Classifying Workload with Eye Movements in a Complex Task

Abstract

Eye movements and pupil size have been used to assess workload in previous research. However, the results presented in the literature vary, and the tasks have been too simple at times or the experimental conditions (e.g. lighting) too tightly controlled to determine if the use of eye data to assess workload is useful in real-world contexts. This research investigates the use of ten eye movement, eyelid, or pupil related metrics as input to support vector machines for classifying workload in a complex task. The results indicate that both pupil size and percentage of eye closure are useful for predicting workload. Further, the combination of the two metrics increases the robustness and accuracy of the workload predictions.