  Wearable Cognitive Assistance (WCA) applications present a challenge to
benchmark and characterize due to their human-in-the-loop nature. Employing
user testing to optimize system parameters is generally not feasible, given the
scope of the problem and the number of observations needed to detect small but
important effects in controlled experiments. Considering the intended
mass-scale deployment of WCA applications in the future, there exists a need
for tools enabling human-independent benchmarking.
  We present in this paper the first model for the complete end-to-end
emulation of humans in WCA. We build this model through statistical analysis of
data collected from previous work in this field, and demonstrate its utility by
studying application task durations. Compared to first-order approximations,
our model shows a ~36% larger gap between step execution times at high system
impairment versus low. We further introduce a novel framework for stochastic
optimization of resource consumption-responsiveness tradeoffs in WCA, and show
that by combining this framework with our realistic model of human behavior,
significant reductions of up to 50% in number processed frame samples and 20%
in energy consumption can be achieved with respect to the state-of-the-art.
