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Browsing by Author "LeBlanc, Katya"

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    Design of Gaze-Based Alarm Acknowledgement by Parameter Characteristics
    Herdt, Katherine; Lau, Nathan; Hilderbrant, Michael; Le, Tai; LeBlanc, Katya (Springer, 2022-06-16)
    Alarms in industrial control rooms are defined by their ability to alert an operator of abnormal events that require prompt response. However, when vigilant, operators may anticipate upcoming alarms, rendering those alarms less informative if not a nuisance. Three gaze-based alarm acknowledgement methods were designed by estimating operator awareness based on their eye fixations on the parameter/area of interest and parameter behavior shortly before the alarm. The three designs differed in acknowledging the types of parameter behaviors, which could be: a) near the alarm threshold, b) fluctuating drastically, or c) trending towards an alarm threshold. These three parameter behaviors correlate with increased visual sampling, which suggests higher operator awareness or expectation of alarms. In a simulator study comparing the three gaze-based acknowledgement methods against no gaze acknowledgement, 24 participants completed 24 trials of alarm monitoring task while maintaining a single parameter within a predefined range. Analysis of variance revealed that usability ratings were higher for conditions with than without gaze acknowledgements, demonstrating promise for this alarm management approach.
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    Using Eye-tracking to Acknowledge Attended Alarms
    Herdt, Katherine Elizabeth (Virginia Tech, 2022-01-21)
    A lack of alarm management for industrial control rooms has led to frequent alarm floods that have the potential to overwhelm operators within minutes. One approach to managing alarm floods would be altering the salience of alarms that operators might already notice, thereby reducing the disruption on workflow and attention for managing uninformative alarms. This research investigated the central hypothesis that eye fixations could supply passive input to acknowledge alarms anticipated by the operators and thereby improve their overall task performance. A dual-task experiment recruiting 24 participants was conducted to compare three gaze-based alarm acknowledgement methods –Proximity, Prediction, and Entropy- against no acknowledgement across three types of scenarios – Near-threshold, Trending, and Fluctuation. The gaze-based acknowledgement methods reduced visual and auditory salience of alarms as a function of the number of fixations on parameters as well as characteristics of the parameter known to influence operator monitoring behaviors. The participants performed an alarm monitoring task while controlling a continuous parameter within an acceptable range. While participants showed a preference for all of three gaze-based acknowledgment methods, performance of the parameter control task did not improve with gaze-based acknowledgement. Scenario types, as defined by the behavior of the parameters, exhibited a significant effect on the performance of the parameter control task, suggesting a greater influence on participant attention than the reduced salience associated with the gaze-based acknowledgments. Additional analysis revealed that gaze-acknowledgements are higher in scenarios with the most suitable for the gaze-based acknowledgement methods, although the participants did not show any gaze-based acknowledgements and did not make a prediction of an alarm for a significant portion of the trials, suggesting a lack of resource allocation to the alarm monitoring task. This result suggests that the effectiveness of gaze-based acknowledgement may depend on the combination of on-going tasks. Taken together, the experimental results showed some utility of user gaze in managing alarms given how acknowledgement occurred more often when the acknowledgement methods and parameters matched; however, further design research is necessary to translate the utility into clear performance or productivity benefits.