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- W66671366 abstract "Maria E. Jabon, Sun Joo Ahn, Jeremy N. Bailenson Every day countless human errors occur around the globe. While many of these errors are harmless, just a few disastrous errors, such as Bhopal, Chernobyl, or Three Mile Island, convince us that developing ways to improve human performance is not only desirable but crucial. Considerable research exists in the area of Human Error Identification (HEI), a field devoted to the development of systems to predict human errors. However, these systems typically predict only instantaneous errors, not the many other possible factors involved in human performance such as speed or productivity. Furthermore, they often rely upon pre-defined hierarchies of errors and minute-by-minute analyses of users by trained analysts, making them costly and timeconsuming to implement. The current work proposes a novel bottom-up approach to human performance prediction using facial features automatically extracted from short video segments of participant faces during laboratory experiments. This bottom-up approach maximizes data usage and gives us the power to create performance models on three different layers: instantaneous errors (individual errors occurring at any time during the task), phase level performance (speed and accuracy on a single iteration of the task) and task level performance (speed, accuracy, and productivity over the entire task). This enhancement takes our model beyond the capabilities of current HEI techniques, which focus solely on predicting instantaneous errors, and expands its practicality in implementation. Using computer vision also eliminates substantial costs associated with standard HEI techniques. Instead of devoting the time and resources to train and employ analysts, we use" @default.
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- W66671366 date "2009-01-01" @default.
- W66671366 modified "2023-09-23" @default.
- W66671366 title "Predicting Performance on a Repetitive Task through Automatic Analysis of Facial Feature Movements" @default.
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