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Creators/Authors contains: "Abdullah, Saeed"

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  1. ; ; ; ; ; ; ; ; ; ; et al ( , The 1st ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities)
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  2. ; ; ; ( , MobileHCI '18 Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services)
    Alertness is a crucial component of our cognitive performance. Reduced alertness can negatively impact memory consolidation, productivity and safety. As a result, there has been an increasing focus on continuous assessment of alertness. The existing methods usually require users to wear sensors, fill out questionnaires, or perform response time tests periodically, in order to track their alertness. These methods may be obtrusvie to some users, and thus have limited capability. In this work, we propose AlertnessScanner, a computer-vision-based system that collects in-situ pupil information to model alertness in the wild. We conducted two in-the-wild studies to evaluate the effectiveness of our solution, and found that AlertnessScanner passively and unobtrusively assess alertness. We discuss the implications of our findings and present opportunities for mobile applications that measure and act upon changes in alertness.
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