Designing meaningful interaction with mental workload data

Master thesis (2024)

Authors

Y. Chen Industrial Design Engineering

Contributors

C. Schneegass Human Information Communication Design - IDE (supervisor 1)
Tilman Dingler Human-Centred Artificial Intelligence - IDE (supervisor 2)
Faculty
Industrial Design Engineering
Copyright: © 2024 Yuwei Chen
Personal informatics Mental workload Neurotechnology User research Self-tracking
More Info
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Published Date

19-03-2024

Language

English

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Faculty

Industrial Design Engineering

Abstract

Our understanding of mental workload (MWL) is still limited compared to well-known physiological data like blood pressure and heart rate. MWL measures the cognitive resources required for tasks against what's available. Innovative technologies offer more comprehensive and objective cognitive data, but their impact on individuals and their tracking needs requires exploration, including how to establish continuous self-tracking behavior.

Therefore, this research aims to understand people's perceptions and thoughts on MWL and explore meaningful MWL self-tracking behavior. It follows three phases: exploratory, validation, and reflection.

In the exploratory phase, literature is reviewed to define MWL and promote self-tracking. Interviews explore users' understanding, motivations, and needs, highlighting issues like data interpretation bias, mistrust, lack of visibility, connection, and use timing.

Validation involves co-design activities, including user tests and surveys. Before user tests, the literature is reviewed for potential solutions. A rapid prototype is designed based on insights and existing metrics from EMOTIV, facilitating discussions with co-designers. Offline surveys track users' workload and stress levels to understand how they record it in their way and their perceptions and confusion between workload and stress.

Reflection combines literature review and validation insights to discuss research findings and propose future design recommendations.

Overall, this research found several barriers and negative attitudes among users toward self-tracking MWL. The main issues include difficulties and misconceptions in understanding MWL, as well as the inability to see the impact of tracking cognitive data. These challenges make it difficult for users to trust MWL data and incorporate MWL tracking into their daily lives. Additionally, this research identifies unique user perceptions of cognitive data compared to physiological metrics like blood pressure and heart rate, informing future design considerations. Ultimately, the research concludes all the insights from the literature review and research to propose several avenues for future design and research.