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Tuesday, January 24 • 4:30pm - 6:00pm
Poster: Measuring User Cognitive Engagement in the Wild Via Camera

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Accurate measuring of cognitive engagement helps users better manage their cognitive resources when performing tasks. In the past, physiological features such as pupillary response and body movements have been widely used by researchers to characterize user cognitive engagement levels. However, existing feature-based solutions are either overly dependent on intrusive devices or only robust to highly controlled lab settings. In this work, we present Engagementometer, a low-cost cognitive engagement prediction framework that is built upon user-contributed video data in the wild. Engagementometer leverages gaming videos recorded by off-the-shelf webcams as data input, and is capable of extracting user physiological features such as blink rate and head motion from those videos full of motion artifacts. Engagementometer then maps extracted features to user engagement levels and produces regression models for engagement prediction. To validate our approach, we first conduct EEG-based benchmarking experiments to demonstrate that the engagement prediction model developed by blink rate and head motion can be generalized across multiple users. After that, we carry out extensive trials using user contributed data in the wild to verify the overall performance of our prediction model.

Tuesday January 24, 2017 4:30pm - 6:00pm
BioScience Research Collaborative Event Hall 6500 Main Street, Houston, TX 77030-1402

Attendees (1)