novel control room equipment(s) and configurations needs to be conducted. These design uncertainties can be reduced through complementary analysis that merges ergonomic manikin models with models of higher cognitive functions, such as attention, memory, decision-making, and problem-solving. This paper will discuss recent advancements in merging a theoretical-driven cognitive modeling framework within a 3D visualization modeling tool to evaluate of next generation control room human factors and ergonomic assessment. Though this discussion primary focuses on control room design, the application for such a merger between 3D visualization and cognitive modeling can be extended to various areas of focus such as training and scenario planning

[en] There is a general consensus that robots could be beneficial in performing tasks within hazardous radiological environments. Most control of robots in hazardous environments involves master-slave or teleoperation relationships between the human and the robot. While teleoperation-based solutions keep humans out of harms way, they also change the training requirements to accomplish a task. In this paper we present a research methodology that allowed scientists at Idaho National Laboratory to identify, develop, and prove a semi-autonomous robot solution for search and characterization tasks within a hazardous environment. Two experiments are summarized that validated the use of semi-autonomy and show that robot autonomy can help mitigate some of the performance differences between operators who have different levels of robot experience, and can improve performance over teleoperated systems