Authors:
Bilal Maqbool
;
Laoa Jalal
and
Sebastian Herold
Affiliation:
Department of Mathematics and Computer Science, Faculty of Health, Science and Technology, Karlstad University, Karlstad, Sweden
Keyword(s):
Time Series Data, Generative Adversarial Networks (GAN), Synthetic Data Generation, Usability Evaluation, Machine Learning (ML), Digital Healthcare (DH).
Abstract:
Effective usability evaluation of user interface (UI) designs is essential. Particularly in digital healthcare, frequently involving relevant user groups in usability evaluations is not always possible or is ethically questionable. On the other hand, neglecting the perspectives of such groups can lead to UI designs that fail to be inclusive and adaptable. In this paper, we outline an initial idea to utilize artificial intelligence methods to simulate mobile user interface interactions of such user groups. The goal is to support software developers and designers with tools that show them how users of certain user groups might interact with a user interface under development and show potential issues before actual, more expensive usability evaluations are conducted. We present a study that employs synthetic representations of user interactions with UI elements based on a small sample of real interactions. This synthetic data was then used to train a classification model predicting whet
her real user interactions were from younger or elderly persons. The good performance of this model provides evidence that synthetic user interface interactions might be accurate enough to feed into imitation learning approaches, which, in turn, could be the foundation for the desired tool support.
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