A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/2010.07703v2.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
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In today's society, our cognition is constantly influenced by information intake, attention switching, and task interruptions. This increases the difficulty of a given task, adding to the existing workload and leading to compromised cognitive performances. The human body expresses the use of cognitive resources through physiological responses when confronted with a plethora of cognitive workload. This temporarily mobilizes additional resources to deal with the workload at the cost of<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.07703v2">arXiv:2010.07703v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/v3cfq4cs2jhuzlym3za2r4b3le">fatcat:v3cfq4cs2jhuzlym3za2r4b3le</a> </span>
more »... mental exhaustion. We predict that recent developments in physiological sensing will increasingly create user interfaces that are aware of the user's cognitive capacities, hence able to intervene when high or low states of cognitive workload are detected. Subsequently, we investigate suitable feedback modalities in a user-centric design process which are desirable for cognitive assistance. We then investigate different physiological sensing modalities to enable suitable real-time assessments of cognitive workload. We provide evidence that the human brain and eye gaze are sensitive to fluctuations in cognitive resting states. We show that electroencephalography and eye tracking are reliable modalities to assess mental workload during user interface operation. In the end, we present applications that regulate cognitive workload in home and work setting, investigate how cognitive workload can be visualized to the user, and show how cognitive workload measurements can be used to predict the efficiency of information intake through reading interfaces. Finally, we present our vision of future workload-aware interfaces. Previous interfaces were limited in their ability to utilize cognitive workload for user interaction. Together with the collected data sets, this thesis paves the way for methodical and technical tools that integrate workload-awareness as a factor for context-aware systems.
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