  There are currently limited guidelines on designing user interfaces (UI) for
immersive augmented reality (AR) applications. Designers must reflect on their
experience designing UI for desktop and mobile applications and conjecture how
a UI will influence AR users' performance. In this work, we introduce a
predictive model for determining users' performance for a target UI without the
subsequent involvement of participants in user studies. The model is trained on
participants' responses to objective performance measures such as consumed
endurance (CE) and pointing time (PT) using hierarchical drop-down menus. Large
variability in the depth and context of the menus is ensured by randomly and
dynamically creating the hierarchical drop-down menus and associated user tasks
from words contained in the lexical database WordNet. Subjective performance
bias is reduced by incorporating the users' non-verbal standard performance
WAIS-IV during the model training. The semantic information of the menu is
encoded using the Universal Sentence Encoder. We present the results of a user
study that demonstrates that the proposed predictive model achieves high
accuracy in predicting the CE on hierarchical menus of users with various
cognitive abilities. To the best of our knowledge, this is the first work on
predicting CE in designing UI for immersive AR applications.
